{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T18:08:28Z","timestamp":1785434908982,"version":"3.56.0"},"reference-count":32,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T00:00:00Z","timestamp":1780358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Health and Welfare","award":["RS-2024-00335935"],"award-info":[{"award-number":["RS-2024-00335935"]}]},{"award":["RS-2024-00335935"],"award-info":[{"award-number":["RS-2024-00335935"]}],"id":[{"id":"https:\/\/ror.org\/00vxgjw72","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medicina"],"abstract":"<jats:p>Background and Objectives: The diagnostic accuracy of an artificial intelligence (AI)-derived initial 12-lead electrocardiogram (ECG) analysis was evaluated for early carbon monoxide-induced cardiomyopathy (CO-CMP) risk detection. Materials and Methods: Retrospective medical data of carbon monoxide poisoning (COP) cases between 1 January 2015 and 31 December 2024 were screened for the primary outcome: odds ratio (OR) for echocardiographically confirmed CO-CMP among those with high-risk probability score per the AI-derived model. Secondary outcomes included left ventricular ejection fraction (LVEF) and AI-derived probability score, critical care requirements, including intubation and intensive care unit (ICU) admission, and cardiac arrest events. Results: A total of 51 patients with acute COP were included in the final analysis, with 13 (25.5%) being diagnosed with CO-CMP. The LVEF in the CO-CMP group was lower than that in the non-CO-CMP group (40.00 \u00b1 13.80% vs. 63.76 \u00b1 6.24%, p &lt; 0.001). The AI-derived probability score was higher in the CO-CMP group (11.3 [3.8\u201332.7] vs. 0.5 [0.2\u20132.2], p &lt; 0.001). Among cardiac biomarkers, troponin I (2.37 [0.32\u20137.88] vs. 0.06 [0.06\u20130.95] ng\/mL, p = 0.002) was higher in the CO-CMP group. Patients with CO-CMP required recurrent ventilator support (76.9% vs. 21.1%, p &lt; 0.001) and ICU admission (92.3% vs. 42.1%, p = 0.003). In multivariable regression analysis, the AI-derived prediction model was independently associated with CO-CMP (OR 1.14; 95% confidence interval (CI) 1.02\u20131.27; p = 0.017; Firth-penalized OR 1.11; 95% CI 1.03\u20131.25; p &lt; 0.001). Receiver operating characteristic analysis of the AI-derived model showed an area under the curve of 0.85 (95% CI 0.70\u20130.96) for the AI score alone and 0.92 (95% CI 0.83\u20130.99) for the Combined AI\u2013cardiac marker model, with a sensitivity of 92.3% and specificity of 81.6%. Pairwise DeLong comparisons between the Combined AI model and comparator models did not reach statistical significance (Combined vs. AI-only, p = 0.092; Combined vs. cardiac markers, p = 0.052); however, the likelihood-ratio test for adding the AI probability score to the cardiac marker-only model demonstrated significant incremental information (\u03c72 = 13.68, p &lt; 0.001). Conclusions: AI-based ECG analysis showed exploratory diagnostic association with LV systolic dysfunction observed in suspected CO-CMP patients. Given the limited sample size, low events-per-variable ratio, and lack of external validation, these findings suggest that AI-ECG analysis may provide incremental information for early cardiac risk stratification in selected patients.<\/jats:p>","DOI":"10.3390\/medicina62061081","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T10:14:57Z","timestamp":1780395297000},"page":"1081","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence-Derived Electrocardiogram Analysis for Identification of Carbon Monoxide-Induced Cardiomyopathy: A Retrospective Study"],"prefix":"10.3390","volume":"62","author":[{"given":"Heewon","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Emergency Medicine, Ajou University School of Medicine, Suwon 16499, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9865-7988","authenticated-orcid":false,"given":"Moon-Seung","family":"Soh","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Ajou University School of Medicine, Suwon 16499, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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 Hospital, Bucheon 14754, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sungwoo","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Soonchunhyang University College of Medicine, Seoul 04401, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9709-3332","authenticated-orcid":false,"given":"Sangsoo","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Soonchunhyang University College of Medicine, Seoul 04401, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6402-7259","authenticated-orcid":false,"given":"Sung-Eun","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Ajou University School of Medicine, Suwon 16499, Republic of Korea"},{"name":"Department of Neurology, Ajou University School of Medicine, Suwon 16499, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yura","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Ajou University School of Medicine, Suwon 16499, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2271-3434","authenticated-orcid":false,"given":"Sangchun","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Soonchunhyang University College of Medicine, Seoul 04401, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6911","DOI":"10.1007\/s11356-021-15895-y","article-title":"Global burden analysis and AutoGluon prediction of accidental carbon monoxide poisoning by Global Burden of Disease Study 2019","volume":"9","author":"Liu","year":"2022","journal-title":"Environ. 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