Machine learning can accurately detect abnormal aortic valves in CMR

Abstract

Abstract Background Improving the efficiency of CMR by acquiring fewer, and more targeted sequences, would improve the diagnostic yield and reduce patient recalls. An AI-assisted clinical decision support system (CDSS) could deliver this efficiency using adaptive scanning protocols which replicate the expertise of highly trained clinicians. Normal aortic valve anatomy on the three-chamber (3CH) cine CMR is a guide to rationalising subsequent sequences, and therefore is a suitable base case for developing an AI-CDSS for CMR. Purpose We propose a machine learning approach to differentiate between normal and abnormal aortic valves from the 3CH cine. Methods We curated a unique expert-annotated dataset of 1221 frames from eighty CMR studies. For each frame, AV landmarks (two hinge points and two leaflets), and stenotic and regurgitant jets were labelled by three cardiologists. We then tested two AI models (Figure 1) to detect these AV abnormalities: A) a convolutional neural network (CNN), and B) a random forest approach. A) Using heat map regression, the AV was localised, and the jets (if present) were identified as pathological curves. We then tracked and quantified the curves in the estimated heatmaps based on their proximity, the length, orientation and angle with respect to the hinge points. B) We used a random forest approach to classify cases as normal or abnormal by using the characteristics of estimated pathological curves obtained from the heat map regression output. We trained and evaluated our models on an unseen dataset of 1017 CMR studies obtained from different scanner types across three NHS hospitals. Each CMR study report was manually assigned a binary ground truth label for a normal or abnormal AV. In total 496/1017 patients had an abnormal AV. Of those abnormal cases, 184 patients had aortic stenosis, 222 aortic regurgitation and 90 cases had mixed valve disease. We assessed the classification performance of our method with accuracy and an F1 score – a composite of precision and recall, where 1 is perfect; and heatmap regression performance for curves with mean absolute error. Results This machine learning approach classified abnormal aortic valves with good agreement to the ground truth labels with mean accuracy of 0.93 (representing approximately 451/496 patients) and mean F1 score of 0.91. The AV hinge points were localised with a mean distance error of 3.5 pixels. This was despite the small size of expert labelled data. Conclusion This machine learning solution successfully differentiated between normal and abnormal aortic valves from routine 3CH cine CMR views. More labelled datasets will enable further classification of pathology and severity, and greater accuracy. Our results represent an important stepping stone towards an AI-assisted CDSS for CMR. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): This work was supported by the UKRI CDT in AI for Healthcare http://ai4health.io