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Artificial intelligence-assisted auscultation in detecting congenital heart disease.

Authors :
Lv J
Dong B
Lei H
Shi G
Wang H
Zhu F
Wen C
Zhang Q
Fu L
Gu X
Yuan J
Guan Y
Xia Y
Zhao L
Chen H
Source :
European heart journal. Digital health [Eur Heart J Digit Health] 2021 Jan 06; Vol. 2 (1), pp. 119-124. Date of Electronic Publication: 2021 Jan 06 (Print Publication: 2021).
Publication Year :
2021

Abstract

Aims: Computer-assisted auscultation has become available to assist clinicians with physical examinations to detect congenital heart disease (CHD). However, its accuracy and effectiveness remain to be evaluated. This study seeks to evaluate the accuracy of auscultations of abnormal heart sounds of an artificial intelligence-assisted auscultation (AI-AA) platform we create.<br />Methods and Results: Initially, 1397 patients with CHD were enrolled in the study. The samples of their heart sounds were recorded and uploaded to the platform using a digital stethoscope. By the platform, both remote auscultation by a team of experienced cardiologists from Shanghai Children's Medical Center and automatic auscultation of the heart sound samples were conducted. Samples of 35 patients were deemed unsuitable for the analysis; therefore, the remaining samples from 1362 patients (mean age-2.4 ± 3.1 years and 46% female) were analysed. Sensitivity, specificity, and accuracy were calculated for remote auscultation compared to experts' face-to-face auscultation and for artificial intelligence automatic auscultation compared to experts' face-to-face auscultation. Kappa coefficients were measured. Compared to face-to-face auscultation, remote auscultation detected abnormal heart sound with 98% sensitivity, 91% specificity, 97% accuracy, and kappa coefficient 0.87. AI-AA demonstrated 97% sensitivity, 89% specificity, 96% accuracy, and kappa coefficient 0.84.<br />Conclusions: The remote auscultations and automatic auscultations, using the AI-AA platform, reported high auscultation accuracy in detecting abnormal heart sound and showed excellent concordance to experts' face-to-face auscultation. Hence, the platform may provide a feasible way to screen and detect CHD.<br /> (© The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Details

Language :
English
ISSN :
2634-3916
Volume :
2
Issue :
1
Database :
MEDLINE
Journal :
European heart journal. Digital health
Publication Type :
Academic Journal
Accession number :
36711176
Full Text :
https://doi.org/10.1093/ehjdh/ztaa017