| 1. |
- Rosendahl, Lene, 1963-, et al.
(författare)
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Computer-assisted calculation of myocardial infarct size shortens the evaluation time of contrast-enhanced cardiac MRI
- 2008
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Ingår i: Clinical Physiology and Functional Imaging. - : John Wiley & Sons. - 1475-0961 .- 1475-097X. ; 28:1, s. 1-7
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Tidskriftsartikel (refereegranskat)abstract
- Background: Delayed enhancement magnetic resonance imaging depicts scar in the left ventricle which can be quantitatively measured. Manual segmentation and scar determination is time consuming. The purpose of this study was to evaluate a software for infarct quantification, to compare with manual scar determination, and to measure the time saved.Methods: Delayed enhancement magnetic resonance imaging was performed in 40 patients where myocardial perfusion single photon emission computed tomography imaging showed irreversible uptake reduction suggesting a myocardial scar. After segmentation, the semi-automatic software was applied. A scar area was displayed, which could be corrected and compared with manual delineation. The different time steps were recorded with both methods.Results: The software shortened the average evaluation time by 12.4min per cardiac exam, compared with manual delineation. There was good correlation of myocardial volume, infarct volume and infarct percentage (%) between the two methods, r = 0.95, r = 0.92 and r = 0.91 respectively.Conclusions: A computer software for myocardial volume and infarct size determination cut the evaluation time by more than 50% compared with manual assessment, with maintained clinical accuracy.
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| 2. |
- Andersson, Jonas, 1975-, et al.
(författare)
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Artificial intelligence and the medical physics profession-A Swedish perspective
- 2021
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Ingår i: Physica Medica-European Journal of Medical Physics. - : Elsevier BV. - 1120-1797 .- 1724-191X. ; 88, s. 218-225
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Tidskriftsartikel (refereegranskat)abstract
- Background: There is a continuous and dynamic discussion on artificial intelligence (AI) in present-day society. AI is expected to impact on healthcare processes and could contribute to a more sustainable use of resources allocated to healthcare in the future. The aim for this work was to establish a foundation for a Swedish perspective on the potential effect of AI on the medical physics profession. Materials and methods: We designed a survey to gauge viewpoints regarding AI in the Swedish medical physics community. Based on the survey results and present-day situation in Sweden, a SWOT analysis was performed on the implications of AI for the medical physics profession. Results: Out of 411 survey recipients, 163 responded (40%). The Swedish medical physicists with a professional license believed (90%) that AI would change the practice of medical physics but did not foresee (81%) that AI would pose a risk to their practice and career. The respondents were largely positive to the inclusion of AI in educational programmes. According to self-assessment, the respondents' knowledge of and workplace preparedness for AI was generally low. Conclusions: From the survey and SWOT analysis we conclude that AI will change the medical physics profession and that there are opportunities for the profession associated with the adoption of AI in healthcare. To overcome the weakness of limited AI knowledge, potentially threatening the role of medical physicists, and build upon the strong position in Swedish healthcare, medical physics education and training should include learning objectives on AI.
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| 3. |
- Khoshnood, Ardavan
(författare)
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Prehospital Diagnosis and Oxygen Treatment in ST Elevation Myocardial Infarction
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- IntroductionPaper I: An Artificial Neural Network (ANN) was constructed to identify ST Elevation Myocardial Infarction (STEMI) and predict the need for Percutaneous Coronary Intervention (PCI). Paper II, III and IV: Studies suggest that O2 therapy may be harmful in STEMI patients. We therefore conducted the SOCCER study to evaluate the effects of O2 therapy in STEMI patients.MethodsPaper I: 560 ambulance ECGs sent to the Cardiac Care Unit (CCU), was together with the CCU physicians interpretation and decision of conducting an acute PCI or not collected, and compared with the interpretation and PCI decision of the ANN. Paper II, III, IV: Normoxic (≥94%) STEMI patients accepted for acute PCI were in the ambulance randomized to standard care with 10 L/min O2 or room air. A subset of the patients underwent echocardiography for determination of the Left Ventricular Ejection Fraction (LVEF) and the Wall Motion Score Index (WMSI). All patients had a Cardiac Magnetic Resonance Imaging (CMRI) to evaluate Myocardial area at Risk (MaR), Infarct Size (IS) and Myocardial Salvage Index (MSI).ResultsPaper I: The area under the ANN’s receiver operating characteristics curve for STEMI detection as well as predicting the need of acute PCI were very good.Paper II, III, IV: No significant differences could be shown in discussing MaR, MSI or IS between the O2 group (n=46) and the air group (n=49). Neither could any differences be shown for LVEF and WMSI at the index visit as well after six months between the O2 group (n=46) and the air group (n=41)ConclusionsPaper I: The results indicate that the number of ECGs sent to the CCU could be reduced with 2/3 as the ANN would safely identify ECGs not being STEMI.Paper II, III, IV: The results suggest that it is safe to withhold O2 therapy in normoxic, stable STEMI patients.
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| 4. |
- Borrelli, P., et al.
(författare)
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AI-based detection of lung lesions in F-18 FDG PET-CT from lung cancer patients
- 2021
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Ingår i: Ejnmmi Physics. - : Springer Science and Business Media LLC. - 2197-7364. ; 8:1
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Tidskriftsartikel (refereegranskat)abstract
- Background[F-18]-fluorodeoxyglucose (FDG) positron emission tomography with computed tomography (PET-CT) is a well-established modality in the work-up of patients with suspected or confirmed diagnosis of lung cancer. Recent research efforts have focused on extracting theragnostic and textural information from manually indicated lung lesions. Both semi-automatic and fully automatic use of artificial intelligence (AI) to localise and classify FDG-avid foci has been demonstrated. To fully harness AI's usefulness, we have developed a method which both automatically detects abnormal lung lesions and calculates the total lesion glycolysis (TLG) on FDG PET-CT.MethodsOne hundred twelve patients (59 females and 53 males) who underwent FDG PET-CT due to suspected or for the management of known lung cancer were studied retrospectively. These patients were divided into a training group (59%; n = 66), a validation group (20.5%; n = 23) and a test group (20.5%; n = 23). A nuclear medicine physician manually segmented abnormal lung lesions with increased FDG-uptake in all PET-CT studies. The AI-based method was trained to segment the lesions based on the manual segmentations. TLG was then calculated from manual and AI-based measurements, respectively and analysed with Bland-Altman plots.ResultsThe AI-tool's performance in detecting lesions had a sensitivity of 90%. One small lesion was missed in two patients, respectively, where both had a larger lesion which was correctly detected. The positive and negative predictive values were 88% and 100%, respectively. The correlation between manual and AI TLG measurements was strong (R-2 = 0.74). Bias was 42 g and 95% limits of agreement ranged from -736 to 819 g. Agreement was particularly high in smaller lesions.ConclusionsThe AI-based method is suitable for the detection of lung lesions and automatic calculation of TLG in small- to medium-sized tumours. In a clinical setting, it will have an added value due to its capability to sort out negative examinations resulting in prioritised and focused care on patients with potentially malignant lesions.
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| 5. |
- Szaro, Pavel, et al.
(författare)
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Magnetic resonance imaging of the brachial plexus. Part 2: Traumatic injuries
- 2022
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Ingår i: European Journal of Radiology Open (EJR Open). - : Elsevier BV. - 2352-0477. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- The most common indications for magnetic resonance imaging (MRI) of the brachial plexus (BP) are traumatic injuries. The role of MRI of the BP has increased because of recent trends favoring earlier surgery. Determining preganglionic vs. postganglionic injury is essential, as different treatment strategies are required. Thus, MRI of the BP should be supplemented with cervical spine MRI to assess the intradural part of the spinal nerves, including highly T2-weighted techniques. Acute preganglionic injuries usually manifest as various combinations of post-traumatic pseudomeningocele, the absence of roots, deformity of nerve root sleeves, displacement of the spinal cord, hemorrhage in the spinal canal, presence of scars in the spinal canal, denervation of the back muscles, and syrinx. Spinal nerve root absence is more specific than pseudomeningocele on MRI. Acute postganglionic injuries can present as lesions in continuity or tears. The following signs indicate injury to the BP: side-to-side difference, swelling, partial, or total BP rupture. Injury patterns and localization are associated with the mechanism of trauma, which implies a significant role for MRI in the work-up of patients. The identification and description of traumatic lesions involving the brachial plexus need to be systematic and detailed. Using an appropriate MRI protocol, obtaining details about the injury, applying a systematic anatomical approach, and correlating imaging findings to relevant clinical data to make a correct diagnosis. Information about the presence or suspicion of root avulsion should always be provided.
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| 6. |
- Grynne, A., et al.
(författare)
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Women's experience of the health information process involving a digital information tool before commencing radiation therapy for breast cancer : a deductive interview study
- 2023
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Ingår i: BMC Health Services Research. - : BioMed Central (BMC). - 1472-6963. ; 23:1
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Individuals undergoing radiation therapy for breast cancer frequently request information before, throughout and after the treatment as a means to reduce distress. Nevertheless, the provision of information to meet individuals needs from their level of health literacy is often overlooked. Thus, individuals information needs are often unmet, leading to reports of discontent. Internet and digital information technology has significantly augmented the available information and changed the way in which persons accesses and comprehends information. As health information is no longer explicitly obtained from healthcare professionals, it is essential to examine the sequences of the health information process in general, and in relation to health literacy. This paper reports on qualitative interviews, targeting women diagnosed with breast cancer who were given access to a health information technology tool, Digi-Do, before commencing radiation therapy, during, and after treatment. METHODS: A qualitative research design, inspired by the integrated health literacy model, was chosen to enable critical reflection by the participating women. Semi-structured interviews were conducted with 15 women with access to a digital information tool, named Digi-Do, in addition to receiving standard information (oral and written) before commencing radiation therapy, during, and after treatment. A deductive thematic analysis process was conducted. RESULTS: The results demonstrate how knowledge, competence, and motivation influence women's experience of the health information process. Three main themes were found: Meeting interactive and personal needs by engaging with health information; Critical recognition of sources of information; and Capability to communicate comprehended health information. The findings reflect the women's experience of the four competencies: to access, understand, appraise, and apply, essential elements of the health information process. CONCLUSIONS: We can conclude that there is a need for tailored digital information tools, such as the Digi-Do, to enable iterative access and use of reliable health information before, during and after the radiation therapy process. The Digi-Do can be seen as a valuable complement to the interpersonal communication with health care professionals, facilitating a better understanding, and enabling iterative access and use of reliable health information before, during and after the radiotherapy treatment. This enhances a sense of preparedness before treatment starts.
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| 7. |
- Almén, Anja, et al.
(författare)
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Challenges assessing radiation risk in image-guided treatments-implications on optimisation of radiological protection
- 2018
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Ingår i: Journal of Radiological Protection. - : IOP Publishing. - 0952-4746 .- 1361-6498. ; 38:3, s. 1064-1076
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Tidskriftsartikel (refereegranskat)abstract
- The present work explores challenges when assessing organ dose and effective dose concerning image-guided treatments. During these treatments considerable x-ray imaging is employed using technically advanced angiographic x-ray equipment. Thus, the radiation dose to organs and the related radiation risk are relatively difficult to assess. This has implications on the optimisation process, in which assessing radiation dose is one important part. In this study, endovascular aortic repair treatments were investigated. Organ dose and effective dose were assessed using Monte Carlo calculations together with a detailed specification of the exposure situation and patient size. The resulting normalised organ dose and effective dose with respect to kerma-area product for patient sizes and radiation qualities representative for the patient group were evaluated. The variability and uncertainty were investigated and their possible impact on optimisation of radiation protection was discussed. Exposure parameters, source to detector distances etc varied between treatments and also varied between image acquisitions during one treatment. Thus the derived normalised organ dose and effective dose exhibited a large range of values depending greatly on used exposure parameters and patient configuration. The derived normalised values for effective dose varied approximately between 0.05 and 0.30 mSv per Gy.cm(2) when taking patient sizes and exposure parameters into consideration, the values for organ doses exhibited even larger variation. The study shows a possible systematic error for derived organ doses and effective dose up to a factor of 7 if detailed exposure or patient characteristics are not known and/or not taken into consideration. The intra-treatment variability was also substantial and the normalised dose values varied up to a factor of 2 between image acquisitions during one treatment. The study shows that the use of conversion factors that are not adapted to the clinic can cause the radiation dose to be exaggerated or underestimated considerably. A conclusion from the present study is that the systematic error could be large and should be estimated together with random errors. A large uncertainty makes it difficult to detect true differences in radiation dose between methods and technology-a prerequisite for optimising radiation protection for image-guided treatments.
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| 8. |
- Sundlöv, Anna, et al.
(författare)
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Individualised Lu-177-DOTATATE treatment of neuroendocrine tumours based on kidney dosimetry
- 2017
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Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - : Springer Science and Business Media LLC. - 1619-7070 .- 1619-7089. ; 44:9, s. 1480-1489
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Tidskriftsartikel (refereegranskat)abstract
- Purpose To present data from an interim analysis of a Phase II trial designed to determine the feasibility, safety, and efficacy of individualising treatment based on renal dosimetry, by giving as many cycles as possible within a maximum renal biologically effective dose (BED). Method Treatment was given with repeated cycles of 7.4 GBq 177Lu-DOTATATE at 8-12-week intervals. Detailed dosimetry was performed in all patients after each cycle using a hybrid method (SPECT + planar imaging). All patients received treatment up to a renal BED of 27 +/- 2 Gy (alpha/beta = 2.6 Gy) (Step 1). Selected patients were offered further treatment up to a renal BED of 40 +/- 2 Gy (Step 2). Renal function was followed by estimation and measurement of the glomerular filtration rate (GFR). Results Fifty-one patients were included in the present analysis. Among the patients who received treatment as planned, the median number of cycles in Step 1 was 5 (range 3-7), and for those who completed Step 2 it was 7 (range 5-8); 73% were able to receive >4 cycles. Although GFR decreased in most patients after the completion of treatment, no grade 3-4 toxicity was observed. Patients with a reduced baseline GFR seemed to have an increased risk of GFR decline. Five patients received treatment in Step 2, none of whom exhibited a significant reduction in renal function. Conclusions Individualising PRRT using renal dosimetry seems feasible and safe and leads to an increased number of cycles in the majority of patients. The trial will continue as planned.
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| 9. |
- Borrelli, P., et al.
(författare)
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Artificial intelligence-aided CT segmentation for body composition analysis: a validation study
- 2021
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Ingår i: European Radiology Experimental. - : Springer Science and Business Media LLC. - 2509-9280. ; 5:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundBody composition is associated with survival outcome in oncological patients, but it is not routinely calculated. Manual segmentation of subcutaneous adipose tissue (SAT) and muscle is time-consuming and therefore limited to a single CT slice. Our goal was to develop an artificial-intelligence (AI)-based method for automated quantification of three-dimensional SAT and muscle volumes from CT images.MethodsEthical approvals from Gothenburg and Lund Universities were obtained. Convolutional neural networks were trained to segment SAT and muscle using manual segmentations on CT images from a training group of 50 patients. The method was applied to a separate test group of 74 cancer patients, who had two CT studies each with a median interval between the studies of 3days. Manual segmentations in a single CT slice were used for comparison. The accuracy was measured as overlap between the automated and manual segmentations.ResultsThe accuracy of the AI method was 0.96 for SAT and 0.94 for muscle. The average differences in volumes were significantly lower than the corresponding differences in areas in a single CT slice: 1.8% versus 5.0% (p <0.001) for SAT and 1.9% versus 3.9% (p < 0.001) for muscle. The 95% confidence intervals for predicted volumes in an individual subject from the corresponding single CT slice areas were in the order of 20%.Conclusions The AI-based tool for quantification of SAT and muscle volumes showed high accuracy and reproducibility and provided a body composition analysis that is more relevant than manual analysis of a single CT slice.
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| 10. |
- Ying, T. M., et al.
(författare)
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Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer
- 2021
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Ingår i: European Radiology Experimental. - : Springer Science and Business Media LLC. - 2509-9280. ; 5:1
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Tidskriftsartikel (refereegranskat)abstract
- Background Radical cystectomy for urinary bladder cancer is a procedure associated with a high risk of complications, and poor overall survival (OS) due to both patient and tumour factors. Sarcopenia is one such patient factor. We have developed a fully automated artificial intelligence (AI)-based image analysis tool for segmenting skeletal muscle of the torso and calculating the muscle volume. Methods All patients who have undergone radical cystectomy for urinary bladder cancer 2011-2019 at Sahlgrenska University Hospital, and who had a pre-operative computed tomography of the abdomen within 90 days of surgery were included in the study. All patients CT studies were analysed with the automated AI-based image analysis tool. Clinical data for the patients were retrieved from the Swedish National Register for Urinary Bladder Cancer. Muscle volumes dichotomised by the median for each sex were analysed with Cox regression for OS and logistic regression for 90-day high-grade complications. The study was approved by the Swedish Ethical Review Authority (2020-03985). Results Out of 445 patients who underwent surgery, 299 (67%) had CT studies available for analysis. The automated AI-based tool failed to segment the muscle volume in seven (2%) patients. Cox regression analysis showed an independent significant association with OS (HR 1.62; 95% CI 1.07-2.44; p = 0.022). Logistic regression did not show any association with high-grade complications. Conclusion The fully automated AI-based CT image analysis provides a low-cost and meaningful clinical measure that is an independent biomarker for OS following radical cystectomy.
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