Automated identification of innocent Still's murmur using a convolutional neural network
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Raj Shekhar1,2, Ganesh Vanama2, Titus John1,2
1Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, United States.
Insights
A new algorithm accurately identifies Still's murmur, a common innocent childhood heart sound. This tool can help primary care providers reduce unnecessary referrals to pediatric cardiologists.
Area of Science:
- Pediatric Cardiology
- Biomedical Engineering
- Artificial Intelligence in Medicine
Background:
- Still's murmur is the most common innocent heart murmur in children.
- Primary care providers often misidentify Still's murmur, leading to unnecessary referrals.
- Accurate identification of innocent murmurs is crucial to avoid over-referral.
Purpose of the Study:
- To develop a computer algorithm for identifying Still's murmur at the point of care.
- To assist primary care providers in distinguishing innocent murmurs from pathological ones.
- To reduce the rate of over-referral to pediatric cardiology specialists.
Main Methods:
- A convolutional neural network-based algorithm was trained and tested.
- 1,473 pediatric patient heart sound recordings were analyzed.
- Recordings included Still's murmurs, pathological murmurs, other innocent murmurs, and normal heart sounds.
Main Results:
- The algorithm achieved 90.0% sensitivity and 98.3% specificity in identifying Still's murmur.
- Murmur sounds from the lower left sternal border yielded the highest accuracy.
- The area under the receiver operating characteristic curve was 0.943.
Conclusions:
- The developed algorithm accurately identifies Still's murmur.
- This AI tool can decrease unnecessary referrals to pediatric cardiologists.
- The algorithm may reduce the need for echocardiography in cases of benign findings.
Background:
Still's murmur is the most prevalent innocent heart murmur of childhood. Auscultation is the primary clinical tool to identify this murmur as innocent. Whereas pediatric cardiologists routinely perform this task, primary care providers are less successful in distinguishing Still's murmur from the murmurs of true heart disease. This results in a large number of children with a Still's murmur being referred to pediatric cardiologists.
Objectives:
To develop a computer algorithm that can aid primary care providers to identify the innocent Still's murmur at the point of care, to substantially decrease over-referral.
Methods:
The study included Still's murmurs, pathological murmurs, other innocent murmurs, and normal (i.e., non-murmur) heart sounds of 1,473 pediatric patients recorded using a commercial electronic stethoscope. The recordings with accompanying clinical diagnoses provided by a pediatric cardiologist were used to train and test the convolutional neural network-based algorithm.
Results:
A comparative analysis showed that the algorithm using only the murmur sounds recorded at the lower left sternal border achieved the highest accuracy. The developed algorithm identified Still's murmur with 90.0% sensitivity and 98.3% specificity for the default decision threshold. The area under the receiver operating characteristic curve was 0.943.
Conclusions:
Still's murmur can be identified with high accuracy with the algorithm we developed. Using this approach, the algorithm could help to reduce the rate of unnecessary pediatric cardiologist referrals and use of echocardiography for a common benign finding.
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