{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T10:37:25Z","timestamp":1792751845057,"version":"4.5.0"},"reference-count":33,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:00:00Z","timestamp":1792022400000},"content-version":"vor","delay-in-days":14,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001863","name":"New Energy and Industrial Technology Development Organization","doi-asserted-by":"publisher","award":["JPNP18004"],"award-info":[{"award-number":["JPNP18004"]}],"id":[{"id":"10.13039\/501100001863","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001863","name":"New Energy and Industrial Technology Development Organization","doi-asserted-by":"publisher","award":["JPNP23019"],"award-info":[{"award-number":["JPNP23019"]}],"id":[{"id":"10.13039\/501100001863","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Cardiology"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Valvular heart disease, particularly aortic stenosis (AS), often remains asymptomatic until advanced stages, making early detection challenging. Although cardiac auscultation is a traditional screening tool, its diagnostic accuracy is limited. We aimed to externally validate a deep learning\u2013based model (eAS model) using phonocardiogram (PCG) and single\u2010lead electrocardiogram (ECG) signals to detect moderate\u2010or\u2010greater AS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>PCG and ECG recordings were acquired using a PCG device (Cardio\u2010EGG). Transthoracic echocardiography served as the reference standard. The external validation cohort consisted of 951 patients from 3 medical institutions, including 70 with moderate\u2010or\u2010greater AS. The primary outcome was sensitivity for detecting moderate\u2010or\u2010greater AS; secondary outcomes included the area under the receiver operating characteristic curve (AUROC), specificity, predictive values, and likelihood ratios (LR).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The external validation cohort (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009951, median age 73 years, male 65%) exhibited more advanced valvular disease than the training\/development cohort. The eAS model achieved an AUROC of 0.9632 (95% CI: 0.9471\u20130.9774). Using the pre\u2010determined threshold, sensitivity was 92.9% and specificity 87.9%, yielding a positive LR of 7.65 and negative LR of 0.08. Model\u2010derived probabilities showed strong associations with echocardiographic measures of AS severity (adjusted\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      = 0.5051 for peak transvalvular velocity). Exploratory subgroup analyses showed generally consistent performance, although estimates were imprecise in small subgroups.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The eAS model demonstrated high diagnostic performance for detecting moderate\u2010or\u2010greater AS in a multicenter external validation cohort. AI\u2010based PCG\/ECG analysis may support the decision to refer patients from auscultation to echocardiography for AS evaluation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/clc.70498","type":"journal-article","created":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T10:02:37Z","timestamp":1792058557000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Diagnostic Performance of a Deep Learning Model Using Phonocardiogram and Single\u2010Lead ECG Signals for Detecting Moderate\u2010or\u2010Greater Aortic Stenosis: Multicenter External Validation"],"prefix":"10.1002","volume":"49","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5718-438X","authenticated-orcid":false,"given":"Shumpei","family":"Saito","sequence":"first","affiliation":[{"name":"AMI Inc Kagoshima 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