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Optical coherence tomography-enabled classification of the human venoatrial junction.
J Biomed Opt
January 2025
Columbia University, Department of Electrical Engineering, New York, United States.
Significance: Radiofrequency ablation to treat atrial fibrillation (AF) involves isolating the pulmonary vein from the left atria to prevent AF from occurring. However, creating ablation lesions within the pulmonary veins can cause adverse complications.
Aim: We propose automated classification algorithms to classify optical coherence tomography (OCT) volumes of human venoatrial junctions.
An automatic and real-time echocardiography quality scoring system based on deep learning to improve reproducible assessment of left ventricular ejection fraction.
Quant Imaging Med Surg
January 2025
Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Background: Echocardiography can conveniently, rapidly, and economically evaluate the structure and function of the heart, and has important value in the diagnosis and evaluation of cardiovascular diseases (CVDs). However, echocardiography still exhibits significant variability in image acquisition and diagnosis, with a heavy dependency on the operator's experience. Image quality affects disease diagnosis in the later stage, and even image quality assessment still has variability in human evaluation.
View Article and Find Full Text PDFAI Machine Learning-Based Diabetes Prediction in Older Adults in South Korea: Cross-Sectional Analysis.
JMIR Form Res
January 2025
1, Department of Health Administration, College of Software and Digital Healthcare Convergence, Yonsei University, Changjogwan, Yonseidae-gil 1, Wonju, 26493, Republic of Korea, +82 (0) 33-760-2257.
Background: Diabetes is prevalent in older adults, and machine learning algorithms could help predict diabetes in this population.
Objective: This study determined diabetes risk factors among older adults aged ≥60 years using machine learning algorithms and selected an optimized prediction model.
Methods: This cross-sectional study was conducted on 3084 older adults aged ≥60 years in Seoul from January to November 2023.
Author Correction: An ensemble method for improving robustness against the electrode contact problems in automated sleep stage scoring.
Sci Rep
January 2025
Center for Computational Sciences, University of Tsukuba, Tsukuba, Japan.
Visceral condition assessment through digital tongue image analysis.
Front Artif Intell
January 2025
School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China.
Traditional Chinese medicine (TCM) has long utilized tongue diagnosis as a crucial method for assessing internal visceral condition. This study aims to modernize this ancient practice by developing an automated system for analyzing tongue images in relation to the five organs, corresponding to the heart, liver, spleen, lung, and kidney-collectively known as the "five viscera" in TCM. We propose a novel tongue image partitioning algorithm that divides the tongue into four regions associated with these specific organs, according to TCM principles.
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