Deep‐Learning‐Based Disease Classification in Patients Undergoing Cine Cardiac MRI

• Published in: Journal of magnetic resonance imaging Vol. 61; no. 4; pp. 1635 - 1647
• Main Authors: Jacob, Athira J., Chitiboi, Teodora, Schoepf, U. Joseph, Sharma, Puneet, Aldinger, Jonathan, Baker, Charles, Lautenschlager, Carla, Emrich, Tilman, Varga‐Szemes, Akos
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.04.2025; Wiley Subscription Services, Inc
• Subjects: Accuracy; Adult; Aged; Algorithms; Cardiomyopathy; Cardiomyopathy, Dilated - diagnostic imaging; Cardiomyopathy, Hypertrophic - diagnostic imaging; Cardiovascular diseases; Classification; Confidence intervals; Deep Learning; Dilated cardiomyopathy; Disease; disease classification; Feature extraction; Female; Field strength; Heart - diagnostic imaging; Heart diseases; Heart Ventricles - diagnostic imaging; Humans; Image Interpretation, Computer-Assisted - methods; Image Processing, Computer-Assisted - methods; Ischemia; Labels; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging, Cine - methods; Male; Middle Aged; model interpretability; Myocardial Ischemia - diagnostic imaging; myocardial strain; Population studies; Reproducibility of Results; Retrospective Studies; Sensitivity and Specificity; Statistical analysis; Statistical tests; Ventricle; deep learning; myocardial strain; model interpretability; disease classification

Background Automated approaches may allow for fast, reproducible clinical assessment of cardiovascular diseases from MRI. Purpose To develop an MRI‐based deep learning (DL) disease classification algorithm to distinguish among normal subjects (NORM), patients with dilated cardiomyopathy (DCM), hyper...