MMMuLNet: Multimodal Mutual Deep learning Framework for the Pediatric Congenital Heart Disease Detection

Area of Science:

• Biomedical Engineering
• Cardiology
• Artificial Intelligence in Medicine

Background:

• Congenital heart disease (CHD) is a leading cause of infant mortality globally.
• Echocardiography, the gold standard for CHD diagnosis, is limited in low- and middle-income countries (LMICs) due to cost and expertise requirements.
• There is a critical need for accessible, reliable diagnostic tools for pediatric CHD in resource-limited environments.

Purpose of the Study:

• To develop and validate a multimodal framework for accurate pediatric CHD detection.
• To integrate phonocardiograms (PCG), electrocardiograms (ECG), and clinical symptoms for enhanced diagnostic performance.
• To ensure the framework's robustness under real-world conditions, including degraded signal quality.

Main Methods:

• A multimodal framework combining PCG, ECG, and clinical symptoms was developed.
• Features were extracted from PCG and ECG using a pretrained audio foundation model; symptoms were selected using SHAP.
• A fusion network with modality dropout and mutual learning was employed for robust integration and handling of missing data.

Main Results:

• The full multimodal system achieved 94.5% accuracy, 94.2% sensitivity, 95.6% specificity, and 96% AUROC.
• Individual modalities showed PCG alone (90% accuracy), ECG alone (79%), and symptoms alone (73%).
• The multimodal framework maintained 88% accuracy and 87% AUROC even with low-quality PCG and ECG signals, outperforming unimodal approaches.

Conclusions:

• Integrating PCG, ECG, and clinical symptoms provides an accurate and resilient method for pediatric CHD screening.
• This multimodal approach offers a practical and scalable solution for early CHD detection in LMICs.
• The framework demonstrates significant potential for improving diagnostic accessibility and patient outcomes in underserved regions.