{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T13:38:53Z","timestamp":1783172333118,"version":"3.54.6"},"reference-count":25,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T00:00:00Z","timestamp":1693440000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Cardiology"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Typical signs of heart failure (HF), like increased cardiothoracic ratio (CTR) and pleural effusion, can be seen on X\u2010ray. Artificial Intelligence (AI) can help in the early and quicker diagnosis of HF.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objectives<\/jats:title><jats:p>The study's goal was to demonstrate that the AI interpretation of chest X\u2010rays can assist the clinician in diagnosing HF.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Patients older than 45 years were included in the study. The study analyzed 10\u2009100 deidentified outpatient chest X\u2010rays by AI algorithm. The AI\u2010generated report was later verified by an independent radiologist. Patients with CTR\u2009&gt;\u20090.5 and pleural effusion were marked as potential HF. Flagged patients underwent confirmatory tests, and those labeled as negative also underwent further investigations to rule out HF.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Out of 10\u2009100, the AI algorithm detected 183 (1.8%) patients with increased CTR and pleural effusion on chest X\u2010rays. One hundred and six out of 183 underwent diagnostic tests. Eighty\u2010two (77%) out of 106 were diagnosed with HF according to current guidelines. From the remaining 9917 patients, 106 patients were randomly selected. Nine (8%) out of them were diagnosed with HF. The positive predictive value of AI for diagnosing HF is 77%, and the negative predictive value is 91%. More than half (54.9%) of newly diagnosed patients had HF with preserved ejection fraction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>HF is a risky condition with nonspecific symptoms that are difficult to diagnose, especially in the early stages. Using AI assistance for X\u2010ray interpretation can be helpful for early diagnosis of HF especially HF with preserved ejection fraction.<\/jats:p><\/jats:sec>","DOI":"10.1002\/clc.24105","type":"journal-article","created":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T06:07:49Z","timestamp":1693548469000},"page":"1562-1568","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["The diagnostic value of chest X\u2010ray scanning by the help of Artificial Intelligence in Heart Failure (ART\u2010IN\u2010HF)"],"prefix":"10.1002","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9417-7610","authenticated-orcid":false,"given":"Ahmet","family":"Celik","sequence":"first","affiliation":[{"name":"Department of Cardiology Mersin University Medical Faculty Mersin Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali 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