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AI model detects pediatric heart disease with 92% accuracy from stethoscope recordings

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method for detecting pediatric congenital heart disease (CHD) using phonocardiogram (PCG) recordings from digital stethoscopes. This approach integrates deep learning features with handcrafted ones to improve diagnostic accuracy, especially in low-resource settings where traditional echocardiography is not readily available. The model achieved high performance metrics, including 92% accuracy and a 96% AUROC, on data from 751 pediatric subjects in Bangladesh. This technology holds promise as a cost-effective screening tool for real-time remote detection of CHDs. AI

IMPACT Offers a potential low-cost, remote screening tool for congenital heart disease in underserved regions.

RANK_REASON This is a research paper detailing a new method for disease detection using machine learning.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI model detects pediatric heart disease with 92% accuracy from stethoscope recordings

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This is a research paper detailing a new method for disease detection using machine learning.
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146 days old
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Abdul Jabbar, Ethan Grooby, Yang Yi Poh, Khawza I. Ahmad, Md Hassanuzzaman, Raqibul Mostafa, Ahsan H. Khandoker, Faezeh Marzbanrad ·

    Automated detection of pediatric congenital heart disease from phonocardiograms using deep and handcrafted feature fusion

    arXiv:2604.24767v1 Announce Type: cross Abstract: Congenital heart disease (CHD) is the most common type of birth defect, impacting about 1% of live births worldwide. Echocardiography, the gold-standard diagnostic method, is costly and inaccessible in low-resource settings. Diagn…

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