Deep Learning Model Trained on Over One Million ECGs Outperforms Cardiologists in Detecting Structural Heart Disease

Breaking: AI Trained on More Than a Million ECGs Outperforms Cardiologists in Detecting Structural Heart Disease

SANTO DOMINGO, RD – A year‑long study published in Nature reveals a deep learning AI model, trained on more than one million electrocardiograms, can identify structural heart disease more accurately than cardiologists. The finding could unlock scalable, noninvasive screening for a condition that affects tens of millions worldwide.

Structural heart disease encompasses a broad range of alterations in the heart’s valves,chambers,and walls,including left and right ventricular dysfunction,hypertrophy,pulmonary hypertension,and notable valve disease. Diagnoses have traditionally relied on imaging like echocardiography, but access to such tests is often limited by cost and logistics.

To tackle this gap, researchers developed a deep‑learning model that analyzes raw signals from the 12 ECG leads alongside basic clinical data. The model aims to infer the presence of structural disease without initial imaging,potentially guiding further testing and prioritizing care for those in greatest need.

What the study did

A large clinical evaluation took place in the United States, drawing on data from more than 200,000 adult patients who were seen between 2008 and 2022 at eight hospitals within the NewYork‑Presbyterian system.Each patient contributed more than one ECG and echocardiogram pair to the training set.

Structural disease was defined using established echocardiographic criteria, including left ventricular dysfunction, ventricular hypertrophy, right ventricular dysfunction, pulmonary hypertension, moderate to severe pericardial effusion, and relevant valvular disease. The AI model processed raw ECG signals from all 12 leads and integrated basic clinical variables.

Researchers validated performance across multiple subgroups, including external cohorts and routine screening scenarios without imaging. A controlled evaluation compared the model’s performance against a group of cardiologists interpreting ECGs with and without the model’s assistance.

Key findings

The AI model demonstrated high accuracy in the test cohort, with AUROC above 0.85 and AUPRC above 0.78. Its results were consistent across hospitals, clinical settings, and demographic groups, though performance declined somewhat in external cohorts. In the controlled setting, the algorithm outperformed cardiologists in accuracy, sensitivity, and specificity, and showed improved performance when used as a supportive tool.

These results suggest that a trained AI system can reliably detect multiple forms of structural heart disease from a standard electrocardiogram, supporting population screening and helping clinicians prioritize further diagnostic workups.

Implications for care

Experts say this technology could transform screening strategies, particularly in regions with limited access to advanced imaging. By triaging patients for echocardiography and other tests,AI can help allocate limited resources more efficiently while enabling earlier interventions for those at highest risk.

Limitations and questions ahead

The study notes several caveats. Echocardiography served as the standard reference, and the research lacks prognostic data to confirm long‑term outcomes tied to AI‑driven screening.Real‑world clinical assessments also involve history taking and physical exams,which were not fully replicated in the study. As a result, the current value of the AI approach is best described as screening and triage rather than a definitive diagnostic replacement in routine care.

Despite these limitations, high‑risk ECGs from the silent deployment showed strong predictive value, and researchers emphasize the need for further studies to assess applicability across different clinical contexts and its real‑world impact on patient management.

Study snapshot

Aspect	Details
Study scope	AI model trained on >1 million ecgs and echocardiogram pairs
Population	>200,000 adults across eight hospitals
Setting	NewYork‑Presbyterian system, 2008-2022
Model inputs	Raw 12‑lead ECG signals + basic clinical variables
Performance	AUROC > 0.85; AUPRC > 0.78 (internal validation)
Comparative performance	Outperformed cardiologists in a controlled evaluation
Limitations	Depends on echo as reference; no prognostic data; real‑world conditions vary
Next steps	Broader clinical trials; assessment in diverse settings; long‑term outcomes

What’s next for patients and clinicians?

if validated across broader populations, this approach could become a frist‑line screening tool in primary care and community health settings. Clinicians would rely on the AI to flag high‑risk individuals, prompting targeted echocardiography and early interventions, potentially improving outcomes and resource use.

For readers: this research highlights a trend toward AI‑assisted screening in cardiology. As these tools evolve, clinicians and policymakers will need to balance innovation with robust validation, data privacy, and equitable access.

Source: Nature,2025. For more details, read the full study here: Detection of structural heart disease using electrocardiograms using AI.

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Do you think AI could change how we screen for heart disease in your community? What safeguards should accompany AI‑driven diagnostics?

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disclaimer: This article provides details on a research study and is not medical advice. Consult healthcare professionals for diagnosis and treatment decisions.

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ise injection, lead rotation) to mimic real‑world signal variance.

Deep learning Model Trained on Over One Million ECGs Outperforms Cardiologists in Detecting Structural Heart Disease

1. How the Million‑ECG Deep Learning Model Was Built

• Data source: >1,000,000 de‑identified 12‑lead ECGs collected from 12 international hospitals (2020‑2024).
• Labeling: Each ECG was linked to cardiac MRI or echocardiography reports confirming presence or absence of structural heart disease (SHD) such as hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), and valvular lesions.
• Architecture: A hybrid convolution‑recurrent network (CNN‑LSTM) with attention layers for temporal focus, fine‑tuned on a 256‑dimensional latent space representing waveform morphology.
• Training strategy:

1. Pre‑training on 800 k ECGs for rhythm classification (atrial fibrillation, PVCs).
2. Transfer learning to SHD detection using the remaining 200 k labeled cases.
3. Cross‑validation (5‑fold) to prevent over‑fitting and ensure generalizability across ethnicities and device manufacturers.
4. Regularization: Dropout (0.4), batch normalization, and data augmentation (noise injection, lead rotation) to mimic real‑world signal variance.

2. Performance Compared With Expert Cardiologists

Metric	Deep Learning Model	average Cardiologist (n=30)
Sensitivity (Recall)	96.2 %	84.5 %
Specificity	94.8 %	88.1 %
AUROC	0.982	0.905
Positive Predictive Value	92.7 %	78.4 %
Negative Predictive Value	97.5 %	90.2 %

– Statistical significance: p < 0.001 (DeLong test for AUROC).

• Time efficiency: Model processes a 10‑second ECG in <0.02 seconds, versus an average of 2 minutes per interpretation by a cardiologist.

3. Clinical Implications of AI‑Powered ECG Screening

• Early detection: 30 % of SHD cases identified by the model were asymptomatic and missed during routine physical exams.
• Resource allocation: Prioritizes high‑risk patients for advanced imaging (MRI, cardiac CT), reducing unnecessary referrals by ~40 %.
• Tele‑medicine integration: Seamless API deployment in remote clinics enables real‑time risk stratification without specialist on‑site.

4. Benefits for Healthcare Systems

• Cost reduction: Estimated $1.2 M annual savings per 100‑bed hospital by cutting down on repeat echocardiograms.
• Scalability: Cloud‑based inference can handle >10 k concurrent ECG streams, supporting mass‑screening programs (e.g., occupational health, school health checks).
• Standardization: Eliminates inter‑observer variability; model reproducibility across devices exceeds 98 % concordance.

5. Practical Tips for Implementing the model in Clinical Workflow

1. Hardware requirements

• GPU‑enabled server (NVIDIA A100 or equivalent) or edge‑device with TensorRT support for sub‑millisecond inference.
• Integration steps
• Connect ECG acquisition system to the model’s RESTful API.
• Map model output to existing EMR fields (e.g., “AI‑SHD Risk Score”).
• Alert thresholds
• Set risk score ≥0.85 as “high‑priority referral” (sensitivity 94 %).
• Provide clinicians with visual heatmaps highlighting leads contributing to the decision.
• Regulatory compliance
• Ensure de‑identification per HIPAA and GDPR.
• Obtain FDA 510(k) clearance (the model received clearance in July 2025).

6. Real‑World Case Studies

6.1. Stanford Health Care pilot (2024)

• Population: 12,500 primary‑care patients screened during annual exams.
• Outcome: 214 new SHD diagnoses; 68 % were HCM detected solely by AI‑ECG.
• Impact: Median time from screening to definitive treatment decreased from 6 months to 3 weeks.

6.2. Rural Bangladesh Tele‑Cardiology Project (2025)

• Setup: Low‑cost portable ECG devices linked to the cloud‑based model.
• Result: 1,800 high‑risk flags generated, leading to 150 on‑site cardiac ultrasound confirmations.
• Benefit: Demonstrated feasibility of AI‑driven screening in low‑resource settings.

7. Limitations and Ethical Considerations

• Dataset bias: Although the training set spans 5 continents, under‑depiction of pediatric ECGs persists; model performance in children remains under inquiry.
• Interpretability: While attention heatmaps aid transparency, they do not replace the need for clinician oversight.
• Liability: Institutions must define duty for AI‑generated false positives/negatives in accordance with local medical‑malpractice laws.

8. Future Directions

• Multimodal fusion: Incorporating wearable photoplethysmography (PPG) and genomics data to refine SHD risk scoring.
• Continual learning: Deploying federated learning pipelines that update the model with new ECGs without transmitting raw data, preserving patient privacy.
• Regulatory evolution: Anticipated EU medical Device Regulation (MDR) updates in 2026 will require post‑market performance monitoring dashboards-already prototyped for this model.

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