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- 2019
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Tidskriftsartikel (refereegranskat)
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| 3. |
- Ademuyiwa, Adesoji O., et al.
(författare)
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Determinants of morbidity and mortality following emergency abdominal surgery in children in low-income and middle-income countries
- 2016
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Ingår i: BMJ Global Health. - : BMJ Publishing Group Ltd. - 2059-7908. ; 1:4
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Tidskriftsartikel (refereegranskat)abstract
- Background: Child health is a key priority on the global health agenda, yet the provision of essential and emergency surgery in children is patchy in resource-poor regions. This study was aimed to determine the mortality risk for emergency abdominal paediatric surgery in low-income countries globally.Methods: Multicentre, international, prospective, cohort study. Self-selected surgical units performing emergency abdominal surgery submitted prespecified data for consecutive children aged <16 years during a 2-week period between July and December 2014. The United Nation's Human Development Index (HDI) was used to stratify countries. The main outcome measure was 30-day postoperative mortality, analysed by multilevel logistic regression.Results: This study included 1409 patients from 253 centres in 43 countries; 282 children were under 2 years of age. Among them, 265 (18.8%) were from low-HDI, 450 (31.9%) from middle-HDI and 694 (49.3%) from high-HDI countries. The most common operations performed were appendectomy, small bowel resection, pyloromyotomy and correction of intussusception. After adjustment for patient and hospital risk factors, child mortality at 30 days was significantly higher in low-HDI (adjusted OR 7.14 (95% CI 2.52 to 20.23), p<0.001) and middle-HDI (4.42 (1.44 to 13.56), p=0.009) countries compared with high-HDI countries, translating to 40 excess deaths per 1000 procedures performed.Conclusions: Adjusted mortality in children following emergency abdominal surgery may be as high as 7 times greater in low-HDI and middle-HDI countries compared with high-HDI countries. Effective provision of emergency essential surgery should be a key priority for global child health agendas.
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| 4. |
- Egenäs, Carl, et al.
(författare)
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Electronically Vacuum RegulatedShut-off Valve for Milking System
- 2023
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Rapport (refereegranskat)abstract
- This report was part of a capstone project course for the Mechatronics track at KTH Royal Institute of Technology. This project was conducted in collaboration with DeLaval International AB. DeLaval is a producer of dairy farm machinery and equipment. This project aimed to solve a problem that occurs during a typical milking process of a cow. The problem in question is a decrease of suction at the teats of the cow as the milk flow increases. The main stakeholder requirement was to develop a new electronically vacuum-regulated valve that eliminates this decrease of suction as the milk flow varies. A test rig was built to imitate a real milking system and to test possible design solutions. The final valve design implemented was an inverted pinch valve which is pneumatically actuated by a vacuum signal. Testing showed that a convincing majority of DeLaval’s requirements could be satisfied.
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- Ma, Wenzhong, et al.
(författare)
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Membrane formation by thermally induced phase separation : materials, involved parameters, modeling, current efforts and future directions
- 2023
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Ingår i: Journal of Membrane Science. - : Elsevier. - 0376-7388 .- 1873-3123. ; 669
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Tidskriftsartikel (refereegranskat)abstract
- Thermally-induced phase separation (TIPS) is one of the most popular methods considered for membrane preparation. Since its introduction by Castro in 1981, there has been significant progress in understanding, controlling, and implementing TIPS. This review provides a critical and integrative evaluation of the literature in this area that effectively defines the current state-of-the-art. It begins with an overview of the basic principles of TIPS and the used materials (polymers, diluents and additives) paying particular attention to the sustainability of the TIPS process. The subsequent sections examine the parameters affecting the outcome of TIPS technique, the role of mass transfer, and methods for modeling TIPS. This is followed by a discussion of current and potential applications of TIPS membranes. Finally, the review concludes with a discussion of likely future developments and prospects for the TIPS process.
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| 6. |
- Sahin, O, et al.
(författare)
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International Multi-Specialty Expert Physician Preoperative Identification of Extranodal Extension n Oropharyngeal Cancer Patients using Computed Tomography: Prospective Blinded Human Inter-Observer Performance Evaluation
- 2024
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Ingår i: medRxiv : the preprint server for health sciences. - : Cold Spring Harbor Laboratory.
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundExtranodal extension (ENE) is an important adverse prognostic factor in oropharyngeal cancer (OPC) and is often employed in therapeutic decision making. Clinician-based determination of ENE from radiological imaging is a difficult task with high inter-observer variability. However, the role of clinical specialty on the determination of ENE has been unexplored.MethodsPre-therapy computed tomography (CT) images for 24 human papillomavirus-positive (HPV+) OPC patients were selected for the analysis; 6 scans were randomly chosen to be duplicated, resulting in a total of 30 scans of which 21 had pathologically-confirmed ENE. 34 expert clinician annotators, comprised of 11 radiologists, 12 surgeons, and 11 radiation oncologists separately evaluated the 30 CT scans for ENE and noted the presence or absence of specific radiographic criteria and confidence in their prediction. Discriminative performance was measured using accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and Brier score for each physician. Statistical comparisons of discriminative performance were calculated using Mann Whitney U tests. Significant radiographic factors in correct discrimination of ENE status were determined through a logistic regression analysis. Interobserver agreement was measured using Fleiss’ kappa.ResultsThe median accuracy for ENE discrimination across all specialties was 0.57. There were significant differences between radiologists and surgeons for Brier score (0.33 vs. 0.26), radiation oncologists and surgeons for sensitivity (0.48 vs. 0.69), and radiation oncologists and radiologists/surgeons for specificity (0.89 vs. 0.56). There were no significant differences between specialties for accuracy or AUC. Indistinct capsular contour, nodal necrosis, and nodal matting were significant factors in regression analysis. Fleiss’ kappa was less than 0.6 for all the radiographic criteria, regardless of specialty.ConclusionsDetection of ENE in HPV+OPC patients on CT imaging remains a difficult task with high variability, regardless of clinician specialty. Although some differences do exist between the specialists, they are often minimal. Further research in automated analysis of ENE from radiographic images is likely needed.
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| 7. |
- Sahlsten, J, et al.
(författare)
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Application of simultaneous uncertainty quantification for image segmentation with probabilistic deep learning: Performance benchmarking of oropharyngeal cancer target delineation as a use-case
- 2023
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Ingår i: medRxiv : the preprint server for health sciences. - : Cold Spring Harbor Laboratory.
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- BackgroundOropharyngeal cancer (OPC) is a widespread disease, with radiotherapy being a core treatment modality. Manual segmentation of the primary gross tumor volume (GTVp) is currently employed for OPC radiotherapy planning, but is subject to significant interobserver variability. Deep learning (DL) approaches have shown promise in automating GTVp segmentation, but comparative (auto)confidence metrics of these models predictions has not been well-explored. Quantifying instance-specific DL model uncertainty is crucial to improving clinician trust and facilitating broad clinical implementation. Therefore, in this study, probabilistic DL models for GTVp auto-segmentation were developed using large-scale PET/CT datasets, and various uncertainty auto-estimation methods were systematically investigated and benchmarked.MethodsWe utilized the publicly available 2021 HECKTOR Challenge training dataset with 224 co-registered PET/CT scans of OPC patients with corresponding GTVp segmentations as a development set. A separate set of 67 co-registered PET/CT scans of OPC patients with corresponding GTVp segmentations was used for external validation. Two approximate Bayesian deep learning methods, the MC Dropout Ensemble and Deep Ensemble, both with five submodels, were evaluated for GTVp segmentation and uncertainty performance. The segmentation performance was evaluated using the volumetric Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance at 95% (95HD). The uncertainty was evaluated using four measures from literature: coefficient of variation (CV), structure expected entropy, structure predictive entropy, and structure mutual information, and additionally with our novelDice-riskmeasure. The utility of uncertainty information was evaluated with the accuracy of uncertainty-based segmentation performance prediction using the Accuracy vs Uncertainty (AvU) metric, and by examining the linear correlation between uncertainty estimates and DSC. In addition, batch-based and instance-based referral processes were examined, where the patients with high uncertainty were rejected from the set. In the batch referral process, the area under the referral curve with DSC (R-DSC AUC) was used for evaluation, whereas in the instance referral process, the DSC at various uncertainty thresholds were examined.ResultsBoth models behaved similarly in terms of the segmentation performance and uncertainty estimation. Specifically, the MC Dropout Ensemble had 0.776 DSC, 1.703 mm MSD, and 5.385 mm 95HD. The Deep Ensemble had 0.767 DSC, 1.717 mm MSD, and 5.477 mm 95HD. The uncertainty measure with the highest DSC correlation was structure predictive entropy with correlation coefficients of 0.699 and 0.692 for the MC Dropout Ensemble and the Deep Ensemble, respectively. The highest AvU value was 0.866 for both models. The best performing uncertainty measure for both models was the CV which had R-DSC AUC of 0.783 and 0.782 for the MC Dropout Ensemble and Deep Ensemble, respectively. With referring patients based on uncertainty thresholds from 0.85 validation DSC for all uncertainty measures, on average the DSC improved from the full dataset by 4.7% and 5.0% while referring 21.8% and 22% patients for MC Dropout Ensemble and Deep Ensemble, respectively.ConclusionWe found that many of the investigated methods provide overall similar but distinct utility in terms of predicting segmentation quality and referral performance. These findings are a critical first-step towards more widespread implementation of uncertainty quantification in OPC GTVp segmentation.
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| 8. |
- Sahlsten, J, et al.
(författare)
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Segmentation stability of human head and neck cancer medical images for radiotherapy applications under de-identification conditions: Benchmarking data sharing and artificial intelligence use-cases
- 2023
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Ingår i: Frontiers in oncology. - : Frontiers Media SA. - 2234-943X. ; 13, s. 1120392-
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Tidskriftsartikel (refereegranskat)abstract
- Demand for head and neck cancer (HNC) radiotherapy data in algorithmic development has prompted increased image dataset sharing. Medical images must comply with data protection requirements so that re-use is enabled without disclosing patient identifiers. Defacing, i.e., the removal of facial features from images, is often considered a reasonable compromise between data protection and re-usability for neuroimaging data. While defacing tools have been developed by the neuroimaging community, their acceptability for radiotherapy applications have not been explored. Therefore, this study systematically investigated the impact of available defacing algorithms on HNC organs at risk (OARs).MethodsA publicly available dataset of magnetic resonance imaging scans for 55 HNC patients with eight segmented OARs (bilateral submandibular glands, parotid glands, level II neck lymph nodes, level III neck lymph nodes) was utilized. Eight publicly available defacing algorithms were investigated: afni_refacer, DeepDefacer, defacer, fsl_deface, mask_face, mri_deface, pydeface, and quickshear. Using a subset of scans where defacing succeeded (N=29), a 5-fold cross-validation 3D U-net based OAR auto-segmentation model was utilized to perform two main experiments: 1.) comparing original and defaced data for training when evaluated on original data; 2.) using original data for training and comparing the model evaluation on original and defaced data. Models were primarily assessed using the Dice similarity coefficient (DSC).ResultsMost defacing methods were unable to produce any usable images for evaluation, while mask_face, fsl_deface, and pydeface were unable to remove the face for 29%, 18%, and 24% of subjects, respectively. When using the original data for evaluation, the composite OAR DSC was statistically higher (p ≤ 0.05) for the model trained with the original data with a DSC of 0.760 compared to the mask_face, fsl_deface, and pydeface models with DSCs of 0.742, 0.736, and 0.449, respectively. Moreover, the model trained with original data had decreased performance (p ≤ 0.05) when evaluated on the defaced data with DSCs of 0.673, 0.693, and 0.406 for mask_face, fsl_deface, and pydeface, respectively.ConclusionDefacing algorithms may have a significant impact on HNC OAR auto-segmentation model training and testing. This work highlights the need for further development of HNC-specific image anonymization methods.
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| 9. |
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| 10. |
- Zhou, Zhuang, et al.
(författare)
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Thermally induced phase separation
- 2024
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Ingår i: Polymeric membrane formation by phase inversion. - : Elsevier. - 9780323956284 - 9780323956291 ; , s. 37-82
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Thermally induced phase separation (TIPS) stands as one of the most widely employed methods for preparing polymeric membranes in industrial and academic settings. This chapter aims to provide comprehensive background information on the TIPS method. First, the mechanism of phase inversion from both thermodynamic and kinetic perspectives will be presented. Subsequently, the impact of preparation parameters on membrane performance will be analyzed. The chapter also emphasizes the crucial role of mass transfer in the TIPS process. Moreover, the practical application of the membranes prepared using the TIPS method will be highlighted. Finally, in the concluding section, a summary and future perspectives will be provided.
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