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| http://dx.doi.org/10.1187/cbe.12-09-0162 | DOI Listing |
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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 PDFFoundation Models for Translational Cancer Biology.
Annu Rev Biomed Data Sci
January 2025
1Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California, USA;
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View Article and Find Full Text PDFA Deep Learning Framework for Automated Classification and Archiving of Orthodontic Diagnostic Documents.
Cureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
View Article and Find Full Text PDFResearch Best Practices for Eye Stroke Vision Rehabilitation: A Qualitative Study.
Curr Eye Res
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Department of Ophthalmology, Duke University, Durham, NC, USA.
Purpose: Central retinal artery occlusion, also known as an eye stroke, results in visual impairment and functional challenges. Our study objectives were to identify meaningful measures and factors that indicate or enable successful recovery after eye stroke and to determine optimal processes to support research, including exploring barriers and facilitators to successful research participation.
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Using artificial intelligence to evaluate adherence to best practices in one anastomosis gastric bypass: first steps in a real-world setting.
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Division of General Surgery, Bariatric Unit, Tel Aviv Medical Center, Affiliated to Sackler Faculty of Medicine, Tel Aviv University, 6, Weizman St, 6423906, Tel- Aviv, Israel.
Background: Safety in one anastomosis gastric bypass (OAGB) is judged by outcomes, but it seems reasonable to utilize best practices for safety, whose performance can be evaluated and therefore improved. We aimed to test an artificial intelligence-based model in real world for the evaluation of adherence to best practices in OAGB.Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary.
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