{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T10:09:44Z","timestamp":1788862184017,"version":"build-2803163510"},"reference-count":31,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Artificial intelligence-enabled electrocardiography (AI-ECG) can infer left ventricular systolic dysfunction (LVSD) and diastolic dysfunction (LVDD) from a standard 12-lead tracing. Whether these structural AI-ECG scores predict incident atrial fibrillation (AF) in patients in sinus rhythm, and how their predictive value changes over time, is unclear. Methods: In a retrospective single-center cohort of patients with a sinus-rhythm index ECG, AI-ECG LVSD and LVDD scores were derived and dichotomized at prespecified cutoffs (LVSD \u2265 9.7; LVDD \u2265 20.8). The outcome was incident AF, assessed within 30, 90, 180, 270, and 365 days. Discrimination was quantified by the AUROC. Because the proportional hazards assumption was violated, the time course of risk for the joint LVSD \u00d7 LVDD classification was characterized with window-specific Cox, Aalen additive hazards, and restricted mean survival time analyses. Results: Among 19,593 index ECGs from 18,984 patients, 1190 (6.07%) were followed by incident AF within one year. One-year incidence rose from 4.1% (both negative) to 9.8% (LVSD-positive only), 20.1% (LVDD-positive only), and 19.9% (both positive). Discrimination was highest for early events and attenuated over time, more steeply for LVSD (AUROC 0.803 at 30 days to 0.713 at 365 days) than LVDD (0.817 to 0.764). Combining AI-ECG LVSD and LVDD models, the excess risk conferred by dual positivity was concentrated in the first weeks after the index ECG and, consistently across cumulative Cox, Aalen additive hazards, and restricted mean survival time analyses, converged with that of isolated LVDD positivity by one year. Conclusions: Structural AI-ECG LVSD and LVDD scores from a single sinus-rhythm ECG predict incident AF in a time-dependent manner, with the strongest performance shortly after acquisition and more durable discrimination for LVDD. A single AI-ECG may help target short-term AF surveillance, particularly in patients with combined systolic\u2013diastolic dysfunction.<\/jats:p>","DOI":"10.3390\/diagnostics16182887","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T09:02:24Z","timestamp":1788858144000},"page":"2887","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence-Enabled Electrocardiography Model for Left Ventricular Systolic and Diastolic Dysfunction Predict Incident Atrial Fibrillation in Patients with Sinus Rhythm: A Time-Dependent Analysis"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3897-0278","authenticated-orcid":false,"given":"Kyung Su","family":"Kim","sequence":"first","affiliation":[{"name":"Medical AI Co., Ltd., Seoul 06180, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"given":"Jeong Min","family":"Son","sequence":"additional","affiliation":[{"name":"Medical AI Co., Ltd., Seoul 06180, Republic of Korea"},{"name":"Digital Healthcare Institute, Sejong Medical Research Institute, Bucheon 14754, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2200-6601","authenticated-orcid":false,"given":"Hak Seung","family":"Lee","sequence":"additional","affiliation":[{"name":"Medical AI Co., Ltd., Seoul 06180, Republic of Korea"},{"name":"Digital Healthcare Institute, Sejong Medical Research Institute, Bucheon 14754, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Soobeen","family":"Seol","sequence":"additional","affiliation":[{"name":"Medical AI Co., Ltd., Seoul 06180, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1137-2593","authenticated-orcid":false,"given":"Sora","family":"Kang","sequence":"additional","affiliation":[{"name":"Medical AI Co., Ltd., Seoul 06180, Republic of Korea"},{"name":"Digital Healthcare Institute, Sejong Medical Research Institute, Bucheon 14754, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Joon-myoung","family":"Kwon","sequence":"additional","affiliation":[{"name":"Medical AI Co., Ltd., Seoul 06180, Republic of Korea"},{"name":"Digital Healthcare Institute, Sejong Medical Research Institute, Bucheon 14754, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9247-2432","authenticated-orcid":false,"given":"Min Sung","family":"Lee","sequence":"additional","affiliation":[{"name":"Medical AI Co., Ltd., Seoul 06180, Republic of Korea"},{"name":"Digital Healthcare Institute, Sejong Medical Research Institute, Bucheon 14754, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0708-8685","authenticated-orcid":false,"given":"Kyung-Hee","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Cardiology, Department of Internal Medicine, Incheon Sejong Hospital, Cardiovascular Center, Incheon 21080, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1161\/CIR.0000000000001193","article-title":"2023 ACC\/AHA\/ACCP\/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology\/American Heart Association Joint Committee on 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