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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

Differentiating Ischemic From Nonischemic T-Wave Inversion Using a Multimodal Vision-Language Model With

Yunzhang Cheng¹, Zhongkai Wang², Wen Zhang¹

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, Room 1002, 10th Floor, Zhuoyue Building, No. 334 Jungong Road, Shanghai, Yangpu District, 200093, China, +86 18566664556.

JMIR Medical Informatics

|June 19, 2026

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This summary is machine-generated.

A novel AI framework, ECG-R1, accurately differentiates ischemic from nonischemic T-wave inversion (TWI) on ECGs using reinforcement learning. This approach improves diagnostic accuracy and provides interpretable reasoning for clinical decision support.

Area of Science:

Artificial Intelligence in Cardiology
Machine Learning for Medical Diagnostics
Multimodal Medical Data Analysis

Background:

Differentiating ischemic from nonischemic T-wave inversion (TWI) on electrocardiograms (ECGs) is a critical diagnostic challenge.
Visual interpretation of TWI has low positive predictive value (~50%), leading to high false-positive rates and unnecessary procedures.
Existing deep learning models for TWI lack multimodality and transparency, limiting clinical trust and adoption.

Purpose of the Study:

To develop a novel diagnostic framework for accurately differentiating ischemic from nonischemic TWI.
To leverage a multimodal vision-language model trained with reinforcement learning (RL) for improved accuracy and interpretable reasoning.
To address the limitations of current AI models in processing complex data and providing transparent clinical insights.

Keywords:

artificial intelligence AI ECG T-wave inversion computer-aided diagnosis electrocardiogram multimodal artificial intelligence myocardial ischemia reinforcement learning vision-language models

Related Experiment Videos

Last Updated: Jun 21, 2026

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

Main Methods:

Developed ECG-R1, a multimodal framework using the Qwen2-VL-2B vision-language model, analyzing ECG images and clinical text.
Employed an RL paradigm with group relative policy optimization, avoiding supervised fine-tuning (SFT).
Trained the model to generate structured outputs with explicit reasoning traces and diagnostic answers, using a rule-based reward function for accuracy and format adherence.

Main Results:

ECG-R1 achieved state-of-the-art in-domain accuracy (75.21%) and AUC (84.18%) on a dataset of 12,917 TWI cases.
Demonstrated robust cross-hospital generalization with 72.93% out-of-domain accuracy and 81.56% AUC.
RL paradigm significantly improved performance (6.69% in-domain, 11.48% out-of-domain) over SFT baselines, indicating superior learning of physiological features.

Conclusions:

The RL-based ECG-R1 framework significantly outperformed SFT baselines in diagnostic accuracy and robustness.
ECG-R1 offers a transparent clinical decision support system by modeling interpretable reasoning and using probabilistic language.
The framework is designed for safe integration into clinical workflows via a human-in-the-loop paradigm, paving the way for trials.