  The assessment of aortic valve pathology using magnetic resonance imaging
(MRI) typically relies on blood velocity estimates acquired using phase
contrast (PC) MRI. However, abnormalities in blood flow through the aortic
valve often manifest by the dephasing of blood signal in gated balanced
steady-state free precession (bSSFP) scans (Cine MRI). We propose a 3D
classification neural network (NN) to automatically identify aortic valve
pathology (aortic regurgitation, aortic stenosis, mixed valve disease) from
Cine MR images. We train and test our approach on a retrospective clinical
dataset from three UK hospitals, using single-slice 3-chamber cine MRI from N =
576 patients. Our classification model accurately predicts the presence of
aortic valve pathology (AVD) with an accuracy of 0.85 +/- 0.03 and can also
correctly discriminate the type of AVD pathology (accuracy: 0.75 +/- 0.03).
Gradient-weighted class activation mapping (Grad-CAM) confirms that the blood
pool 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 clinical
CMR protocols for accurate and efficient AVD detection.
