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March 26, 2026NEJM AIOpen Access

CardioNets improved cardiac phenotypes regression R² by 28.1% over models trained from scratch and achieved a 15.2% higher accuracy in cardiomyopathy screening compared to the average performance of human physicians using both ECG and real CMR.

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Why the study?

CMR provides gold-standard structural and functional insights but is limited by high cost and complexity, whereas ECG is accessible but lacks the granularity of CMR.

Does the CardioNets deep learning framework improve the accuracy of cardiovascular disease screening and cardiac phenotype estimation from 12-lead ECGs compared to baseline ECG models and human physicians?

Population

159,819 training samples from five cohorts and independent clinical validation datasets (n=3,767)

Comparison

CardioNets deep learning framework vs baseline models and human physicians

Design

Multicenter deep learning development and external validation study

Key result

CardioNets improved cardiac phenotypes regression R² by 28.1% over models trained from scratch and achieved a 15.2% higher accuracy in cardiomyopathy screening compared to the average performance of human physicians using both ECG and real CMR.

Authors

ZDZhengyao DingZLZiyu LiYHYujian Hu

Discussion

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Overview

Demonstrates the potential of AI-assisted multimodal screening to outperform standard clinical assessment; leaves open.

Structured PICO

Does the CardioNets deep learning framework improve the accuracy of cardiovascular disease screening and cardiac phenotype estimation from 12-lead ECGs compared to baseline ECG models and human physicians?

P
Population
Over 160,000 samples from datasets including the UK Biobank and MIMIC-IV-ECG, with external testing on 3,767 clinical samples, to evaluate an AI framework for cardiovascular disease screening.
I
Intervention
CardioNets, a deep learning framework utilizing cross-modal contrastive learning and generative pretraining to translate standard 12-lead ECG signals into CMR-level functional parameters and synthetic high-resolution CMR images.
C
Comparator
Baseline ECG models (trained from scratch, self-supervised learning, or phenotype-supervised), CMR-based reference models, and human physicians (in a reader study using both ECG and real CMR).
O
Outcome
Model performance for disease screening (AUC, accuracy, sensitivity, specificity) and cardiac phenotype estimation (R2, MAE, RMSE), as well as image generation quality (SSIM, PSNR).surrogate

CardioNets demonstrates that deep learning can accurately translate widely accessible 12-lead ECGs into CMR-level functional insights and synthetic images, offering a scalable, low-cost tool for cardiovascular screening.

Limitations

  • Real-world implementation and regulatory pathways for AI-generated imaging require further validation.
  • The generative model tends to regress toward the mean during training, amplifying multimodal clustering and less faithfully representing transitional or boundary regions in the phenotype space.
  • Heterogeneity in acquisition protocols and scanner settings between datasets may limit transferability of CMR reference models.
  • Controlled experimental setup may not fully reflect real-world clinical conditions with class imbalances
  • Requires future validation for real-world implementation and regulatory pathways

Cite This Study

Ding et al. (2026) studied Cardiovascular diseases (cardiomyopathy, coronary artery disease, heart failure, pulmonary hypertension) (n=163,586). CardioNets (deep learning framework) vs. Baseline ECG models (trained from scratch or self-supervised) and human physicians using ECG and real CMR was evaluated on Cardiomyopathy screening accuracy and cardiac phenotypes regression R². CardioNets improved cardiac phenotypes regression R² by 28.1% over models trained from scratch and achieved a 15.2% higher accuracy in cardiomyopathy screening compared to the average performance of human physicians using both ECG and real CMR.

synapsesocial.com/papers/6a8ddcc4a4bc1cf4cf7ab2f3https://doi.org/10.1056/aioa2500549
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