{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T01:56:38Z","timestamp":1781661398708,"version":"3.54.5"},"reference-count":38,"publisher":"BMJ","issue":"3","license":[{"start":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T00:00:00Z","timestamp":1711670400000},"content-version":"unspecified","delay-in-days":28,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Huzhou Science and Technology Project","award":["2022GZ46"],"award-info":[{"award-number":["2022GZ46"]}]},{"name":"Cardiovascular Discipline Group Funds of Huzhou","award":["XKQ-HT-202102A"],"award-info":[{"award-number":["XKQ-HT-202102A"]}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2024,3,11]]},"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Mitral regurgitation (MR) is the most common valvular heart disorder, with a morbidity rate of 2.5%. While echocardiography is commonly used in assessing MR, it has many limitations, especially for large-scale MR screening. Cardiac auscultation with electronic stethoscope and artificial intelligence (AI) can be a fast and economical modality for assessing MR severity. Our objectives are (1) to establish a deep neural network (DNN)-based cardiac auscultation method for assessing the severity of MR; and (2) to quantitatively measure the performance of the developed AI-based MR assessment method by virtual clinical trial.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and analysis<\/jats:title>\n                    <jats:p>In a cross-sectional design, phonocardiogram will be recorded at the mitral valve auscultation area of outpatients. The enrolled patients will be checked by echocardiography to confirm the diagnosis of MR or no MR. Echocardiographic parameters will be used as gold standard to assess the severity of MR, classified into four levels: none, mild, moderate and severe. The study consists of two stages. First, an MR-related cardiac sound database will be created on which a DNN-based MR severity classifier will be trained. The automatic MR severity classifier will be integrated with the Smartho-D2 electronic stethoscope. Second, the performance of the developed smart device will be assessed in an independent clinical validation data set. Sensitivity, specificity, precision, accuracy and F1 score of the developed smart MR assessment device will be evaluated. Agreement on the performance of the smart device between cardiologist users and patient users will be inspected. The interpretability of the developed model will also be studied with statistical comparisons of occlusion map-guided variables among the four severity groups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Ethics and dissemination<\/jats:title>\n                    <jats:p>The study protocol was approved by the Medical Ethics Committee of Huzhou Central Hospital, China (registration number: 202302009-01). Informed consent is required from all participants. Dissemination will be through conference presentations and peer-reviewed journals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial registration number<\/jats:title>\n                    <jats:p>ChiCTR2300069496.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2023-074288","type":"journal-article","created":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T22:09:23Z","timestamp":1711750163000},"page":"e074288","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":5,"title":["Developing an AI-assisted digital auscultation tool for automatic assessment of the severity of mitral regurgitation: protocol for a cross-sectional, non-interventional study"],"prefix":"10.1136","volume":"14","author":[{"given":"Li","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Cardiology, Huzhou Central Hospital, The Fifth School of Clinical Medicine 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Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengsheng","family":"Cai","sequence":"additional","affiliation":[{"name":"Center for Intelligent Acoustics and Signal Processing, Huzhou Institute of Zhejiang University, Huzhou, Zhejiang, China"},{"name":"Suzhou Melodicare Medical Technology Co., Ltd, Suzhou, Jiangsu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianming","family":"Ma","sequence":"additional","affiliation":[{"name":"Administration Office, Huzhou Central Hospital, The Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, The Affiliated Central Hospital of Huzhou University, Huzhou, Zhejiang, 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