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The assessment of aortic valve pathology using magnetic resonance imaging(MRI) typically relies on blood velocity estimates acquired using phasecontrast (PC) MRI. However, abnormalities in blood flow through the aorticvalve often manifest by the dephasing of blood signal in gated balancedsteady-state free precession (bSSFP) scans (Cine MRI). We propose a 3Dclassification neural network (NN) to automatically identify aortic valvepathology (aortic regurgitation, aortic stenosis, mixed valve disease) fromCine MR images. We train and test our approach on a retrospective clinicaldataset from three UK hospitals, using single-slice 3-chamber cine MRI from N =576 patients. Our classification model accurately predicts the presence ofaortic valve pathology (AVD) with an accuracy of 0.85 +/- 0.03 and can alsocorrectly discriminate the type of AVD pathology (accuracy: 0.75 +/- 0.03).Gradient-weighted class activation mapping (Grad-CAM) confirms that the bloodpool voxels close to the aortic root contribute the most to the classification.Our approach can be used to improve the diagnosis of AVD and optimise clinicalCMR protocols for accurate and efficient AVD detection.

Automatic Aortic Valve Pathology Detection from 3-Chamber Cine MRI with Spatio-Temporal Attention Maps