{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T07:03:09Z","timestamp":1789714989571,"version":"4.0.1"},"reference-count":47,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"vor","delay-in-days":259,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cardiology Research and Practice"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Transthyretin amyloid cardiomyopathy (ATTR\u2010CM) remains under\u2010recognised, and earlier identification is increasingly important with disease\u2010modifying therapy. Artificial intelligence (AI) applied to routine cardiovascular data may support screening, triage and diagnostic interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>MEDLINE, Embase, CINAHL, Web of Science and CENTRAL were searched from inception to August 2026 for studies evaluating AI\/machine learning for ATTR\u2010CM detection, screening or classification, including broader cardiac\u2010amyloidosis models with separately extractable ATTR\u2010specific performance and models detecting imaging phenotypes relevant to ATTR\u2010CM screening. Risk of bias was assessed using an adapted QUADAS\u20102 framework, with AI\u2010specific considerations informed by QUADAS\u2010AI development work; selected TRIPOD\u2009+\u2009AI and CLAIM domains were assessed descriptively. Owing to substantial clinical and methodological heterogeneity, meta\u2010analysis was not performed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Twenty\u2010eight primary reports were included, representing &gt;\u2009120,000 report\u2010level participant entries, although overlapping cohorts precluded estimation of a unique\u2010patient total. Performance varied by modality and task. AUCs ranged approximately 0.55\u20130.97 for ECG\u2010containing models, 0.72\u20131.00 for echocardiography\/POCUS and 0.70\u20130.85 for CT\u2010based single\u2010modality approaches; CMR differentiation of ATTR from AL achieved an AUC of 0.92. Nuclear\u2010imaging models often showed high discrimination, including external\u2010testing AUCs of 0.925\u20131.000, although several targeted tracer\u2010uptake phenotypes rather than definitive ATTR\u2010CM. Recurrent concerns included enriched populations, limited event counts, internal\u2010only testing, threshold optimisation and incomplete calibration reporting.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI shows promise for ATTR\u2010CM screening and automated interpretation, but reported discrimination should not be equated with real\u2010world clinical performance. Prospective multicentre validation, calibration at realistic prevalence, transparent reporting and workflow\u2010level evaluation are required before routine implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/crp\/3289574","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T06:27:36Z","timestamp":1789712856000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence for the Diagnosis of Transthyretin Amyloid Cardiomyopathy: A Systematic Review of Machine Learning Application"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1006-9824","authenticated-orcid":false,"given":"Eyad","family":"Jamileh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8257-1260","authenticated-orcid":false,"given":"Ahmed 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