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Updated: Aug 10, 2025
Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
Published on: April 11, 2025
Multi-center retrospective cohort study applying deep learning to electrocardiograms to identify left heart valvular
Akhil Vaid1,2, Edgar Argulian3,4, Stamatios Lerakis3,4
1The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Insights
Deep learning models effectively detect Aortic Stenosis and Mitral Regurgitation using ECGs. These tools offer a cost-effective method for earlier diagnosis and improved patient outcomes.
Area of Science:
- Cardiology
- Artificial Intelligence in Medicine
- Medical Imaging Analysis
Background:
- Aortic Stenosis and Mitral Regurgitation are prevalent valvular heart conditions.
- Current diagnostic methods may not fully capture the disease burden.
- There is a need for advanced tools to aid clinical decision-making.
Purpose of the Study:
- To develop and validate deep learning (DL) models for screening and diagnosing Aortic Stenosis and Mitral Regurgitation.
- To leverage Electrocardiograms (ECGs) for valvular heart disease detection.
- To assess the clinical utility of DL tools in guiding patient management.
Main Methods:
- A multi-center retrospective cohort study utilizing Transthoracic Echocardiogram reports and ECGs from diverse patient populations.
- Development of a Natural Language Processing pipeline to extract ground-truth labels for valvular status.
- External validation of DL models for detecting Aortic Stenosis and Mitral Regurgitation.
Main Results:
- DL model for Mitral Regurgitation detection achieved an AUROC of 0.88 (internal) and 0.81 (external).
- DL model for Aortic Stenosis detection achieved an AUROC of 0.89 (internal) and 0.86 (external).
- Model performance improved leading up to diagnostic echocardiograms and showed promise for Transcatheter Aortic Valve Replacement (TAVR) evaluations.
Conclusions:
- Deep learning tools can extract valuable information from routine ECGs.
- These AI-driven tools are cost-effective and facilitate earlier disease detection.
- The application of DL in ECG analysis holds potential for improving patient prognosis in valvular heart disease.
Background:
Aortic Stenosis and Mitral Regurgitation are common valvular conditions representing a hidden burden of disease within the population. The aim of this study was to develop and validate deep learning-based screening and diagnostic tools that can help guide clinical decision making.
Methods:
In this multi-center retrospective cohort study, we acquired Transthoracic Echocardiogram reports from five Mount Sinai hospitals within New York City representing a demographically diverse cohort of patients. We developed a Natural Language Processing pipeline to extract ground-truth labels about valvular status and paired these to Electrocardiograms (ECGs). We developed and externally validated deep learning models capable of detecting valvular disease, in addition to considering scenarios of clinical deployment.
Results:
We use 617,338 ECGs paired to transthoracic echocardiograms from 123,096 patients to develop a deep learning model for detection of Mitral Regurgitation. Area Under Receiver Operating Characteristic curve (AUROC) is 0.88 (95% CI:0.88-0.89) in internal testing, and 0.81 (95% CI:0.80-0.82) in external validation. To develop a model for detection of Aortic Stenosis, we use 617,338 Echo-ECG pairs for 128,628 patients. AUROC is 0.89 (95% CI: 0.88-0.89) in internal testing, going to 0.86 (95% CI: 0.85-0.87) in external validation. The model's performance increases leading up to the time of the diagnostic echo, and it performs well in validation against requirement of Transcatheter Aortic Valve Replacement procedures.
Conclusions:
Deep learning based tools can increase the amount of information extracted from ubiquitous investigations such as the ECG. Such tools are inexpensive, can help in earlier disease detection, and potentially improve prognosis.
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