ArticleIEEE transactions on bio-medical engineering2026
Advancing Point-of-Care Still's Murmur Identification: Evaluating the Efficacy of ConvNets and Transformers Using the StethAid Multicenter Heart Sound Database.
Article in IEEE transactions on bio-medical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Pediatric murmur auscultation and tele-consultation: importance of signal quality and classification.Pediatric research · 2026Article
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8 authors.
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Abstract
backgroundPrimary care providers (PCPs) are not successful in accurately identifying Still's murmur with no available clinical tools to aid in the process. Existing deep learning (DL) methods primaryly focused on adult pathological murmurs or murmur detection, lacking dedicated approaches for Still's murmur. Furthermore, the absence of a specialized database hampers the development and validation of AI models for pediatric populations.
objectiveTo collect a pediatric dataset and evaluate DL methods in identifying Still's murmur in pediatric patients from phonocardiograms (PCGs) acquired using the StethAid digital auscultation platform.
methodsWe collected a large pediatric dataset (>2 hours of heart sound data) from four pediatric medical centers using StethAid, in addition to our previous Littmann 4100 dataset, which consisted of 1450 heart sound recordings. The StethAid dataset comprises 527 PCGs with diagnoses from four expert pediatric cardiologists. This dataset was used to train convolution- and pure attention-based models, which are our designed architecture SQ-NET, ResNet18, audio spectrogram transformer (AST), data-efficient transformer (DeiT), shifted windows (Swin) transformer, self-distillation with no labels (DINO). These methods were trained on segmented heart sounds except AST.
resultsDL methods achieved a sensitivity of 90.7%-100%, specificity of 75%-98.2%, and accuracy of 91.3%-98.5%. These results reflect post-hoc comparisons and highlight the potential of DL models to distinguish Still's murmur from other innocent and pathological murmurs. Pretraining on domain specific Littmann 4100 dataset improved models' performance by at least 2% over training from scratch.
conclusionBoth ConvNets and transformers enjoyed high performance in Still's murmur identification. SIGNIFICANCE: The proposed Still's murmur solution can assist PCPs in recognizing Still's murmur from other heart murmurs, potentially reducing unnecessary specialty referrals and the use of echocardiography. Additionally, StethAid could enhance PCPs' auscultation skills for Still's murmur by providing instant feedback, a feature often lacking in current clinical workflows.
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