ArticleJACC. Advances2025
Automatic Diagnosis of Left Valvular Heart Disease Based on Artificial Intelligence Stethoscope.
Article in JACC. Advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Unimodal to multimodal: a systematic review of predictive machine learning models for valvular heart diseases.Frontiers in cardiovascular medicine · 2026Pooled it
- Responsibility Beyond the Black Box: A Medical Student's Reading of Giubilini's Paper "It Is Not About AI, It's About Humans".Journal of bioethical inquiry · 2026Article
- Mechanical-medical convergence in heart failure: Artificial intelligence, finite-element modeling, and 3D printing for diagnosis and prognosis.Bioengineering & translational medicine · 2026Review
- The Digital Evolution of the Medical Black Bag: Environmental Scan With Trend Analysis and Horizon Scanning.Journal of medical Internet research · 2026Article
- Evaluation of the DAMSUN-HF trial: the role of an artificial intelligence stethoscope in detecting reduced ejection fraction in patients living in a low-resource region.Heart failure reviews · 2026Review
- Artificial intelligence in early heart failure detection: from algorithms to bedside integration.Frontiers in cardiovascular medicine · 2026Article
- Feasibility of detecting aortic stenosis with mobile phone auscultation data: a pilot study.Frontiers in cardiovascular medicine · 2026Article
- Behavioral Intelligence in Action: Implementation Science and Cardiovascular Learning Health Systems.JACC. Advances · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundValvular heart disease (VHD) remains underdiagnosed and results in serious complications. Early screening for VHD facilitates enhanced clinical management.
objectivesThis study aim to develop an artificial intelligence-based stethoscope model for detecting left-sided VHD, including aortic stenosis, aortic regurgitation, mitral stenosis, and mitral regurgitation.
methodsUsing an electronic stethoscope, we recorded heart sounds from derivation group to construct a machine learning algorithm. Then, the algorithm was tested on a testing group. Echocardiography was referred as the gold standard. Model performance was assessed using area under the receiver-operating characteristic (AU-ROC).
resultsA total of 514 patients were included in the final analyses (304 in the algorithm training group and 210 in the result testing group). The diagnostic performance of machine learning model was as follows: aortic stenosis (AU-ROC: 0.7621), aortic regurgitation (AU-ROC: 0.7075), mitral stenosis (AU-ROC: 0.6426), mitral regurgitation (AU-ROC: 0.7906), and left-sided VHD (AU-ROC: 0.8541; sensitivity 83.07%, specificity 78.26%). When applied to the testing group, the sensitivity, specificity, and AU-ROC for identifying left-sided VHD were 70.00%, 73.68%, and 0.7554, respectively.
conclusionsArtificial intelligence-based stethoscope is capable of diagnosing left-sided VHD accurately and may make routine screening for VHD more practical.
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