How can I classify a heart murmur using an AI model that analyzes the audio recording together with the patient’s age, sex, symptoms, and cardiac history?

AI-Based Classification of Heart Murmurs

Integrate the audio recording with patient age, sex, symptoms, and cardiac history into a multimodal AI model that analyzes phonocardiogram features alongside clinical variables to classify murmurs as innocent versus pathologic, then stratify pathologic murmurs by specific valvular lesions. 1

Core AI Architecture and Input Integration

Build your AI model using deep learning approaches—specifically convolutional neural networks (CNNs) or hybrid architectures—that accept both raw phonocardiogram waveforms and structured clinical metadata. 1, 2

• Audio preprocessing: Convert heart sound recordings into time-frequency representations (spectrograms or mel-frequency cepstral coefficients) that capture murmur timing, pitch, and intensity characteristics. 2, 3
• Clinical feature encoding: Include patient age, sex, presence of symptoms (syncope, angina, dyspnea, palpitations), and cardiac history (prior valve disease, heart failure, arrhythmias) as structured inputs to the model. 1, 2, 3
• Multimodal fusion: Combine audio-derived features with clinical variables at an intermediate layer of the neural network to allow the model to learn interactions between auscultatory findings and patient context. 1, 3

Classification Framework

Structure your AI output as a two-stage classifier: first distinguish innocent from pathologic murmurs, then subclassify pathologic murmurs by specific valvular lesions (aortic stenosis, mitral regurgitation, tricuspid regurgitation, etc.). 1, 4, 2

Stage 1: Innocent vs. Pathologic

• Train the model to recognize innocent murmur patterns: grade 1–2 intensity, systolic ejection configuration, normal S2 splitting, absence of radiation, and resolution with positional changes. 1, 4
• Flag pathologic indicators: any diastolic or continuous murmur, holosystolic or late systolic timing, grade ≥3 intensity, abnormal S2 splitting, or murmurs that increase with Valsalva or standing. 1, 4
• Incorporate symptom weighting: Symptomatic patients (syncope, angina, heart failure) should trigger pathologic classification even with lower-grade murmurs. 1, 4

Stage 2: Lesion-Specific Classification

• Aortic stenosis: Detect crescendo-decrescendo midsystolic murmur at right upper sternal border, radiation to carotids, parvus et tardus pulse (if available from clinical data), and association with elderly age or hypertension. 1, 5
• Mitral regurgitation: Identify holosystolic murmur at apex radiating to axilla, lack of intensity change after premature beats, and correlation with heart failure symptoms. 1, 5
• Tricuspid regurgitation and pulmonary regurgitation: Use right-sided murmur characteristics (increased intensity with inspiration) and clinical context (pulmonary hypertension, right heart failure). 1, 5
• Multiple valve involvement: Train the model to output probabilities for each valve lesion simultaneously, as AI-ECG studies show that positive findings for one valve often correlate with other valvular diseases. 5

Training Data Requirements and Validation

Use large, diverse, and well-labeled phonocardiogram datasets paired with echocardiographic gold standards to train and validate your model. 1, 3

• Dataset composition: Include recordings from multiple chest locations (apex, left sternal border, right upper sternal border, back) across diverse patient populations (pediatric, adult, elderly; varied sex, race, ethnicity) to minimize bias. 1, 3
• Gold standard labeling: Label each recording based on transthoracic echocardiography findings (valve morphology, regurgitation severity, stenosis gradients) performed within 7 days of the audio recording. 2, 3, 5
• Performance metrics: Report area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value; aim for AUC >0.85 for pathologic murmur detection and >0.80 for specific valve lesions. 1, 2, 3, 5
• External validation: Test the model on independent datasets from different clinical settings (community hospitals, rural clinics) to ensure generalizability. 3, 5

Clinical Integration and Decision Support

Design the AI output to provide a certainty score and actionable clinical recommendations based on the classification result. 1, 2

• High-certainty pathologic classification: Recommend immediate echocardiography or cardiology referral for any diastolic/continuous murmur, holosystolic murmur, grade ≥3 systolic murmur, or symptomatic patient. 4, 6
• Moderate-certainty findings: Flag for clinical review when the model detects borderline features (grade 2 murmur with abnormal S2, murmur in elderly hypertensive patient) or when clinical variables conflict with audio features. 1, 4
• Innocent classification: For asymptomatic patients with grade 1–2 midsystolic murmurs, normal S2, and no concerning clinical features, the AI can suggest no further workup is needed. 4, 7
• Pediatric-specific logic: In children with new murmurs during acute illness (fever, tachycardia, anemia), recommend re-examination after treating the underlying illness unless the murmur is diastolic, continuous, holosystolic, or grade ≥3. 6

Critical Pitfalls and Bias Mitigation

Proactively address algorithmic bias by ensuring training datasets include adequate representation of women, racial minorities, and diverse age groups, and validate performance across all subgroups. 1

• Sex and race bias: AI models trained on predominantly male or White populations perform poorly in women and minorities; require balanced datasets and report subgroup-specific performance metrics. 1
• Age-related challenges: Elderly patients with aortic sclerosis may have grade 1–2 murmurs that mimic innocent murmurs but require surveillance; the model must incorporate age and hypertension status to flag these cases. 1, 8
• Overfitting risk: Avoid training on datasets from a single institution or patient population; use external validation and "stress testing" with adversarial examples to ensure robustness. 1
• Automation bias: Clinicians may over-rely on AI predictions; design the interface to display the model's certainty score and key features driving the classification to support informed clinical judgment. 1

Practical Implementation Considerations

Deploy the AI model in settings where expert auscultation is limited—primary care clinics, rural health centers, school screenings—to maximize clinical impact. 1, 3

• Hardware integration: Pair the AI algorithm with digital stethoscopes or smartphone-based phonocardiogram devices to enable point-of-care screening. 1
• Workflow integration: Embed the AI output into the electronic health record with automated alerts for high-risk findings and suggested next steps (order echocardiography, refer to cardiology). 1
• Cost-effectiveness: Use the AI to triage patients for echocardiography, reducing unnecessary imaging in low-risk cases while ensuring timely diagnosis in high-risk cases. 3
• Regulatory approval: Ensure the AI model meets FDA or equivalent regulatory standards for clinical decision support software, including transparency in training data, validation performance, and intended use. 1