Echocardiography-based intelligent diagnosis and risk stratification management for tetralogy of Fallot

Early and accurate detection of Tetralogy of Fallot (TOF), along with proper risk stratification management, is critical for improving patient survival and prognosis. We developed an end-to-end automated framework for TOF, aimed at supporting decisions from preoperative diagnosis through postoperative risk prediction.

A total of 1986 filtered participants (1018 healthy controls, 480 TOF mimics, and 488 patients with TOF) from four centres were recruited for the development and validation of DynaTOF, an integrated diagnostic and predictive system. The DynaTOF system comprises: (1) an echocardiographic view classification module built on ResNet-18; (2) a key diameter localisation and calculation module constructed with HRNet and a custom composite loss combining heatmap loss with geometric constraint loss; (3) a multimodal TOF diagnostic module that integrates a ResNet-LSTM-based video feature extractor for echocardiographic videos and a Transformer-based feature extractor for key diameters; (4) a time-aware postoperative prediction module, implemented with a GNN (Graph Neural Network), which estimates postoperative abnormal score dynamics based on preoperative video data, key diameters, surgical type, and specific postoperative time; and (5) a risk-stratification module that employs a Random Forest classifier to differentiate between high- and low-risk patients using the predicted abnormal score series.

The view classification module achieved AUC values of 0.999, 0.999, 0.998, and 0.998 for classifying the Apical Four-Chamber (A4C), Apical Five-Chamber (A5C), Parasternal Short-Axis (PSAX), and Parasternal Long-Axis (PLAX) views, respectively. The key diameter localisation and calculation module demonstrated R² values of 0.98, 0.76, and 0.97 for the prediction of LVD (left ventricular diameter), RVD (right ventricular diameter), and MPAD (main pulmonary artery diameter). The multimodal diagnostic module exhibited excellent performance in identifying TOF, with an accuracy of 0.910 (95% CI 0.881-0.938), an AUC of 0.989 (95% CI 0.977-0.992), a precision of 0.893 (95% CI 0.860-0.927), and a recall of 0.892 (95% CI 0.856-0.927), surpassing all single-modality approaches. The time-aware prediction module showed a high correlation (R² = 0.852) between predicted and observed postoperative abnormal scores. Finally, the risk stratification module achieved an AUC of 0.904 for distinguishing between high-risk and low-risk patients.

DynaTOF enables efficient diagnosis of TOF and provides personalised abnormal dynamics after operation, facilitating early screening and longitudinal monitoring. This system holds promise for improving comprehensive clinical care for infants with TOF.

This work was supported by Shanghai Municipal Education Commission (No. 2024AIYB010), Fundamental Research Funds for the Central Universities (YG2025LC03), Shanghai Special Fund for Promoting High-Quality Industrial Development - Pilot Industry Innovation Development (AI Special Topic) Project (No. 2025-GZL-RGZN-02078), National Key Research and Development Program of China (2025YFC2511603), Shenzhen Medical Research Special Project clinical multi-center study (No. C2405001), the Science and Technology Commission of Shanghai Municipality (STCSM) (Grant No. 23JS1400700; 24JS2840200; 25JS2850100), the Innovative Research Team of High-Level Local Universities in Shanghai, and the Sanya Science and Technology Special Fund (No. 2022KJCX41).