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A comprehensive RCT in screening, surveillance, and diagnostic AI-assisted colonoscopies (ACCENDO-Colo study).
Dig Liver Dis
January 2025
Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy; Università Cattolica del Sacro Cuore, 00168, Roma, Italy.
Background And Aims: Adenoma detection rate (ADR) serves as a primary quality metric in colonoscopy. Various computer-aided detection (CADe) tools have emerged, yielding diverse impacts on ADR across different demographic cohorts. This study aims to evaluate a new CADe system in patients undergoing colonoscopy.
View Article and Find Full Text PDFHistoColAi: An open-source web platform for collaborative digital histology image annotation with AI-driven predictive integration.
Comput Methods Programs Biomed
January 2025
Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, Universitat Politècnica de València, Valencia, Spain; valgrAI: Valencian Graduate School and Research Network of Artificial Intelligence, Valencia, Spain.
Digital pathology is now a standard component of the pathology workflow, offering numerous benefits such as high-detail whole slide images and the capability for immediate case sharing between hospitals. Recent advances in deep learning-based methods for image analysis make them a potential aid in digital pathology. However, A significant challenge in developing computer-aided diagnostic systems for pathology is the lack of intuitive, open-source web applications for data annotation.
View Article and Find Full Text PDFExtracellular vesicles in liquid biopsies: there is hope for oral squamous cell carcinoma.
Extracell Vesicles Circ Nucl Acids
December 2024
Department of Otorhinolaryngology, Head and Neck Surgery, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong 00000, China.
Current approaches to oral cancer diagnosis primarily involve physical examination, tissue biopsy, and advanced computer-aided imaging techniques. However, despite these advances, patient survival rates have not significantly improved. Hence, there is a critical need to develop minimally invasive tools with high sensitivity and specificity to improve patient survival and quality of life.
View Article and Find Full Text PDFDeep learning model to diagnose cardiac amyloidosis from haematoxylin/eosin-stained myocardial tissue.
Eur Heart J Imaging Methods Pract
January 2025
Department of Cardiovascular Medicine, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.
Aims: Amyloid deposition in myocardial tissue is a definitive feature for diagnosing cardiac amyloidosis, though less invasive imaging modalities such as bone tracer cardiac scintigraphy and cardiac magnetic resonance imaging have been established as first steps for its diagnosis. This study aimed to develop a deep learning model to support the diagnosis of cardiac amyloidosis from haematoxylin/eosin (HE)-stained myocardial tissue.
Methods And Results: This single-centre retrospective observational study enrolled 166 patients who underwent myocardial biopsies between 2008 and 2022, including 76 patients diagnosed with cardiac amyloidosis and 90 with other diagnoses.
Deciphering the Anti-Diabetic Potential of Gymnema Sylvestre Using Integrated Computer-Aided Drug Design and Network Pharmacology.
J Cell Mol Med
January 2025
Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia.
This study explores novel therapeutic avenues for diabetes, a global health concern marked by elevated blood glucose levels. We investigated the anti-diabetic potential of Gymnema Sylvestre's bioactive compounds, including Gymnemic acid I, Stigmasterol, Deacylgymnemic acid, Beta-Amyrin acetate, Longispinogenin, Gymnemic acid II, Gymnemic acid, Gymnemic acid X, Gymnemaside VI, Phytic acid and Gymnemic acid X. Employing network pharmacology, molecular docking and molecular dynamics (MD), we elucidated the potential mechanism of action.
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