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Background <p>Transthoracic echocardiography is the first-line test for congenital heart disease (CHD), but accurate targeted triage and lesion subtyping require expertise and synthesis across multiple heterogeneous cine clips. We developed a video-based deep learning approach for examination-level targeted triage and subtype classification of common left-to-right shunt lesions.</p> Methods <p>We retrospectively assembled 2,373 echocardiography examinations (800 normal; 493 VSD, 751 ASD, and 329 PDA) from The First Affiliated Hospital of Xinjiang Medical University. Each examination comprised multiple cine clips acquired across standard views. We proposed Echocardiography Hierarchical Multiple-Instance Learning network (EchoHMIL), which encodes clips with a spatiotemporal backbone and aggregates variable numbers of clips using attention-based multi-instance learning to form an examination-level representation. A hierarchical dual-head design was optimized with a gated multi-task objective to perform: (i) normal-versus-target-shunt discrimination, and (ii) VSD/ASD/PDA subtype classification conditional on target-shunt status. Data were split at the patient level (70%/10%/20%) and evaluated on an independent test set using AUC, sensitivity, specificity, accuracy, and macro-F1, with bootstrap 95% confidence intervals. The task was explicitly framed as a closed-set targeted triage and subtype-classification problem for three common left-to-right shunt lesions, not as comprehensive pediatric CHD screening.</p> Results <p>On the test set (<i>n</i> = 475), EchoHMIL achieved an AUC of 0.957 for normal-versus-target-shunt discrimination. At a sensitivity-prioritized operating point, sensitivity was 92.2% and specificity was 82.4%. For VSD/ASD/PDA subtype classification among target-shunt cases, EchoHMIL achieved an overall accuracy of 88.8% with a macro-F1 of 0.885. Attention weights and gradient-based saliency maps highlighted clinically plausible regions associated with septal and ductal anatomy.</p> Conclusions <p>EchoHMIL enables automated examination-level triage and subtype classification of common left-to-right shunt lesions from routine echocardiography videos. Further validation on complex CHD and broader out-of-distribution abnormalities is required before extension to general CHD screening. These findings should therefore be interpreted within a closed-set target-lesion setting; prospective multicenter validation including complex, mixed, postoperative, and out-of-distribution abnormalities is required before any extension to general CHD screening or routine clinical deployment.</p>

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Hierarchical multi-instance learning for targeted triage and classification of common left-to-right shunt lesions from echocardiography videos

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月6日

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  • Peipei Zhang,
  • Yang Wu,
  • Xiaomei Hu,
  • Yuming Mu

摘要

Background

Transthoracic echocardiography is the first-line test for congenital heart disease (CHD), but accurate targeted triage and lesion subtyping require expertise and synthesis across multiple heterogeneous cine clips. We developed a video-based deep learning approach for examination-level targeted triage and subtype classification of common left-to-right shunt lesions.

Methods

We retrospectively assembled 2,373 echocardiography examinations (800 normal; 493 VSD, 751 ASD, and 329 PDA) from The First Affiliated Hospital of Xinjiang Medical University. Each examination comprised multiple cine clips acquired across standard views. We proposed Echocardiography Hierarchical Multiple-Instance Learning network (EchoHMIL), which encodes clips with a spatiotemporal backbone and aggregates variable numbers of clips using attention-based multi-instance learning to form an examination-level representation. A hierarchical dual-head design was optimized with a gated multi-task objective to perform: (i) normal-versus-target-shunt discrimination, and (ii) VSD/ASD/PDA subtype classification conditional on target-shunt status. Data were split at the patient level (70%/10%/20%) and evaluated on an independent test set using AUC, sensitivity, specificity, accuracy, and macro-F1, with bootstrap 95% confidence intervals. The task was explicitly framed as a closed-set targeted triage and subtype-classification problem for three common left-to-right shunt lesions, not as comprehensive pediatric CHD screening.

Results

On the test set (n = 475), EchoHMIL achieved an AUC of 0.957 for normal-versus-target-shunt discrimination. At a sensitivity-prioritized operating point, sensitivity was 92.2% and specificity was 82.4%. For VSD/ASD/PDA subtype classification among target-shunt cases, EchoHMIL achieved an overall accuracy of 88.8% with a macro-F1 of 0.885. Attention weights and gradient-based saliency maps highlighted clinically plausible regions associated with septal and ductal anatomy.

Conclusions

EchoHMIL enables automated examination-level triage and subtype classification of common left-to-right shunt lesions from routine echocardiography videos. Further validation on complex CHD and broader out-of-distribution abnormalities is required before extension to general CHD screening. These findings should therefore be interpreted within a closed-set target-lesion setting; prospective multicenter validation including complex, mixed, postoperative, and out-of-distribution abnormalities is required before any extension to general CHD screening or routine clinical deployment.