Multiple instance learning framework can facilitate explainability in murmur detection.

Area of Science:

• Cardiology
• Biomedical Engineering
• Machine Learning

Background:

• Cardiovascular diseases (CVDs) are a leading cause of global mortality.
• Heart murmurs detected via phonocardiograms (PCGs) can indicate CVDs but often require expert interpretation.
• Current methods may overlook subtle murmur indicators or lack explainability.

Purpose of the Study:

• To develop an explainable multitask model for predicting heart murmurs and clinical outcomes from multiple PCG recordings.
• To leverage multiple instance learning (MIL) for improved murmur detection and localization.
• To integrate explainable features for enhanced clinical decision support.

Main Methods:

• A two-stage multitask model incorporating MIL in the first stage for murmur detection in single PCGs.
• Fusion of explainable hand-crafted features with features from a pooling-based artificial neural network (PANN) in the second stage.
• Prediction of patient-specific murmur presence and clinical outcome using multiple PCG recordings via a feed-forward neural network.

Main Results:

• The MIL approach effectively identifies murmur locations and provides useful features for PCG analysis.
• The PANN model achieved a weighted accuracy of 0.714 on the CirCor dataset.
• The model demonstrated competitive classification performance for murmur detection and clinical outcome prediction.

Conclusions:

• This work is the first to demonstrate the utility of MIL for phonocardiogram classification.
• The study highlights a method for quantitative analysis of model explainability, mitigating confirmation bias.
• The findings underscore the value of combining MIL with handcrafted features for explainable AI in cardiovascular diagnostics.