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Performance of Chat Generative Pre-trained Transformer-4o in the Adult Clinical Cardiology Self-Assessment Program.
Eur Heart J Digit Health
January 2025
Cardiovascular Center, Tufts Medical Center, 800 Washington Street, Boston, MA 02111, USA.
Aims: This study evaluates the performance of OpenAI's latest large language model (LLM), Chat Generative Pre-trained Transformer-4o, on the Adult Clinical Cardiology Self-Assessment Program (ACCSAP).
Methods And Results: Chat Generative Pre-trained Transformer-4o was tested on 639 ACCSAP questions, excluding 45 questions containing video clips, resulting in 594 questions for analysis. The questions included a mix of text-based and static image-based [electrocardiogram (ECG), angiogram, computed tomography (CT) scan, and echocardiogram] formats.
Research on atrial fibrillation diagnosis in electrocardiograms based on CLA-AF model.
Eur Heart J Digit Health
January 2025
Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, No. 88 West Taishan Road, Zhuzhou 412007, Hunan, China.
Aims: The electrocardiogram (ECG) is the primary method for diagnosing atrial fibrillation (AF), but interpreting ECGs can be time-consuming and labour-intensive, which deserves more exploration.
Methods And Results: We collected ECG data from 6590 patients as YY2023, classified as Normal, AF, and Other. Convolutional Neural Network (CNN), bidirectional Long Short-Term Memory (BiLSTM), and Attention construct the AF recognition model CNN BiLSTM Attention-Atrial Fibrillation (CLA-AF).
Advanced electrocardiography heart age: a prognostic, explainable machine learning approach applicable to sinus and non-sinus rhythms.
Eur Heart J Digit Health
January 2025
Kolling Institute, Royal North Shore Hospital, University of Sydney, St Leonards, Sydney, NSW 2065, Australia.
Aims: An explainable advanced electrocardiography (A-ECG) Heart Age gap is the difference between A-ECG Heart Age and chronological age. This gap is an estimate of accelerated cardiovascular aging expressed in years of healthy human aging, and can intuitively communicate cardiovascular risk to the general population. However, existing A-ECG Heart Age requires sinus rhythm.
View Article and Find Full Text PDFClinically interpretable multiclass neural network for discriminating cardiac diseases.
Heliyon
January 2025
Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche, Ancona, 60131, Italy.
Background: Deep-learning applications in cardiology typically perform trivial binary classification and are able to discriminate between subjects affected or not affected by a specific cardiac disease. However, this working scenario is very different from the real one, where clinicians are required to recognize the occurrence of one cardiac disease among the several possible ones, performing a multiclass classification. The present work aims to create a new interpretable deep-learning tool able to perform a multiclass classification and, thus, discriminate among several different cardiac diseases.
View Article and Find Full Text PDFSafety, Tolerability, and Pharmacokinetics of a Novel Anti-obesity Agent, S-309309, in Healthy Adults with or Without Obesity.
Clin Drug Investig
January 2025
Medical Science Department, Shionogi & Co., Ltd., Osaka, Japan.
Background: Anti-obesity medications are recommended for patients who do not achieve and maintain weight loss despite lifestyle interventions. S-309309 is a novel oral inhibitor of monoacylglycerol O-acyltransferase 2 being developed as a treatment for obesity.
Objective: The objective of the study was to investigate the safety, clinical pharmacology, pharmacokinetics and pharmacodynamic biomarker of S-309309.
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