{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T02:47:30Z","timestamp":1792464450958,"version":"4.5.0"},"reference-count":19,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"4","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Circ: Cardiovascular Imaging"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:sec>\n                    <jats:title>BACKGROUND:<\/jats:title>\n                    <jats:p>Diagnosing cardiac amyloidosis (CA) on echocardiography can be challenging due to the imaging overlap between CA and more prevalent causes of a hypertrophic phenotype. This study sought to (1) evaluate the performance of artificial-intelligence (AI) derived measurements incorporated into the established multiparametric echocardiographic scoring system to detect CA; (2) develop and validate an AI-based deep-learning model for video-based detection of CA on echocardiography.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS:<\/jats:title>\n                    <jats:p>The study population comprised 5776 patients (CA, 2756; controls, 3020). The training data set included patients from the UK National Amyloidosis Center and Taiwan MacKay Memorial Hospital (CA, 2241; controls, 2130). External test data sets were obtained from the US Duke University Health System (CA, 334; left ventricular hypertrophy controls, 668) and Japan National Cerebral and Cardiovascular Center (CA, 181; left ventricular hypertrophy controls, 222).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS:<\/jats:title>\n                    <jats:p>\n                      The multiparametric echocardiographic score computed using AI-derived measurements achieved an accuracy of 79.5% (sensitivity, 75.4%; specificity, 81.5%) in the United States cohort and 79.7% (sensitivity, 81.6%; specificity, 78.1%) in the Japan cohort. The deep-learning model demonstrated accuracies of 96.2% (sensitivity, 96.8%; specificity, 95.7%) and 95.8% (sensitivity, 97.3%; specificity, 94.3%) in the internal validation and internal test sets, respectively. External validation of the deep-learning model showed accuracies of 87.5% (sensitivity, 86.6%; specificity, 87.9%) in the United States and 88.4% (sensitivity, 92.3%; specificity, 85.3%) in the Japanese cohort. Subgroup analysis demonstrated that the deep-learning model showed robust discrimination of CA from other hypertrophic phenocopies: CA versus hypertension (area under the curve [AUC], 0.92 [95% CI, 0.91\u20130.94]), CA versus hypertrophic cardiomyopathy (AUC, 0.91 [95% CI, 0.87\u20130.94]), CA versus aortic stenosis (AUC, 0.93 [95% CI, 0.90\u20130.95]), CA versus chronic kidney disease (AUC, 0.93 [95% CI, 0.91\u20130.95]). The deep-learning model was able to classify a greater proportion of patients compared with the AI-derived multiparametric echocardiographic score and achieved superior diagnostic accuracy (AUC, 0.93 [95% CI, 0.91\u20130.95] versus AUC, 0.88 [95% CI, 0.85\u20130.90];\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt;0.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS:<\/jats:title>\n                    <jats:p>Both the multiparametric echocardiographic score computed from AI-derived measurements and the fully automated deep-learning model can accurately identify patients with CA in globally diverse cohorts, with the deep-learning model providing superior performance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/circimaging.125.018991","type":"journal-article","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T10:00:19Z","timestamp":1770890419000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":11,"title":["Diagnosis of Cardiac Amyloidosis on Echocardiography Using Artificial Intelligence"],"prefix":"10.1161","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5017-8657","authenticated-orcid":true,"given":"Adam","family":"Ioannou","sequence":"first","affiliation":[{"name":"National Amyloidosis Centre, University College London, Royal Free Campus, London, United Kingdom (A.I., J.M., A.S., L.V., Y.R., A.P., A.M.-N., M.U.R., H.L., P.N.H., A.W., J.D.G., M. Fontana)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6235-5176","authenticated-orcid":true,"given":"Michel G.","family":"Khouri","sequence":"additional","affiliation":[{"name":"Duke University Health System, Durham, NC (M.G.K.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2416-5865","authenticated-orcid":true,"given":"Takeshi","family":"Kitai","sequence":"additional","affiliation":[{"name":"Department of Heart Failure and Transplantation, National Cerebral and Cardiovascular Centre, Osaka, Japan (T.K.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1599-2957","authenticated-orcid":true,"given":"Sreekanth","family":"Vemulapalli","sequence":"additional","affiliation":[{"name":"Duke Clinical Research Institute, Durham, NC (S.V.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2858-3493","authenticated-orcid":true,"given":"Chung-Lieh","family":"Hung","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Sciences, Mackay Medical College, Taipei, Taiwan (C.-L.H.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8153-858X","authenticated-orcid":true,"given":"Sze Chi","family":"Lim","sequence":"additional","affiliation":[{"name":"Us2.ai, Singapore (S.C.L., M. Frost, W.W.T.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9760-4784","authenticated-orcid":true,"given":"Matthew","family":"Frost","sequence":"additional","affiliation":[{"name":"Us2.ai, Singapore (S.C.L., M. Frost, W.W.T.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6011-3741","authenticated-orcid":true,"given":"Weile Wayne","family":"Tee","sequence":"additional","affiliation":[{"name":"Us2.ai, Singapore (S.C.L., M. Frost, W.W.T.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1778-3713","authenticated-orcid":true,"given":"Josephine","family":"Mansell","sequence":"additional","affiliation":[{"name":"National Amyloidosis Centre, University College London, Royal Free Campus, London, United Kingdom (A.I., J.M., A.S., L.V., Y.R., A.P., A.M.-N., M.U.R., H.L., P.N.H., A.W., J.D.G., M. Fontana)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7707-226X","authenticated-orcid":true,"given":"Awais","family":"Sheikh","sequence":"additional","affiliation":[{"name":"National Amyloidosis Centre, University College London, Royal Free Campus, London, United Kingdom (A.I., J.M., A.S., L.V., Y.R., A.P., A.M.-N., M.U.R., H.L., P.N.H., A.W., J.D.G., M. Fontana)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2551-7403","authenticated-orcid":true,"given":"Lucia","family":"Venneri","sequence":"additional","affiliation":[{"name":"National Amyloidosis Centre, University College London, Royal Free Campus, London, United Kingdom (A.I., J.M., A.S., L.V., Y.R., A.P., A.M.-N., M.U.R., H.L., P.N.H., A.W., J.D.G., M. 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