{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T13:07:57Z","timestamp":1790255277514,"version":"4.1.0"},"posted":{"date-parts":[[2026,9,21]]},"group-title":"Cardiovascular Medicine","reference-count":31,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.medrxiv.org\/about\/FAQ#license"}],"funder":[{"award":["R01AG08998"],"award-info":[{"award-number":["R01AG08998"]}],"id":[{"id":"https:\/\/ror.org\/001d55x84","id-type":"ROR","asserted-by":"publisher"}]},{"award":["R01HL167858"],"award-info":[{"award-number":["R01HL167858"]}],"id":[{"id":"https:\/\/ror.org\/001d55x84","id-type":"ROR","asserted-by":"publisher"}]},{"award":["K23HL153775"],"award-info":[{"award-number":["K23HL153775"]}],"id":[{"id":"https:\/\/ror.org\/001d55x84","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,9,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Cascade screening increasingly identifies carriers of pathogenic or likely pathogenic sarcomere variants at risk for hypertrophic cardiomyopathy (HCM) in whom penetrance is incomplete, and surveillance relies on resource-intensive serial imaging. We evaluated whether a validated artificial intelligence-enhanced electrocardiography (AI-ECG) model identifies the HCM phenotype at first clinical assessment, predicts development of HCM during follow-up, and complements polygenic risk.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We assembled 1,095 genotype-positive (G+) individuals with pathogenic or likely pathogenic sarcomere variants from Yale-New Haven Hospital (n=119), Erasmus MC (n=858), and Motol University Hospital (n=118). At baseline (first clinical assessment), individuals were classified as phenotype-positive (P+) or phenotype-negative (P\u2212). A previously validated AI-ECG model applied to 12-lead ECG images generated an HCM score. The primary outcome was detection of phenotypic positivity at baseline; secondary analyses included manifest HCM (at baseline or during follow-up) and identifying risk of developing future HCM among G+\/P\u2212individuals. In 57,007 UK Biobank participants, we assessed whether AI-ECG adds to an established polygenic risk score (PRS).<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Among 1,095 G+ individuals (median age 46 years [I\u01eaR 34\u201356]; 52.1% female), 808 (73.8%) were P+ at baseline, 56 (5.1%) developed HCM during follow-up, and 231 (21.1%) remained P\u2212. AI-ECG achieved an AUROC of 0.91 (95% CI 0.89\u20130.93) for P+ at baseline and 0.92 (95% CI 0.90\u20130.94) for manifest HCM. At a threshold of 0.15, sensitivity was 0.78, specificity 0.89, PPV 0.95, and NPV 0.59. Among G+\/P\u2212 individuals, higher AI-ECG scores predicted development of HCM (HR 1.55 per 1-SD; 95% CI 1.28\u20131.88; p&lt;0.001; adjusted HR 1.38; 95% CI 1.11\u20131.71; p=0.004). In the UK Biobank, individuals with both high AI-ECG and high PRS had 60-fold higher odds of HCM (adjusted OR 60.2; 95% CI 26.5\u2013137.2), versus 15.0 for high AI-ECG alone and 4.1 for high PRS alone.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>AI-ECG detects the HCM phenotype at baseline in sarcomere variant carriers, predicts development of HCM in G+\/P\u2212 individuals, and complements PRS in the general population, supporting AI-ECG as a scalable tool to detect HCM and guide surveillance in individuals with monogenic or polygenic susceptibility.<\/jats:p>\n                  <jats:sec>\n                    <jats:title>Clinical Perspective What Is New?<\/jats:title>\n                    <jats:list list-type=\"simple\">\n                      <jats:list-item>\n                        <jats:label>-<\/jats:label>\n                        <jats:p>In 1,095 carriers of pathogenic or likely pathogenic sarcomere variants across three international centers, a validated AI-ECG model applied to 12-lead ECG images detected the HCM phenotype at first clinical assessment (AUROC 0.91).<\/jats:p>\n                      <\/jats:list-item>\n                      <jats:list-item>\n                        <jats:label>-<\/jats:label>\n                        <jats:p>Among G+\/P\u2212 individuals, a higher AI-ECG score predicted development of HCM during follow-up (HR 1.55 per 1-SD increase), with consistent performance across sites, sexes, age groups, and sarcomere genes.<\/jats:p>\n                      <\/jats:list-item>\n                      <jats:list-item>\n                        <jats:label>-<\/jats:label>\n                        <jats:p>In 57,007 UK Biobank participants, AI-ECG and polygenic risk provided complementary information; individuals with both a high AI-ECG score and a high polygenic risk score had 60-fold higher odds of HCM.<\/jats:p>\n                      <\/jats:list-item>\n                    <\/jats:list>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>What Are the Clinical Implications?<\/jats:title>\n                    <jats:list list-type=\"simple\">\n                      <jats:list-item>\n                        <jats:label>-<\/jats:label>\n                        <jats:p>AI-ECG applied to standard ECG images is a scalable, low-cost tool to stratify the risk of HCM in carriers of a sarcomere variant.<\/jats:p>\n                      <\/jats:list-item>\n                      <jats:list-item>\n                        <jats:label>-<\/jats:label>\n                        <jats:p>The AI-ECG score may help individualize the intensity of imaging surveillance in G+\/P\u2212individuals, focusing resources on those at highest risk of developing HCM.<\/jats:p>\n                      <\/jats:list-item>\n                      <jats:list-item>\n                        <jats:label>-<\/jats:label>\n                        <jats:p>Combining AI-ECG with polygenic risk may refine identification of high-risk individuals in the general population and might further improve risk stratification.<\/jats:p>\n                      <\/jats:list-item>\n                    <\/jats:list>\n                  <\/jats:sec>\n                <\/jats:sec>","DOI":"10.64898\/2026.09.12.26362902","type":"posted-content","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T18:00:21Z","timestamp":1790013621000},"source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence-Enhanced Electrocardiography for Detection and Prediction of Hypertrophic Cardiomyopathy across Monogenic and Polygenic Susceptibility"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4490-8857","authenticated-orcid":false,"given":"Philip M.","family":"Croon","sequence":"first","affiliation":[{"name":"Section of Cardiovascular Medicine, Department of Internal 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