{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T23:21:27Z","timestamp":1794352887674,"version":"4.9.0"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T00:00:00Z","timestamp":1700438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>Echocardiographic strain imaging reflects myocardial deformation and is a sensitive measure of cardiac function and wall-motion abnormalities. Deep learning (DL) algorithms could automate the interpretation of echocardiographic strain imaging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>We developed and trained an automated DL-based algorithm for left ventricular (LV) strain measurements in an internal dataset. Global longitudinal strain (GLS) was validated externally in (i) a real-world Taiwanese cohort of participants with and without heart failure (HF), (ii) a core-lab measured dataset from the multinational prevalence of microvascular dysfunction-HF and preserved ejection fraction (PROMIS-HFpEF) study, and regional strain in (iii) the HMC-QU-MI study of patients with suspected myocardial infarction. Outcomes included measures of agreement [bias, mean absolute difference (MAD), root-mean-squared-error (RMSE), and Pearson\u2019s correlation (R)] and area under the curve (AUC) to identify HF and regional wall-motion abnormalities. The DL workflow successfully analysed 3741 (89%) studies in the Taiwanese cohort, 176 (96%) in PROMIS-HFpEF, and 158 (98%) in HMC-QU-MI. Automated GLS showed good agreement with manual measurements (mean \u00b1 SD): \u221218.9 \u00b1 4.5% vs. \u221218.2 \u00b1 4.4%, respectively, bias 0.68 \u00b1 2.52%, MAD 2.0 \u00b1 1.67, RMSE = 2.61, R = 0.84 in the Taiwanese cohort; and \u221215.4 \u00b1 4.1% vs. \u221215.9 \u00b1 3.6%, respectively, bias \u22120.65 \u00b1 2.71%, MAD 2.19 \u00b1 1.71, RMSE = 2.78, R = 0.76 in PROMIS-HFpEF. In the Taiwanese cohort, automated GLS accurately identified patients with HF (AUC = 0.89 for total HF and AUC = 0.98 for HF with reduced ejection fraction). In HMC-QU-MI, automated regional strain identified regional wall-motion abnormalities with an average AUC = 0.80.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>DL algorithms can interpret echocardiographic strain images with similar accuracy as conventional measurements. These results highlight the potential of DL algorithms to democratize the use of cardiac strain measurements and reduce time-spent and costs for echo labs globally.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ehjdh\/ztad072","type":"journal-article","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T13:30:59Z","timestamp":1699968659000},"page":"60-68","source":"Crossref","is-referenced-by-count":57,"title":["External validation of a deep learning algorithm for automated echocardiographic strain measurements"],"prefix":"10.1093","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5871-1804","authenticated-orcid":false,"given":"Peder L","family":"Myhre","sequence":"first","affiliation":[{"name":"Division of Medicine, Akershus University Hospital , L\u00f8renskog , Norway"},{"name":"K.G. Jebsen Center of Cardiac Biomarkers, University of Oslo , Oslo , Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chung-Lieh","family":"Hung","sequence":"additional","affiliation":[{"name":"Division of Cardiology, Department of Internal Medicine, MacKay Memorial Hospital , Taipei , Taiwan"},{"name":"Institute of Biomedical Sciences, MacKay Medical College , New Taipei , Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew J","family":"Frost","sequence":"additional","affiliation":[{"name":"Us2.ai , Singapore , Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhubo","family":"Jiang","sequence":"additional","affiliation":[{"name":"Us2.ai , Singapore , Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wouter","family":"Ouwerkerk","sequence":"additional","affiliation":[{"name":"National Heart Centre Singapore , Singapore , Singapore"},{"name":"Department of Dermatology, Amsterdam Institute for Infection and Immunity, Cancer Centre Amsterdam , Amsterdam UMC, University of Amsterdam, Amsterdam , The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4217-9497","authenticated-orcid":false,"given":"Kanako","family":"Teramoto","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, National Cerebral and Cardiovascular Center , Osaka , Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Svedlund","sequence":"additional","affiliation":[{"name":"Department of Clinical Physiology, Institute of Medicine, Sahlgrenska University Hospital, University of Gothenburg , Gothenburg , Sweden"},{"name":"Ribocure Pharmaceuticals AB\/Ribo Life Science Co Ltd , Gothenburg , Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2488-0893","authenticated-orcid":false,"given":"Antti","family":"Saraste","sequence":"additional","affiliation":[{"name":"Heart Center, Turku University Hospital, University of Turku , Turku , Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Camilla","family":"Hage","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Heart, Vascular and Neuro Theme, Karolinska University Hospital , Stockholm , Sweden"},{"name":"Department of Medicine, Cardiology Unit, Karolinska Institutet , Stockholm , Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ru-San","family":"Tan","sequence":"additional","affiliation":[{"name":"National Heart Centre Singapore , Singapore , Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lauren","family":"Beussink-Nelson","sequence":"additional","affiliation":[{"name":"Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine , Chicago, IL , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria L","family":"Fermer","sequence":"additional","affiliation":[{"name":"Early Clinical Development, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca , Gothenburg , Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li-Ming","family":"Gan","sequence":"additional","affiliation":[{"name":"Ribocure Pharmaceuticals AB\/Ribo Life Science Co Ltd , Gothenburg , Sweden"},{"name":"Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy at the University of Gothenburg , Gothenburg , Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoran M","family":"Hummel","sequence":"additional","affiliation":[{"name":"Us2.ai , Singapore , 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