Vimalesvaran, KavithaUslu, FatmatulzehraZaman, SameerGalazis, ChristoforosHoward, JamesCole, GrahamBharath, Anil A.2026-02-122026-02-122022978-3-031-16431-6978-3-031-16430-90302-97431611-3349https://doi.org/10.1007/978-3-031-16431-6_54https://hdl.handle.net/20.500.12885/685525th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) -- SEP 18-22, 2022 -- Singapore, SINGAPORECardiac magnetic resonance (CMR) is the gold standard for quantification of cardiac volumes, function, and blood flow. Tailored MR pulse sequences define the contrast mechanisms, acquisition geometry and timing which can be applied during CMR to achieve unique tissue characterisation. It is impractical for each patient to have every possible acquisition option. We target the aortic valve in the three-chamber (3-CH) cine CMR view. Two major types of anomalies are possible in the aortic valve. Stenosis: the narrowing of the valve which prevents an adequate outflow of blood, and insufficiency (regurgitation): the inability to stop the back-flow of blood into the left ventricle. We develop and evaluate a deep learning system to accurately classify aortic valve abnormalities to enable further directed imaging for patients who require it. Inspired by low level image processing tasks, we propose a multi-level network that generates heat maps to locate the aortic valve leaflets' hinge points and aortic stenosis or regurgitation jets. We trained and evaluated all our models on a dataset of clinical CMR studies obtained from three NHS hospitals (n = 1,017 patients). Our results (mean accuracy = 0.93 and F1 score = 0.91), show that an expert-guided deep learning-based feature extraction and a classification model provide a feasible strategy for prescribing further, directed imaging, thus improving the efficiency and utility of CMR scanning.eninfo:eu-repo/semantics/closedAccessCardiac MRIAortic valveExplainabilityMachine learningConference Object10.1007/978-3-031-16431-6_5413431571580WOS:0008675243000542-s2.0-85138834454N/AQ3