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Meas."],"published-print":{"date-parts":[[2025,8,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    The potential of optical pumped magnetometer magnetocardiography (OPM-MCG) for diagnosing coronary artery disease (CAD) has been initially shown, yet lacks large-scale prospective research.\n                    <jats:italic>Approach.<\/jats:italic>\n                    Using invasive coronary angiography (ICA) as a reference, we constructed three feature sets for the development of machine learning (ML) models: a \u2018Heart\u2019 feature set consisting only of OPM-MCG features, a \u2018Clinical\u2019 feature set, and a \u2018Heart + Clinical\u2019 combined feature set. We assessed the performance of 11 ML models with 10-fold cross-validation and conducted a feature importance analysis.\n                    <jats:italic>Main results and Significance<\/jats:italic>\n                    . Among 1513 participants (mean age 58.2 \u00b1 12.0 years, 75.5% male), 1194 (78.92%) tested positive for ICA. Significant differences were observed in \u2018Heart\u2019 and \u2018Clinical\u2019 features between ICA-positive and negative groups. ML models using only \u2018Heart\u2019 features (AUC 0.84\u20130.88) outperformed those using only \u2018Clinical\u2019 features (AUC 0.62\u20130.75). Combining both feature types improved diagnostic accuracy (AUC 0.75\u20130.90). Feature importance analysis highlighted that \u2018Significant change of Ar-PN\u2019 in OPM-MCG was key for ICA diagnosis (47.8%), along with \u2018Abnormal Sp-TT\u2019, \u2018Significant change of Ps-PN\u2019, and \u2018Abnormal Mg-TT\u2019. OPM-MCG has high performance in diagnosing CAD, and the significant change of Ar-PN is the most important feature. Cat Boost and random forest are more suitable for OPM-MCG to build ML diagnostic models for CAD.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/adf0be","type":"journal-article","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T22:55:52Z","timestamp":1752706552000},"page":"085003","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Machine learning in diagnosing coronary artery disease via optical pumped magnetometer magnetocardiography: a prospective cohort study"],"prefix":"10.1088","volume":"46","author":[{"given":"Chenchen","family":"Tu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuwen","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linqi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhao","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lanxin","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjia","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0980-7014","authenticated-orcid":true,"given":"Xiantao","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,8,11]]},"reference":[{"key":"pmeaadf0bebib1","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2024.101746","article-title":"Development and validation of a machine learning model to predict myocardial blood flow and clinical outcomes from patients\u2019 electrocardiograms","volume":"5","author":"Alahdab","year":"2024","journal-title":"Cell Rep. 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Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2025-05-07","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-07-16","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-08-11","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}