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| 2. |
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| 3. |
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| 4. |
- Bakidou, Anna, 1996, et al.
(författare)
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On Scene Injury Severity Prediction (OSISP) model for trauma developed using the Swedish Trauma Registry
- 2023
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Ingår i: BMC Medical Informatics and Decision Making. - 1472-6947. ; 23:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Providing optimal care for trauma, the leading cause of death for young adults, remains a challenge e.g., due to field triage limitations in assessing a patient’s condition and deciding on transport destination. Data-driven On Scene Injury Severity Prediction (OSISP) models for motor vehicle crashes have shown potential for providing real-time decision support. The objective of this study is therefore to evaluate if an Artificial Intelligence (AI) based clinical decision support system can identify severely injured trauma patients in the prehospital setting. Methods: The Swedish Trauma Registry was used to train and validate five models – Logistic Regression, Random Forest, XGBoost, Support Vector Machine and Artificial Neural Network – in a stratified 10-fold cross validation setting and hold-out analysis. The models performed binary classification of the New Injury Severity Score and were evaluated using accuracy metrics, area under the receiver operating characteristic curve (AUC) and Precision-Recall curve (AUCPR), and under- and overtriage rates. Results: There were 75,602 registrations between 2013–2020 and 47,357 (62.6%) remained after eligibility criteria were applied. Models were based on 21 predictors, including injury location. From the clinical outcome, about 40% of patientswere undertriaged and 46% were overtriaged. Models demonstrated potential for improved triaging and yielded AUC between 0.80–0.89 and AUCPR between 0.43–0.62. Conclusions: AI based OSISP models have potential to provide support during assessment of injury severity. The findings may be used for developing tools to complement field triage protocols, with potential to improve prehospital trauma care and thereby reduce morbidity and mortality for a large patient population.
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| 5. |
- Buendia, Ruben, 1982, et al.
(författare)
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Assessing mechanisms of injury as predictors of severe injury for adult car and truck occupants
- 2015
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Ingår i: The 24th International Technical Conference on the Enhanced Safety of Vehicles (ESV).
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This study evaluates Mechanisms of Injury (MOI) that can be rapidly assessed at the scene of accident and may be used as predictors of severe injury for traffic accidents involving occupants in cars or trucks. The objective is to increase the knowledge of how MOI can be used to differentiate whether a patient is severely injured or not. This knowledge can be used to improve trauma triage systems. Furthermore, an objective is to analyze safety differences between cars and light/heavy trucks. The scope is adult occupants of cars, light and heavy trucks injured in accidents registered in the Swedish Traffic Accident Data Acquisition (STRADA) database from 2003 to 2013. Partition between severe and non-severe injury was done according to the Injury Severity Score (ISS) with ISS > 15 as definition of severe injury. The MOIs considered were: belt use, airbag deployment, posted speed limit, elderly occupant (age ≥ 55 years), sex, type of accident (single, intersection, turning, head-on, overtaking, rear end, tram/train, wild animal or other) and location of the accident (urban or rural). The different MOI were evaluated individually using univariate chi-square tests and together using multivariate logistic regression models. Results show that belt use is the most crucial factor determining risk of severe injury for all vehicle types. Age is the second most important factor, with elderly occupants exhibiting a higher risk. Head-on accidents are the most dangerous for cars and light trucks while single accidents are the most dangerous for heavy trucks. Belt use compliance is much lower for truck occupants. This appears to be the main reason for the frequency of severe injury being higher for truck occupants than for car occupants.
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| 6. |
- Buendia, Ruben, 1982, et al.
(författare)
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Bioimpedance technology for detection of thoracic injury
- 2017
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Ingår i: Physiological Measurement. - : IOP Publishing. - 0967-3334 .- 1361-6579. ; 38:11, s. 2000-2014
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Thoracic trauma is one of the most common and lethal types of injury, causing over a quarter of traumatic deaths. Severe thoracic injuries are often occult and difficult to diagnose in the field. There is a need for a point-of-care diagnostic device for severe thoracic injuries in the prehospital setting. Electrical bioimpedance (EBI) is non-invasive, portable, rapid and easy to use technology that can provide objective and quantitative diagnostic information for the prehospital environment. Here, we evaluated the performance of EBI to detect thoracic injuries. Approach: In this open study, EBI resistance (R), reactance (X) and phase angle (PA) of both sides of the thorax were measured at 50 kHz on patients suffering from thoracic injuries (n = 20). In parallel, a control group consisting of healthy subjects (n = 20) was recruited. A diagnostic mathematical algorithm, fed with input parameters derived from EBI data, was designed to differentiate patients from healthy controls. Main results: Ratios between the X and PA measurements of both sides of the thorax were significantly different (p < 0.05) between healthy volunteers and patients with left-and right-sided injuries. The diagnostic algorithm achieved a performance evaluated by leave-one-out cross-validation analysis and derived area under the receiver operating characteristic curve of 0.88. Significance: A diagnostic algorithm that accurately discriminates between patients suffering thoracic injuries and healthy subjects was designed using EBI technology. A larger, prospective and blinded study is thus warranted to validate the feasibility of EBI technology as a prehospital tool.
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| 7. |
- Buendia, Ruben, 1982, et al.
(författare)
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Deriving heart rate variability indices from cardiac monitoring : An indicator of driver sleepiness
- 2019
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Ingår i: Traffic Injury Prevention. - : Taylor & Francis. - 1538-9588 .- 1538-957X. ; 20:3, s. 249-254
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Driver fatigue is considered to be a major contributor to road traffic crashes. Cardiac monitoring and heart rate variability (HRV) analysis is a candidate method for early and accurate detection of driver sleepiness. This study has 2 objectives: to evaluate the (1) suitability of different preprocessing strategies for detecting and removing outlier heartbeats and spectral transformation of HRV signals and their impact of driver sleepiness assessment and (2) relation between common HRV indices and subjective sleepiness reported by a large number of drivers in real driving situations, for the first time.Methods: The study analyzed >3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. The electrocardiograph (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The drivers reported their perceived level of sleepiness according to the Karolinska Sleepiness Scale (KSS) every 5 min. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat intervals deviating >30% from the mean of the four previous intervals and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain: (1) Fourier transform; (2) autoregressive model; and (3) Lomb-Scargle periodogram. Different preprocessing strategies were compared regarding their impact on derivation of common HRV indices and their relation to KSS data distribution, using box plots and statistical tests such as analysis of variance (ANOVA) and Student’s t test.Results: The ability of HRV indices to discriminate between alert and sleepy drivers does not differ significantly depending on which outlier detection and spectral transformation methods are used. As expected, with increasing sleepiness, the heart rate decreased, whereas heart rate variability overall increased. Furthermore, HRV parameters representing the parasympathetic branch of the autonomous nervous system increased. An unexpected finding was that parameters representing the sympathetic branch of the autonomous nervous system also increased with increasing KSS level. We hypothesize that this increment was due to stress induced by trying to avoid an incident, because the drivers were in real driving situations.Conclusions: The association of HRV indices to KSS did not depend on the preprocessing strategy. No preprocessing method showed superiority for HRV association to driver sleepiness. This was also true for combinations of methods for frequency domain HRV indices. The results prove clear relationships between HRV indices and perceived sleepiness. Thus, HRV analysis shows promise for driver sleepiness detection.
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| 8. |
- Buendia, Ruben, 1982, et al.
(författare)
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On scene injury prediction (OSISP) algorithm for car occupants
- 2015
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Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 81, s. 211-217
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Tidskriftsartikel (refereegranskat)abstract
- Many victims in traffic accidents do not receive optimal care due to the fact that the severity of their injuries is not realized early on. Triage protocols are based on physiological and anatomical criteria and subsequently on mechanisms of injury in order to reduce undertriage. In this study the value of accident characteristics for field triage is evaluated by developing an on scene injury severity prediction (OSISP) algorithm using only accident characteristics that are feasible to assess at the scene of accident. A multi-variate logistic regression model is constructed to assess the probability of a car occupant being severely injured following a crash, based on the Swedish Traffic Accident Data Acquisition (STRADA) database. Accidents involving adult occupants for calendar years 2003–2013 included in both police and hospital records, with no missing data for any of the model variables, were included. The total number of subjects was 29 128, who were involved in 22 607 accidents. Partition between severe and non-severe injury was done using the Injury Severity Score (ISS) with two thresholds: ISS > 8 and ISS > 15. The model variables are: belt use, airbag deployment, posted speed limit, type of accident, location of accident, elderly occupant (>55 years old), sex and occupant seat position. The area under the receiver operator characteristic curve (AUC) is 0.78 and 0.83 for ISS > 8 and ISS > 15, respectively, as estimated by 10-fold cross-validation. Belt use is the strongest predictor followed by type of accident. Posted speed limit, age and accident location contribute substantially to increase model accuracy, whereas sex and airbag deployment contribute to a smaller extent and seat position is of limited value. These findings can be used to refine triage protocols used in Sweden and possibly other countries with similar traffic environments.
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| 9. |
- Candefjord, Stefan, 1981, et al.
(författare)
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A wearable microwave instrument can detect and monitor traumatic abdominal injuries in a porcine model
- 2021
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322 .- 2045-2322. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Abdominal injury is a frequent cause of death for trauma patients, and early recognition is essential to limit fatalities. There is a need for a wearable sensor system for prehospital settings that can detect and monitor bleeding in the abdomen (hemoperitoneum). This study evaluates the potential for microwave technology to fill that gap. A simple prototype of a wearable microwave sensor was constructed using eight antennas. A realistic porcine model of hemoperitoneum was developed using anesthetized pigs. Ten animals were measured at healthy state and at two sizes of bleeding. Statistical tests and a machine learning method were used to evaluate blood detection sensitivity. All subjects presented similar changes due to accumulation of blood, which dampened the microwave signal (p< 0.05). The machine learning analysis yielded an area under the receiver operating characteristic (ROC) curve (AUC) of 0.93, showing 100% sensitivity at 90% specificity. Large inter-individual variability of the healthy state signal complicated differentiation of bleedings from healthy state. A wearable microwave instrument has potential for accurate detection and monitoring of hemoperitoneum, with automated analysis making the instrument easy-to-use. Future hardware development is necessary to suppress measurement system variability and enable detection of smaller bleedings.
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| 10. |
- Candefjord, Stefan
(författare)
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Combining a resonance and a Raman sensor : towards a new method for localizing prostate tumors in vivo
- 2007
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Prostate cancer is the most common malignancy diagnosed in western men today. Measuring the amount of prostate-specific antigen in blood is the most widespread tool for diagnosing prostate cancer early on. However, clinical investigations show that many people with high levels of PSA can be healthy and vice versa. Ultrasound-guided biopsies is the clinical method used to prove prostate cancer. Unfortunately, many tumors are overlooked by this procedure. It has been estimated that about 30% of the biopsy examinations fail to find a present tumor. The resonance technique, which can differentiate hard and soft materials, has in vitro been shown to be able to distinguish prostate cancer and healthy epithelial prostate tissue. Raman spectroscopy is also a good candidate for detecting prostate cancer. Our novel idea of combining the resonance and Raman techniques into a portable probe, appropriate for in vivo examinations of the prostate, will be discussed in this report. The two techniques are presented and explained, and the feasibility of combining them is discussed. Planned experiments and investigations are outlined.
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| 11. |
- Candefjord, Stefan, et al.
(författare)
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Combining fibre optic Raman spectroscopy and tactile resonance measurement for tissue characterization
- 2010
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Ingår i: Measurement science and technology. - : IOP Publishing Ltd. - 0957-0233 .- 1361-6501. ; 21:125801, s. 1-8
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Tidskriftsartikel (refereegranskat)abstract
- Tissue characterization is fundamental for identification of pathological conditions. Raman spectroscopy (RS) and tactile resonance measurement (TRM) are two promising techniques that measure biochemical content and stiffness, respectively. They have potential to complement the golden standard-–histological analysis. By combining RS and TRM, complementary information about tissue content can be obtained and specific drawbacks can be avoided. The aim of this study was to develop a multivariate approach to compare RS and TRM information. The approach was evaluated on measurements at the same points on porcine abdominal tissue. The measurement points were divided into five groups by multivariate analysis of the RS data. A regression analysis was performed and receiver operating characteristic (ROC) curves were used to compare the RS and TRM data. TRM identified one group efficiently (area under ROC curve 0.99). The RS data showed that the proportion of saturated fat was high in this group. The regression analysis showed that stiffness was mainly determined by the amount of fat and its composition. We concluded that RS provided additional, important information for tissue identification that was not provided by TRM alone. The results are promising for development of a method combining RS and TRM for intraoperative tissue characterization.
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| 12. |
- Candefjord, Stefan, et al.
(författare)
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Combining scanning haptic microscopy and fibre optic Raman spectroscopy for tissue characterization
- 2012
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Ingår i: Journal of Medical Engineering & Technology. - : Taylor & Francis. - 0309-1902 .- 1464-522X. ; 36:6, s. 319-327
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Tidskriftsartikel (refereegranskat)abstract
- The tactile resonance method (TRM) and Raman spectroscopy (RS) are promising for tissue characterization in vivo. Our goal is to combine these techniques into one instrument, to use TRM for swift scanning, and RS for increasing the diagnostic power. The aim of this study was to determine the classification accuracy, using support vector machines, for measurements on porcine tissue and also produce preliminary data on human prostate tissue. This was done by developing a new experimental set-up combining micro-scale TRMscanning haptic microscopy (SHM)for assessing stiffness on a micro-scale, with fibre optic RS measurements for assessing biochemical content. We compared the accuracy using SHM alone versus SHM combined with RS, for different degrees of tissue homogeneity. The cross-validation classification accuracy for healthy porcine tissue types using SHM alone was 6581%, and when RS was added it increased to 8187%. The accuracy for healthy and cancerous human tissue was 6770% when only SHM was used, and increased to 7277% for the combined measurements. This shows that the potential for swift and accurate classification of healthy and cancerous prostate tissue is high. This is promising for developing a tool for probing the surgical margins during prostate cancer surgery.
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| 13. |
- Candefjord, Stefan
(författare)
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Combining the tactile resonance method and Raman spectroscopy for tissue characterization towards prostate cancer detection
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Prostate cancer (PCa) is the most common male cancer in Europe and the US, and only lung and colorectal cancer have a higher mortality among European men. In Sweden, PCa is the most common cause of cancer-related death for men.The overall aim of this thesis was to explore the need for new and complementary methods for PCa detection and to take the rst step towards a novel approach: combining the tactile resonance method (TRM) and Raman spectroscopy (RS). First, the main methods for PCa detection were reviewed. Second, to establish a robust protocol for RS experiments in vitro, the eects of snap-freezing and laser illumination on porcine prostate tissue were studied using RS and multivariate statistics. Third, measurements on porcine and human tissue were performed to compare the TRM and RS data via multivariate techniques, and to assess the accuracy of classifying healthy and cancerous tissue using a support vector machine algorithm.It was concluded through the literature review that the gold standard for PCa detection and diagnosis, the prostate specic antigen test and systematic biopsy, have low sensitivity and specicity. Indolent and aggressive tumors cannot be reliably dierentiated, and many men are therefore treated either unnecessarily or too late. Clinical benets of the state-of-the-art in PCa imaging - advanced ultrasound and MR techniques - have still not been convincingly shown. There is a need for complementary and cost-eective detection methods. TRM and RS are promising techniques, but hitherto their potential for PCa detection have only been investigated in vitro.In the RS study no evidence of tissue degradation due to 830 nm laser illumination at an irradiance of ∼3 · 1010 W m-2 were found. Snap-freezing and subsequent storage at -80° C gave rise to subtle but signicant changes in Raman spectra, most likely related to alterations in the protein structure. The major changes due to PCa do not seem to be related to the protein structure, hence snap-freezing may be applied in our experiments.The combined measurements on porcine and human prostate tissue showed that RS provided additional discriminatory power to TRM. The classication accuracy for healthy porcine prostate tissue, and for healthy and cancerous human prostate tissue, was > 73%. This shows the power of the support vector machine applied to the combined data.In summary, this work indicates that an instrument combining TRM and RS is a promising complementary method for PCa detection. Snap-freezing of samples may be used in future RS studies of PCa. A combined instrument could be used for tumor-border demarcation during surgery, and potentially for guiding prostate biopsies towards lesions suspicious for cancer. All of this should provide a more secure diagnosis and consequently more effcient treatment of the patient.
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| 14. |
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| 16. |
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| 17. |
- Candefjord, Stefan, et al.
(författare)
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Effects of snap-freezing and near-infrared laser illumination on porcine prostate tissue as measured by Raman spectroscopy
- 2009
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Ingår i: The Analyst. - : Royal Society of Chemistry (RSC). - 0003-2654 .- 1364-5528. ; 134:9, s. 1815-1821
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Tidskriftsartikel (refereegranskat)abstract
- Most Raman spectroscopic studies on tissue are performed in vitro. To assure that the results are applicable to in vivo examinations, preparation protocols and measurement procedures of tissue for in vitro studies should preserve tissue characteristics close to the native state. This study had two aims. The first was to elucidate if photoinduced effects arise during 5 minutes' continuous illumination of tissue with an 830 nm laser at an irradiance of 3 × 1010 W/m2. The second was to investigate the effects of snap-freezing of porcine prostate tissue in liquid nitrogen and subsequent storage at -80 °C, by means of multivariate analysis. 830 nm laser illumination of the specified irradiance did not affect the Raman spectra. A decrease of the spectral background was observed, likely due to photobleaching of tissue fluorophores. Snap-freezing and subsequent storage at -80 °C gave rise to subtle but significant alterations in Raman spectra, most likely related to changes in the protein conformations
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| 18. |
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| 19. |
- Candefjord, Stefan, et al.
(författare)
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Evaluating the use of a Raman fiberoptic probe in conjunction with a resonance sensor for measuring porcine tissue in vitro
- 2009
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Ingår i: IFMBE Proceedings of the World Congress on Medical Physics and Biomedical Engineering. - Heidelberg : Springer. ; , s. 414-417, s. 414-417
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Konferensbidrag (refereegranskat)abstract
- Prostate cancer is the most common form of cancer and is the third leading cause of cancer-related death in European men. There is a need for new methods that can accurately localize and diagnose prostate cancer. In this study a new approach is presented: a combination of resonance sensor technology and Raman spectroscopy. Both methods have shown promising results for prostate cancer detection in vitro. The aim of this study was to evaluate the combined information from measurements with a Raman fiberoptic probe and a resonance sensor system. Pork belly tissue was used as a model system. A three-dimensional translation table was equipped with an in-house developed software, allowing measurements to be performed at the same point using two separate instruments. The Raman data was analyzed using principal component analysis and hierarchical clustering analysis. The spectra were divided into 5 distinct groups. The mean stiffness of each group was calculated from the resonance sensor measurements. One of the groups differed significantly (p < 0.05) from the others. A regression analysis, with the stiffness parameter as response variable and the principal component scores of the Raman data as the predictor variables, explained 67% of the total variability. The use of a smaller resonance sensor tip would probably increase the degree of correlation. In conclusion, Raman spectroscopy provides additional discriminatory power to the resonance sensor.
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| 20. |
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| 21. |
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| 22. |
- Candefjord, Stefan, 1981, et al.
(författare)
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Microwave technology for detecting traumatic intracranial bleedings: tests on phantom of subdural hematoma and numerical simulations
- 2017
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Ingår i: Medical and Biological Engineering and Computing. - : Springer Science and Business Media LLC. - 1741-0444 .- 0140-0118. ; 55:8, s. 1177-1188
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Tidskriftsartikel (refereegranskat)abstract
- Traumatic brain injury is the leading cause of death and severe disability for young people and a major public health problem for elderly. Many patients with intracranial bleeding are treated too late, because they initially show no symptoms of severe injury and are not transported to a trauma center. There is a need for a method to detect intracranial bleedings in the prehospital setting. In this study, we investigate whether broadband microwave technology (MWT) in conjunction with a diagnostic algorithm can detect subdural hematoma (SDH). A human cranium phantom and numerical simulations of SDH are used. Four phantoms with SDH 0, 40, 70 and 110 mL are measured with a MWT instrument. The simulated dataset consists of 1500 observations. Classification accuracy is assessed using fivefold cross-validation, and a validation dataset never used for training. The total accuracy is 100 and 82–96 % for phantom measurements and simulated data, respectively. Sensitivity and specificity for bleeding detection were 100 and 96 %, respectively, for the simulated data. SDH of different sizes is differentiated. The classifier requires training dataset size in order of 150 observations per class to achieve high accuracy. We conclude that the results indicate that MWT can detect and estimate the size of SDH. This is promising for developing MWT to be used for prehospital diagnosis of intracranial bleedings.
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| 23. |
- Candefjord, Stefan, 1981, et al.
(författare)
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Microwave technology for localization of traumatic intracranial bleedings—a numerical simulation study
- 2013
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Ingår i: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. - 1557-170X. - 9781457702167 ; , s. 1948-1951
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Konferensbidrag (refereegranskat)abstract
- Traumatic brain injury (TBI) is a major public health problem worldwide. Intracranial bleedings represents the most serious complication of TBI and need to be surgically evacuated promptly to save lives and mitigate injury. Microwave technology (MWT) is promising as a complement to computed tomography (CT) to be used in road and air ambulances for early detection of intracranial bleedings. In this study, we perform numerical simulations to investigate if a classification algorithm based on singular value decomposition can distinguish between bleedings at different positions adjacent to the skull bone for a similar but simplified problem. The classification accuracy is 94-100% for all classes, a result that encourages us to pursue our efforts with MWT for more realistic scenarios. This indicates that MWT has potential for localizing a detected bleeding, which would increase the diagnostic value of this technique.
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| 24. |
- Candefjord, Stefan, 1981, et al.
(författare)
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Mortality of trauma patients treated at trauma centers compared to non-trauma centers in Sweden: a retrospective study
- 2022
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Ingår i: European Journal of Trauma and Emergency Surgery. - : Springer Science and Business Media LLC. - 1863-9933 .- 1863-9941 .- 1615-3146. ; 48, s. 525-536
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Tidskriftsartikel (refereegranskat)abstract
- Objective The main objective was to compare the 30-day mortality rate of trauma patients treated at trauma centers as compared to non-trauma centers in Sweden. The secondary objective was to evaluate how injury severity influences the potential survival benefit of specialized care. Methods This retrospective study included 29,864 patients from the national Swedish Trauma Registry (SweTrau) during the period 2013-2017. Three sampling exclusion criteria were applied: (1) Injury Severity Score (ISS) of zero; (2) missing data in any variable of interest; (3) data falling outside realistic values and duplicate registrations. University hospitals were classified as trauma centers; other hospitals as non-trauma centers. Logistic regression was used to analyze the effect of trauma center care on mortality rate, while adjusting for other factors potentially affecting the risk of death. Results Treatment at a trauma center in Sweden was associated with a 41% lower adjusted 30-day mortality (odds ratio 0.59 [0.50-0.70],p < 0.0001) compared to non-trauma center care, considering all injured patients (ISS >= 1). The potential survival benefit increased substantially with higher injury severity, with up to > 70% mortality decrease for the most critically injured group (ISS >= 50). Conclusions There exists a potentially substantial survival benefit for trauma patients treated at trauma centers in Sweden, especially for the most severely injured. This study motivates a critical review and possible reorganization of the national trauma system, and further research to identify the characteristics of patients in most need of specialized care.
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| 25. |
- Candefjord, Stefan, 1981, et al.
(författare)
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On-Scene Injury Severity Prediction (OSISP) Algorithm for Truck Occupants
- 2015
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Ingår i: Traffic Injury Prevention. - : Informa UK Limited. - 1538-957X .- 1538-9588. ; 16, s. 190-196
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this study is to develop an on-scene injury severity prediction (OSISP) algorithm for truck occupants using only accident characteristics that are feasible to assess at the scene of the accident. The purpose of developing this algorithm is to use it as a basis for a field triage tool used in traffic accidents involving trucks. In addition, the model can be valuable for recognizing important factors for improving triage protocols used in Sweden and possibly in other countries with similar traffic environments and prehospital procedures.Methods:The scope is adult truck occupants involved in traffic accidents on Swedish public roads registered in the Swedish Traffic Accident Data Acquisition (STRADA) database for calendar years 2003 to 2013. STRADA contains information reported by the police and medical data on injured road users treated at emergency hospitals. Using data from STRADA, 2 OSISP multivariate logistic regression models for deriving the probability of severe injury (defined here as having an Injury Severity Score [ISS]>15)were implemented for light and heavy trucks; that is, trucks with weight up to 3,500 kg and ≥16,500 kg, respectively. A 10-fold cross-validation procedure was used to estimate the performance of the OSISP algorithm in terms of the area under the receiver operating characteristic curve (AUC).Results:The rate of belt use was low, especially for heavy truck occupants. The OSISP models developed for light and heavytrucks achieved cross-validation AUC of 0.81 and 0.74, respectively. The AUC values obtained when the models were evaluated on all data without cross-validation were 0.87 for both light and heavy trucks. The difference in the AUC values with and without use of cross-validation indicates overfitting of the model, which may be a consequence of relatively small data sets. Belt use stands out asthe most valuable predictor in both types of trucks; accident type and age are important predictors for light trucks.Conclusions:The OSISP models achieve good discriminating capability for light truck occupants and a reasonable performance for heavy truck occupants. The prediction accuracy may be increased by acquiring more data. Belt use was the strongest predictor of severe injury for both light and heavy truck occupants. There is a need for behavior-based safety programs and/or other means to encourage truck occupants to always wear a seat belt.
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| 26. |
- Candefjord, Stefan, 1981, et al.
(författare)
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On Scene Injury Severity Prediction (OSISP) machine learning algorithms for motor vehicle crash occupants in US
- 2021
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Ingår i: Journal of Transport and Health. - : Elsevier BV. - 2214-1405. ; 22
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Tidskriftsartikel (refereegranskat)abstract
- A significant proportion of motor vehicle crash fatalities are potentially preventable with improved acute care. By increasing the accuracy of triage more victims could be transported directly to the best suited care facility and be provided optimal care. We hypothesize that On Scene Injury Severity Prediction (OSISP) algorithms, developed utilizing machine learning methods, have potential to improve triage by complementing the field triage protocol. In this study, the accuracy of OSISP algorithms based on the “National Automotive Sampling System - Crashworthiness Data System” (NASS-CDS) of crashes involving adult occupants for calendar years 2010–2015 was evaluated. Severe injury was the dependent variable, defined as Injury Severity Score (ISS) > 15. The dataset contained 37873 subjects, whereof 21589 included injury data and were further analyzed. Selection of model predictors was based on potential for injury severity prediction and perceived feasibility of assessment by first responders. We excluded vehicle telemetry data due to the limited availability of these systems in the contemporary vehicle fleet, and because this data is not yet being utilized in prehospital care. The machine learning algorithms Logistic Regression, Ridge Regression, Bernoulli Naïve Bayes, Stochastic Gradient Descent and Artificial Neural Networks were evaluated. Best performance with small margin was achieved with Logistic Regression, achieving area under the receiver operator characteristic curve (AUC) of 0.86 (95% confidence interval 0.82–0.90), as estimated by 10-fold stratified cross-validation. Ejection, Entrapment, Belt use, Airbag deployment and Crash type were good predictors. Using only a subset of the 5–7 best predictors approached the prediction accuracy achieved when using the full set (14 predictors). A simplified benefit analysis indicated that nationwide implementation of OSISP in the US could bring improved care for 3100 severely injured patients, and reduce unnecessary use of trauma center resources for 94000 non-severely injured patients, every year.
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| 27. |
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| 28. |
- Candefjord, Stefan, 1981, et al.
(författare)
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Prehospital transportation decisions for patients sustaining major trauma in road traffic crashes in Sweden
- 2016
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Ingår i: Traffic Injury Prevention. - : Informa UK Limited. - 1538-9588 .- 1538-957X. ; 17:S1, s. 16-20
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The objective of this study was to evaluate the proportion and characteristics of patients sustaining major trauma in road traffic crashes (RTCs) who could benefit from direct transportation to a trauma center (TC).Methods: Currently, there is no national classification of TC in Sweden. In this study, 7 university hospitals (UHs) in Sweden were selected to represent a TC levelI or levelII. These UHs have similar capabilities as the definition for level I and level II TC in the United States. Major trauma was defined as Injury Severity Score (ISS) > 15. A total of 117,730 patients who were transported by road or air ambulance were selected from the Swedish TRaffic Accident Data Acquisition (STRADA) database between 2007 to 2014. An analysis of the patient characteristics sustaining major trauma in comparison with patients sustaining minor trauma (ISS < 15) was conducted. Major trauma patients transported to a TC versus non-TC were further analysed with respect to injured body region and road user type.Results: Approximately 3% (n = 3, 411) of patients sustained major trauma. Thirty-eight percent of major trauma patients were transported to a TC, and 62% were transported to a non-TC. This results in large proportions of patients with Abbreviated Injury Scale (AIS) 3+ injuries being transported to a non-TC. The number of AIS 3+ head injuries for major trauma patients transported to a TC versus non-TC were similar, whereas a larger number of AIS 3+ thorax injuries were present in the non-TC group. The non-TC major trauma patients had a higher probability of traveling in a car, truck, or bus and to be involved in a crash in a rural location.Conclusions: Our results show that the majority of RTC major trauma patients are transported to a non-TC. This may cause unnecessary morbidity and mortality. These findings can guide the development of improved prehospital treatment guidelines, protocols and decision support systems.
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| 29. |
- Candefjord, Stefan, et al.
(författare)
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Technologies for localization and diagnosis of prostate cancer
- 2009
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Ingår i: Journal of Medical Engineering & Technology. - : Informa UK Limited. - 0309-1902 .- 1464-522X. ; 33:8, s. 585-603
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Tidskriftsartikel (refereegranskat)abstract
- The gold standard for detecting prostate cancer (PCa), systematic biopsy, lacks sensitivity as well as grading accuracy. PSA screening leads to over-treatment of many men, and it is unclear whether screening reduces PCa mortality. This review provides an understanding of the difficulties of localizing and diagnosing PCa. It summarizes recent developments of ultrasound (including elastography) and MRI, and discusses some alternative experimental techniques, such as resonance sensor technology and vibrational spectroscopy. A comparison between the different methods is presented. It is concluded that new ultrasound techniques are promising for targeted biopsy procedures, in order to detect more clinically significant cancers while reducing the number of cores. MRI advances are very promising, but MRI remains expensive and MR-guided biopsy is complex. Resonance sensor technology and vibrational spectroscopy have shown promising results in vitro. There is a need for large prospective multicentre trials that unambiguously prove the clinical benefits of these new techniques.
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| 30. |
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| 31. |
- Candefjord, Stefan
(författare)
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Towards new sensors for prostate cancer detection : combining Raman spectroscopy and resonance sensor technology
- 2009
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Prostate cancer (PCa) is the most common male cancer in Europe and the US, and only lung and colorectal cancer have a higher mortality among European men. In Sweden, PCa is the most common cause of cancer-related death for men.The overall aim of this licentiate work was to explore the need for new and complementary methods for PCa detection and to take the first step towards a novel approach: combining Raman spectroscopy and resonance sensor technology. Firstly, the main methods for PCa detection were reviewed. Secondly, to establish a robust protocol for Raman experiments in vitro, the effects of snap-freezing and laser illumination on porcine prostate tissue were studied using Raman spectroscopy and multivariate statistics. Thirdly, measurements on pork belly tissue using both a resonance sensor and a Raman fiberoptic probe were evaluated regarding correlation of the data.It was concluded that the gold standard for PCa detection and diagnosis, the prostate specific antigen test and systematic biopsy, have low sensitivity and specificity. Indolent and aggressive tumors cannot be reliably differentiated, and many men are therefore treated either unnecessarily or too late. Clinical benefits of the state-of-the-art in PCa imaging - advanced ultrasound and MR techniques - have still not been convincingly shown. There is a need for complementary and cost-effective detection methods. Raman spectroscopy and resonance sensor technology are promising alternative techniques, but hitherto their potential for PCa detection have only been investigated in vitro.No evidence of tissue degradation due to 830 nm laser illumination at an irradiance of 3 1010 W/m2 were found. Snap-freezing and subsequent storage at -80◦C gave rise to subtle but significant changes in Raman spectra, most likely related to alterations in the protein structure. The major changes in cancerous prostate tissue do not seem to be related to the protein structure, hence snap-freezing may be applied.The combined measurements on pork belly tissue showed that Raman spectroscopy provided additional discriminatory power to the resonance sensor. The Raman data explained 67% of the variability of the stiffness parameter. The differentiation of tissue types using the resonance sensor was relatively poor, likely due to its large sample volume compared to the Raman sensor. A smaller resonance sensor tip may improve the results.In summary, this work indicates that an instrument combining Raman spectroscopy and resonance sensor technology is a promising complementary method for PCa detection. Snap-freezing of samples may be used in future Raman studies of PCa. A combined instrument could potentially be used to guide prostate biopsies towards lesions suspicious for cancer, and for tumor-border demarcation during surgery. All of this should provide a more secure diagnosis and consequently more efficient treatment of the patient.
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| 32. |
- Candefjord, Stefan, 1981, et al.
(författare)
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Using Smartphones to Monitor Cycling and Automatically Detect Accidents - Towards eCall Functionality for Cyclists
- 2014
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Ingår i: Proceedings, International Cycling Safety Conference 2014. ; , s. 1-9
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Konferensbidrag (refereegranskat)abstract
- Automatic crash notification to the nearest emergency center in case of a traffic accident will through the EU initiative eCall improve the safety for cars on European roads. eCall function- ality could also increase the safety for vulnerable road users such as cyclists, but there is no technical implementation agreed upon for this purpose. We propose to use smartphones due to their widespread availability and no need for extra hardware. Today’s high-end smartphones are equipped with both GPS functionality and movement sensors. The aims of this study were to explore if smartphones can be used to collect cycling data of sufficient quality and to design and evaluate a crash detection algorithm (CDA) for cycling accidents. A Google Nexus 4 smartphone was chosen for the study. This device is equipped with a combined accelerometer and gyroscope chip. Over five hours of “normal” cycling data, i.e. without accidents/incidents, was collected. Six crash tests were performed using a simplified crash test dummy. In order to achieve a realistic user scenario the smartphone was allowed to be easily carried as in everyday use, i.e. the users were not required to fix it to the body. We used the total acceleration based on the sum of square of each direction to obtain a measure independent on smartphone orientation. For normal cycling this measure was found to momentarily be as high as 50 ms−2. High levels were often due to handling of the smartphone. This prompted that an acceleration threshold alone is not sufficient for an accurate CDA. A marked rotation during a short time interval was found to be an important predictor for crashes. An accurate CDA was designed based on a combination of sensor data such as acceleration and rotation. The CDA detected all crashes and was subsequently evaluated in several hours of normal cycling without any false positive alarms.
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| 33. |
- Candefjord, Stefan, et al.
(författare)
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Video support for prehospital stroke consultation: implications for system design and clinical implementation from prehospital simulations
- 2024
-
Ingår i: BMC Medical Informatics and Decision Making. - 1472-6947. ; 24:1
-
Tidskriftsartikel (refereegranskat)abstract
- BackgroundVideo consultations between hospital-based neurologists and Emergency Medical Services (EMS) have potential to increase precision of decisions regarding stroke patient assessment, management and transport. In this study we explored the use of real-time video streaming for neurologist–EMS consultation from the ambulance, using highly realistic full-scale prehospital simulations including role-play between on-scene EMS teams, simulated patients (actors), and neurologists specialized in stroke and reperfusion located at the remote regional stroke center.MethodsVideo streams from three angles were used for collaborative assessment of stroke using the National Institutes of Health Stroke Scale (NIHSS) to assess symptoms affecting patient’s legs, arms, language, and facial expressions. The aim of the assessment was to determine appropriate management and transport destination based on the combination of geographical location and severity of stroke symptoms. Two realistic patient scenarios were created, with severe and moderate stroke symptoms, respectively. Each scenario was simulated using a neurologist acting as stroke patient and an ambulance team performing patient assessment. Four ambulance teams with two nurses each all performed both scenarios, for a total of eight cases. All scenarios were video recorded using handheld and fixed cameras. The audio from the video consultations was transcribed. Each team participated in a semi-structured interview, and neurologists and actors were also interviewed. Interviews were audio recorded and transcribed.ResultsAnalysis of video-recordings and post-interviews (n = 7) show a more thorough prehospital patient assessment, but longer total on-scene time, compared to a baseline scenario not using video consultation. Both ambulance nurses and neurologists deem that video consultation has potential to provide improved precision of assessment of stroke patients. Interviews verify the system design effectiveness and suggest minor modifications.ConclusionsThe results indicate potential patient benefit based on a more effective assessment of the patient’s condition, which could lead to increased precision in decisions and more patients receiving optimal care. The findings outline requirements for pilot implementation and future clinical tests.
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| 34. |
- Fagerlind, Helen, 1975, et al.
(författare)
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Does injury pattern among major road trauma patients influence prehospital transport decisions regardless of the distance to the nearest trauma centre? – a retrospective study
- 2019
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Ingår i: Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine. - : Springer Science and Business Media LLC. - 1757-7241. ; 27:1, s. 1-9
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Tidskriftsartikel (refereegranskat)abstract
- Prehospital undertriage occurs when the required level of care for a major trauma patient is underestimated and the patient is transported to a lower-level emergency care facility. One possible reason is that the pattern of injuries exceeding a certain severity threshold is not easily recognizable in the field. The present study aims to examine whether the injury patterns of major road trauma patients are associated with trauma centre transport decisions in Sweden, controlling for the distance from the crash to the nearest trauma centre and other patient characteristics. The Swedish Traffic Accident Data Acquisition (STRADA) database was queried from April 2011 to March 2017. Teaching hospitals with neurosurgery capabilities were classified as trauma centres (TC), all other hospitals were classified as other emergency departments (ED). Injury Severity Score ≥ 13 was used as the threshold for major trauma. Ten common injury patterns were derived from the STRADA data; six patterns included serious neuro trauma to the head or spine. The remaining four patterns were: other severe injuries, moderate to serious abdomen injuries, serious thorax injuries and all other remaining injury patterns. Logistic regression was used to analyse the effect of injury patterns, age, sex and distance from crash to nearest TC on transport decision (TC or ED). Of the 2542 patients, 38.0% were transported to a TC, equating to a prehospital undertriage of 62%. Over half (59.4%) of the patients had four or more Abbreviated Injury Scale (AIS) 2+ injuries. After controlling for age, sex and distance to nearest TC, only patients sustaining serious head injuries together with other severe injuries had significantly higher odds of being transported to a TC (OR = 4.18, 95% CI: 2.03, 8.73). The odds of being transported to a TC decreased by 5% with every kilometre further away the crash location was to the nearest TC. These results highlight that there is considerable prehospital undertriage in Sweden and suggest that distance to nearest TC is more influential in transport decisions than injury pattern. These results can be used to further develop prehospital transportation guidelines and designation of trauma centres.
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| 35. |
- Fhager, Andreas, 1976, et al.
(författare)
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3D Simulations of Intracerebral Hemorrhage Detection Using Broadband Microwave Technology
- 2019
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 19:16
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Tidskriftsartikel (refereegranskat)abstract
- Early, preferably prehospital, detection of intracranial bleeding after trauma or stroke would dramatically improve the acute care of these large patient groups. In this paper, we use simulated microwave transmission data to investigate the performance of a machine learning classification algorithm based on subspace distances for the detection of intracranial bleeding. A computational model, consisting of realistic human head models of patients with bleeding, as well as healthy subjects, was inserted in an antenna array model. The Finite-Difference Time-Domain (FDTD) method was then used to generate simulated transmission coefficients between all possible combinations of antenna pairs. These transmission data were used both to train and evaluate the performance of the classification algorithm and to investigate its ability to distinguish patients with versus without intracranial bleeding. We studied how classification results were affected by the number of healthy subjects and patients used to train the algorithm, and in particular, we were interested in investigating how many samples were needed in the training dataset to obtain classification results better than chance. Our results indicated that at least 200 subjects, i.e., 100 each of the healthy subjects and bleeding patients, were needed to obtain classification results consistently better than chance (p < 0.05 using Student's t-test). The results also showed that classification results improved with the number of subjects in the training data. With a sample size that approached 1000 subjects, classifications results characterized as area under the receiver operating curve (AUC) approached 1.0, indicating very high sensitivity and specificity.
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| 36. |
- Fhager, Andreas, 1976, et al.
(författare)
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FDTD based simulation study of a classification based hemorrhagic stroke detector
- 2018
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Ingår i: IET Conference Publications. - : Institution of Engineering and Technology. ; 2018:CP741
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Konferensbidrag (refereegranskat)abstract
- The possibility to detect intracranial bleedings caused by stroke or head trauma in a prehospital setting would be a major breakthrough in the strive to deliver the best possible care for these patients. Our research is focused on developing a microwave based diagnostic system for prehospital use, which is capable of detecting intracranial bleedings. This paper contains a numerical simulation study to investigate the detection capability of a machine learning algorithm and its performance for differentiating hemorrhagic stroke from patients with no bleeding. The goal is to assess the performance of the detection algorithm as a function of the number of patients included in the training phase. The results show that this approach is feasible, but that it requires one thousand patients for training the algorithm, in order to reach a detection rate with AUC values approaching 0.9.
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| 37. |
- Fhager, Andreas, 1976, et al.
(författare)
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Microwave Diagnostics Ahead: Saving Time and the Lives of Trauma and Stroke Patients
- 2018
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Ingår i: IEEE Microwave Magazine. - 1527-3342 .- 1557-9581. ; 19:3, s. 78-90
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Tidskriftsartikel (refereegranskat)abstract
- Microwave technology has the potential to revolutionize how, when, and what care can be delivered to patients with acute, life-threatening medical conditions. The prospects are that microwave systems can both improve diagnostic ability and accuracy and enable earlier diagnosis. Early diagnosis is a key factor in acute situations, especially when breathing and circulation are affected. Conventional imaging modalities used for diagnostics, such as magnetic resonance imaging (MRI) and X-ray computed tomography (CT), are powerful but normally available only at hospitals.
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| 38. |
- Fhager, Andreas, 1976, et al.
(författare)
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Simulation Study of a Haemorrhagic Stroke Detector and Its Performance
- 2019
-
Ingår i: 13th European Conference on Antennas and Propagation, EuCAP 2019.
-
Konferensbidrag (refereegranskat)abstract
- Intracranial bleedings caused by stroke or head trauma is a serious condition that need immediate medical care and interventions. Pre-hospital detection and diagnosis would constitute a major breakthrough in streamlining the care and in reducing the time from incidence to start of treatment. In this paper we present a numerical simulation study to investigate the detection capability of a machine learning algorithm and its performance when diagnosing patients with intracranial bleedings from healthy subjects, for example hemorrhagic stroke patients from healthy persons. The specific goal is to study the training phase of the classifier and how parameters, such as number of antennas, number of training samples, noise, etc. affect the ability to detect bleedings with different volumes. The detection performance is evaluated in a cross-validation scheme.
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| 39. |
- Forcolin, Fabio, et al.
(författare)
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Comparison of outlier heartbeat identification and spectral transformation strategies for deriving heart rate variability indices for drivers at different stages of sleepiness
- 2018
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Ingår i: Traffic Injury Prevention. - : Taylor & Francis. - 1538-9588 .- 1538-957X. ; 19, s. S112-S119
-
Tidskriftsartikel (refereegranskat)abstract
- Objective: Appropriate preprocessing for detecting and removing outlier heartbeats and spectral transformation is essential for deriving heart rate variability (HRV) indices from cardiac monitoring data with high accuracy. The objective of this study is to evaluate agreement between standard preprocessing methods for cardiac monitoring data used to detect outlier heartbeats and perform spectral transformation, in relation to estimating HRV indices for drivers at different stages of sleepiness.Methods: The study analyzed more than 3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. Electrocardiography (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The Pan-Tompkins algorithm was used for peak detection of heartbeats from ECG data. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat interval deviating >30% from the mean of the 4 previous intervals, and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain; these methods were (1) the Fourier transform; (2) an autoregressive model; and (3) the Lomb-Scargle periodogram. The preprocessing methods were compared quantitatively and by assessing agreement between estimations of 13 common HRV indices using Bland-Altman plots and paired Student's t-tests.Results: The PC method detected more than 4times as many outliers (0.28%) than SD (0.065%). Most HRV indices derived using different preprocessing methods exhibited significant systematic (P <.05) and substantial random variations.Conclusions: The standard preprocessing methods for HRV data for outlier heartbeat detection and spectral transformation show low levels of agreement. This finding implies that, prior to designing algorithms for detection of sleepy drivers based on HRV analysis, the impact of different preprocessing methods and combinations thereof on driver sleepiness assessment needs to be studied.
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| 40. |
- Jalo, Hoor, 1994, et al.
(författare)
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Early Characterization of Stroke Using Video Analysis and Machine Learning
- 2023
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Ingår i: Emerging Technologies in Healthcare and Medicine. - 9781958651926 ; 116:2023, s. 74-84
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Konferensbidrag (refereegranskat)abstract
- Stroke is one of the leading causes of death and disability worldwide and requires an immediate attention as the longer the patient is left untreated, the more sever its outcomes are. Enhancing access to optimal treatment and reducing mortality rates require improving the accuracy of stroke characterization methods in prehospital settings. This study explores how video analysis and machine learning (ML) can be leveraged to identify stroke symptoms on the National Institute of Health Stroke Scale (NIHSS), with the goal of facilitating the prehospital management of patients with suspected stroke. A total of 888 videos were captured from the research group members, who mimicked stroke symptoms including facial palsy, leg and arm paresis, ataxia and dysarthria, following the criteria of the NIHSS. Multiple algorithms, utilized in earlier studies, were examined to predict these symptoms, and their performance was assessed using accuracy, sensitivity and specificity. The best method for detecting facial palsy was found using Histogram of Oriented Gradients (HOG) features in conjunction with Adaptive Boosting (AdaBoost), achieving an accuracy, sensitivity and specificity values of 97.8%, 98.0% and 97.0%, respectively. The identification of arm paresis reached 100% on all metrics using a combination of MediaPipe and SVM. For leg paresis, all algorithms had poor detection rates. The outcome for ataxia for both limbs varied. Google Cloud Speech-to-Text was used to detect dysarthria and reached 100% on all evaluation metrics. These findings suggest that video analysis and ML have the potential to assist early stroke diagnosis, but further research is needed to validate this.
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| 41. |
- Jalo, Hoor, 1994, et al.
(författare)
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Early identification and characterisation of stroke to support prehospital decision-making using artificial intelligence : A scoping review protocol
- 2023
-
Ingår i: BMJ Open. - : BMJ Publishing Group. - 2044-6055. ; 13:5
-
Tidskriftsartikel (refereegranskat)abstract
- Introduction Stroke is a time-critical condition and one of the leading causes of mortality and disability worldwide. To decrease mortality and improve patient outcome by improving access to optimal treatment, there is an emerging need to improve the accuracy of the methods used to identify and characterise stroke in prehospital settings and emergency departments (EDs). This might be accomplished by developing computerised decision support systems (CDSSs) that are based on artificial intelligence (AI) and potential new data sources such as vital signs, biomarkers and image and video analysis. This scoping review aims to summarise literature on existing methods for early characterisation of stroke by using AI. Methods and analysis The review will be performed with respect to the Arksey and O'Malley's model. Peer-reviewed articles about AI-based CDSSs for the characterisation of stroke or new potential data sources for stroke CDSSs, published between January 1995 and April 2023 and written in English, will be included. Studies reporting methods that depend on mobile CT scanning or with no focus on prehospital or ED care will be excluded. Screening will be done in two steps: title and abstract screening followed by full-text screening. Two reviewers will perform the screening process independently, and a third reviewer will be involved in case of disagreement. Final decision will be made based on majority vote. Results will be reported using a descriptive summary and thematic analysis. Ethics and dissemination The methodology used in the protocol is based on information publicly available and does not need ethical approval. The results from the review will be submitted for publication in a peer-reviewed journal. The findings will be shared at relevant national and international conferences and meetings in the field of digital health and neurology. © 2023 BMJ Publishing Group. All rights reserved.
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| 42. |
- Jalo, Hoor, 1994, et al.
(författare)
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Stroke Prehospital Decision Support Systems Based on Artificial Intelligence: Grey Literature Scoping Review
- 2024
-
Ingår i: Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2. - 2184-4305.
-
Konferensbidrag (refereegranskat)abstract
- Stroke is a leading cause of mortality and disability worldwide. Therefore, there is a growing interest in prehospital point-of-care stroke clinical decision support systems (CDSSs), which with improved precision can identify stroke and decrease the time to optimal treatment, thereby improving clinical outcomes. Artificial intelligence (AI) may be a route to improve CDSSs for clinical benefit. Deploying AI in the area of prehospital stroke care is still in its infancy. There are several existing systematic and scoping reviews summarizing the progress of AI methods for stroke assessment. None of these reviews include grey literature, which could be a valuable source of information, especially when analysing future research and development directions. This paper aims to use grey literature to investigate stroke assessment CDSSs based on AI. The study adheres to PRISMA guidelines and presents seven records showcasing promising technologies. These records included three clinical trials, two smartphone applications, one master thesis and one PhD dissertation, which identify electroencephalogram (EEG), video analysis and voice and facial recognition as potential data sources for early stroke identification. The integration of these technologies may offer the prospect of faster and more accurate CDSSs in the future.
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| 43. |
- Lee, Eunji, et al.
(författare)
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Development of Verified Innovation Process for Healthcare Solutions (VIPHS): A Stepwise Model for Digital Health
- 2023
-
Ingår i: Studies in Health Technology and Informatics. ; , s. 736-740
-
Konferensbidrag (refereegranskat)abstract
- Many digital health projects often stop in the pilot or test phase. Realisation of new digital health services is often challenging due to lack of guidelines for the step-by-step roll-out and implementation of the systems when changing work processes and procedures are needed. This study describes development of the Verified Innovation Process for Healthcare Solutions (VIPHS) – a stepwise model for digital health innovation and utilisation using service design principles. A multiple case study (two cases) involving participant observation, role play, and semi-structured interviews were conducted for the model development in prehospital settings. The model might be helpful to support realisation of innovative digital health projects in a holistic, disciplined, and strategic way.
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| 44. |
- Lee, Eunji, 1980, et al.
(författare)
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Development of Verified Innovation Process for Healthcare Solutions (VIPHS): A Stepwise Model for Digital Health
- 2023
-
Ingår i: Studies in health technology and informatics. - 1879-8365 .- 0926-9630. ; 302, s. 736-740
-
Tidskriftsartikel (refereegranskat)abstract
- Many digital health projects often stop in the pilot or test phase. Realisation of new digital health services is often challenging due to lack of guidelines for the step-by-step roll-out and implementation of the systems when changing work processes and procedures are needed. This study describes development of the Verified Innovation Process for Healthcare Solutions (VIPHS) - a stepwise model for digital health innovation and utilisation using service design principles. A multiple case study (two cases) involving participant observation, role play, and semi-structured interviews were conducted for the model development in prehospital settings. The model might be helpful to support realisation of innovative digital health projects in a holistic, disciplined, and strategic way.
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| 45. |
- Ljungqvist, Johan, et al.
(författare)
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Clinical Evaluation of a Microwave-Based Device for Detection of Traumatic Intracranial Hemorrhage
- 2017
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Ingår i: Journal of Neurotrauma. - : Mary Ann Liebert Inc. - 0897-7151 .- 1557-9042. ; 34:13, s. 2176-2182
-
Tidskriftsartikel (refereegranskat)abstract
- Traumatic brain injury (TBI) is the leading cause of death and disability among young persons. A key to improve outcome for patients with TBI is to reduce the time from injury to definitive care by achieving high triage accuracy. Microwave technology (MWT) allows for a portable device to be used in the pre-hospital setting for detection of intracranial hematomas at the scene of injury, thereby enhancing early triage and allowing for more adequate early care. MWT has previously been evaluated for medical applications including the ability to differentiate between hemorrhagic and ischemic stroke. The purpose of this study was to test whether MWT in conjunction with a diagnostic mathematical algorithm could be used as a medical screening tool to differentiate patients with traumatic intracranial hematomas, chronic subdural hematomas (cSDH), from a healthy control (HC) group. Twenty patients with cSDH and 20 HC were measured with a MWT device. The accuracy of the diagnostic algorithm was assessed using a leave-one-out analysis. At 100% sensitivity, the specificity was 75%-i.e., all hematomas were detected at the cost of 25% false positives (patients who would be overtriaged). Considering the need for methods to identify patients with intracranial hematomas in the pre-hospital setting, MWT shows promise as a tool to improve triage accuracy. Further studies are under way to evaluate MWT in patients with other intracranial hemorrhages.
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| 46. |
- Lu, Ke, 1991, et al.
(författare)
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Detecting driver fatigue using heart rate variability: A systematic review
- 2022
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Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575 .- 1879-2057. ; 178
-
Tidskriftsartikel (refereegranskat)abstract
- Driver fatigue detection systems have potential to improve road safety by preventing crashes and saving lives. Conventional driver monitoring systems based on driving performance and facial features may be challenged by the application of automated driving systems. This limitation could potentially be overcome by monitoring systems based on physiological measurements. Heart rate variability (HRV) is a physiological marker of interest for detecting driver fatigue that can be measured during real life driving. This systematic review investigates the relationship between HRV measures and driver fatigue, as well as the performance of HRV based fatigue detection systems. With the applied eligibility criteria, 18 articles were identified in this review. Inconsistent results can be found within the studies that investigated differences of HRV measures between alert and fatigued drivers. For studies that developed HRV based fatigue detection systems, the detection performance showed a large variation, where the detection accuracy ranged from 44% to 100%. The inconsistency and variation of the results can be caused by differences in several key aspects in the study designs. Progress in this field is needed to determine the relationship between HRV and different fatigue causal factors and its connection to driver performance. To be deployed, HRV-based fatigue detection systems need to be thoroughly tested in real life conditions with good coverage of relevant driving scenarios and a sufficient number of participants.
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| 47. |
- Lu, Ke, 1991, et al.
(författare)
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Detecting Driver Sleepiness Using Consumer Wearable Devices in Manual and Partial Automated Real-Road Driving
- 2022
-
Ingår i: IEEE Transactions on Intelligent Transportation Systems. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1524-9050 .- 1558-0016. ; 23:5, s. 4801 -4810
-
Tidskriftsartikel (refereegranskat)abstract
- Driver sleepiness constitutes a well-known traffic safety risk. With the introduction of automated driving systems, the chance of getting sleepy and even falling asleep at wheel could increase further. Conventional sleepiness detection methods based on driving performance and behavior may not be available under automated driving. Methods based on physiological measurements such as heart rate variability (HRV) becomes a potential solution under automated driving. However, with reduced task load, HRV could potentially be affected by automated driving. Therefore, it is essential to investigate the influence of automated driving on the relation between HRV and sleepiness. Data from real-road driving experiments with 43 participants were used in this study. Each driver finished four trials with manual and partial automated driving under normal and sleep-deprived condition. Heart rate was monitored by consumer wearable chest bands. Subjective sleepiness based on Karolinska sleepiness scale was reported at five min interval as ground truth. Reduced heart rate and increased overall variability were found in association with severe sleepy episodes. A binary classifier based on the AdaBoost method was developed to classify alert and sleepy episodes. The results indicate that partial automated driving has small impact on the relationship between HRV and sleepiness. The classifier using HRV features reached area under curve (AUC) = 0.76 and it was improved to AUC = 0.88 when adding driving time and day/night information. The results show that commercial wearable heart rate monitor has the potential to become a useful tool to assess driver sleepiness under manual and partial automated driving.
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| 48. |
- Lu, Ke, et al.
(författare)
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Heart rate variability as an indicator for driver fatigue, different effects of time of day and time-on-task
- 2022
-
Ingår i: DDI 2022 Gothenburg. - Göteborg : Safer. ; , s. 98-100
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Heart rate variability (HRV) has been considered as a potential physiological marker for driver fatigue. However, consensus has not been reached for how HRV changes during the development of fatigue, due to inconsistent results in the literature. One potential cause for inconsistent results is that different causal factors were used to introduce fatigue. The aim of this study is to investigate how HRV parameters change during driving in relation to fatigue caused by sleep related and task related factors. Data from a real road experiment, with 89 participants who drove four times over a 180 km route, were used for the analysis. We investigated how time of day and time-on-task factors influence HRV parameters. The result shows that different HRV parameters react differently in relation to time of day and time-on-task factors. The result emphasizes the importance of considering the causal factors when interpreting results from driver fatigue studies and when developing fatigue detectors based on physiological measures.
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| 49. |
- Nyberg, Morgan, et al.
(författare)
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A combined tactile and Raman probe for tissue characterization - Design considerations
- 2012
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Ingår i: Measurement Science and Technology. - : IOP Publishing. - 0957-0233 .- 1361-6501. ; 23:6
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Tidskriftsartikel (refereegranskat)abstract
- Histopathology is the golden standard for cancer diagnosis and involves the characterization of tissue components. It is labour intensive and time consuming. We have earlier proposed a combined fibre-optic near-infrared Raman spectroscopy (NIR-RS) and tactile resonance method (TRM) probe for detecting positive surgical margins as a complement to interoperative histopathology. The aims of this study were to investigate the effects of attaching an RS probe inside a cylindrical TRM sensor and to investigate how laser-induced heating of the fibre-optic NIR-RS affected the temperature of the RS probe tip and an encasing TRM sensor. In addition, the possibility to perform fibre-optic NIR-RS in a well-lit environment was investigated. A small amount of rubber latex was preferable for attaching the thin RS probe inside the TRM sensor. The temperature rise of the TRM sensor due to a fibre-optic NIR-RS at 270 mW during 20 s was less than 2 degrees C. Fibre-optic NIR-RS was feasible in a dimmed bright environment using a small light shield and automatic subtraction of a pre-recorded contaminant spectrum. The results are promising for a combined probe for tissue characterization.
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