{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T17:12:30Z","timestamp":1783098750270,"version":"3.54.6"},"reference-count":36,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T00:00:00Z","timestamp":1667520000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: This study challenges state-of-the-art cardiac amyloidosis (CA) diagnostics by feeding multi-chamber strain and cardiac function into supervised machine (SVM) learning algorithms. Methods: Forty-three CA (32 males; 79 years (IQR 71; 85)), 20 patients with hypertrophic cardiomyopathy (HCM, 10 males; 63.9 years (\u00b17.4)) and 44 healthy controls (CTRL, 23 males; 56.3 years (IQR 52.5; 62.9)) received cardiovascular magnetic resonance imaging. Left atrial, right atrial and right ventricular strain parameters and cardiac function generated a 41-feature matrix for decision tree (DT), k-nearest neighbor (KNN), SVM linear and SVM radial basis function (RBF) kernel algorithm processing. A 10-feature principal component analysis (PCA) was conducted using SVM linear and RBF. Results: Forty-one features resulted in diagnostic accuracies of 87.9% (AUC = 0.960) for SVM linear, 90.9% (0.996; Precision = 94%; Sensitivity = 100%; F1-Score = 97%) using RBF kernel, 84.9% (0.970) for KNN, and 78.8% (0.787) for DT. The 10-feature PCA achieved 78.9% (0.962) via linear SVM and 81.8% (0.996) via RBF SVM. Explained variance presented bi-atrial longitudinal strain and left and right atrial ejection fraction as valuable CA predictors. Conclusion: SVM RBF kernel achieved competitive diagnostic accuracies under supervised conditions. Machine learning of multi-chamber cardiac strain and function may offer novel perspectives for non-contrast clinical decision-support systems in CA diagnostics.<\/jats:p>","DOI":"10.3390\/diagnostics12112693","type":"journal-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T06:21:56Z","timestamp":1667542916000},"page":"2693","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["A Machine Learning Challenge: Detection of Cardiac Amyloidosis Based on Bi-Atrial and Right Ventricular Strain and Cardiac Function"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5172-8080","authenticated-orcid":false,"given":"Jan","family":"Eckstein","sequence":"first","affiliation":[{"name":"Institute for Radiology, Nuclear Medicine and Molecular Imaging, Heart and Diabetes Center North-Rhine Westphalia, Ruhr-University of Bochum, 32545 Bad Oeynhausen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0475-8233","authenticated-orcid":false,"given":"Negin","family":"Moghadasi","sequence":"additional","affiliation":[{"name":"Department of Engineering Systems & Environment, University of Virginia, Charlottesville, VA 22904, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5425-8440","authenticated-orcid":false,"given":"Hermann","family":"K\u00f6rperich","sequence":"additional","affiliation":[{"name":"Institute for Radiology, Nuclear Medicine and Molecular Imaging, Heart and Diabetes Center North-Rhine Westphalia, Ruhr-University of Bochum, 32545 Bad Oeynhausen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elena","family":"Weise Vald\u00e9s","sequence":"additional","affiliation":[{"name":"Institute for Radiology, Nuclear Medicine and Molecular Imaging, Heart and Diabetes Center North-Rhine Westphalia, Ruhr-University of Bochum, 32545 Bad Oeynhausen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6303-2040","authenticated-orcid":false,"given":"Vanessa","family":"Sciacca","sequence":"additional","affiliation":[{"name":"Clinic for Electrophysiology, Heart and Diabetes Center North-Rhine Westphalia, Ruhr-University of Bochum, 32545 Bad Oeynhausen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9095-1277","authenticated-orcid":false,"given":"Lech","family":"Paluszkiewicz","sequence":"additional","affiliation":[{"name":"Clinic for Thoracic and Cardiovascular Surgery, Heart and Diabetes Center North-Rhine Westphalia, Ruhr-University of Bochum, 32545 Bad Oeynhausen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4995-7378","authenticated-orcid":false,"given":"Wolfgang","family":"Burchert","sequence":"additional","affiliation":[{"name":"Institute for Radiology, Nuclear Medicine and Molecular Imaging, Heart and Diabetes Center North-Rhine Westphalia, Ruhr-University of Bochum, 32545 Bad Oeynhausen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0539-0610","authenticated-orcid":false,"given":"Misagh","family":"Piran","sequence":"additional","affiliation":[{"name":"Institute for Radiology, Nuclear Medicine and Molecular Imaging, Heart and Diabetes Center North-Rhine Westphalia, Ruhr-University of Bochum, 32545 Bad Oeynhausen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1161\/CIRCULATIONAHA.108.843334","article-title":"Systemic cardiac amyloidoses: Disease profiles and clinical courses of the 3 main types","volume":"120","author":"Rapezzi","year":"2009","journal-title":"Circulation"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2047","DOI":"10.1161\/CIRCULATIONAHA.104.489187","article-title":"Diagnosis and Management of the Cardiac Amyloidoses","volume":"112","author":"Falk","year":"2005","journal-title":"Circulation"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Garc\u00eda, E., Gonz\u00e1lez-Romero, G.M., Mart\u00edn-P\u00e9rez, E.M., Cornejo, E.d.D.Z., Escobar-Aguilar, G., and Bonnet, M.F.C. 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