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Transformers for Neuroimage Segmentation: Scoping Review.
J Med Internet Res
January 2025
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation.
View Article and Find Full Text PDFEvaluating the Development, Reliability, and Validation of the Tele-Primary Care Oral Health Clinical Information System Questionnaire: Cross-Sectional Questionnaire Study.
JMIR Hum Factors
January 2025
Department of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras Kuala Lumpur, Malaysia.
Background: Evaluating digital health service delivery in primary health care requires a validated questionnaire to comprehensively assess users' ability to implement tasks customized to the program's needs.
Objective: This study aimed to develop, test the reliability of, and validate the Tele-Primary Care Oral Health Clinical Information System (TPC-OHCIS) questionnaire for evaluating the implementation of maternal and child digital health information systems.
Methods: A cross-sectional study was conducted in 2 phases.
Prediction of rain garden runoff control effects based on multiple machine learning techniques.
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Shaanxi Huashan Road and Bridge Group Co., Ltd., Xi'an, People's Republic of China.
Due to the rapid development of urbanisation, cities frequently experience waterlogging during rainfall. Rain gardens are widely used in new urban construction because they effectively control surface runoff from rainwater, thereby reducing waterlogging. The runoff control effectiveness of rain gardens is influenced by multiple factors.
View Article and Find Full Text PDFThe TOXIN knowledge graph: supporting animal-free risk assessment of cosmetics.
Database (Oxford)
January 2025
Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels 1090, Belgium.
The European Union's ban on animal testing for cosmetic products and their ingredients, combined with the lack of validated animal-free methods, poses challenges in evaluating their potential repeated-dose organ toxicity. To address this, innovative strategies like Next-Generation Risk Assessment (NGRA) are being explored, integrating historical animal data with new mechanistic insights from non-animal New Approach Methodologies (NAMs). This paper introduces the TOXIN knowledge graph (TOXIN KG), a tool designed to retrieve toxicological information on cosmetic ingredients, with a focus on liver-related data.
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