P203 Unsupervised one-class classification and anomaly detection of stress echocardiograms with deep denoising spatio-temporal autoencoders

Abstract

Abstract Introduction The combination of medical knowledge, experience and AI algorithms have supported the advancement of patient care and the lowering of healthcare costs. Machine and deep learning methods enable the extraction of meaningful patterns that remain beyond human perception. Numerous computer-aided diagnosis and detection systems have been developed to assist in the assessment of stress echocardiograms. However, issues are encountered when facing imbalanced, limited, and unannotated datasets. Learning from imbalanced medical datasets impairs diagnostic accuracy due to classifier bias and overfitting. Furthermore, datasets comprising of all existing abnormal classes are impossible to obtain, hence supervised algorithms would fail to generate predictions for classes devoid of training samples. Moreover, reliance on prior knowledge in the form of expert annotation and anatomical region extraction impairs scalability, as these procedures are time-consuming, computationally expensive, and limited to specific tasks. Purpose We aimed to perform one-class classification and anomaly detection of stress echocardiograms using unsupervised deep learning techniques to discriminate between normal and abnormal videos as well as to localise wall motion abnormalities within individual frames. Methods Deep denoising spatio-temporal autoencoder networks were employed to learn visual and motion representations from multiple echocardiographic cross-sections and stress stages. Extracted middle layer features were modelled by one-class support vector machines to discriminate between regular and irregular echocardiograms despite the absence of abnormal training samples. Reconstruction errors allowed for direct visualisation and localisation of anomalous cardiac regions, without the need for annotated training data or segmentation of structures. Results 2D B-mode stress echocardiograms acquired from 36 patients were classified as normal or abnormal based on patient reports and served as the ground truth. Results revealed that learnt features extracted from spatio-temporal autoencoders trained solely on normal data can be utilised to classify abnormal echocardiograms with a high level of accuracy, sensitivity and specificity. In addition to that, as validated by an expert reader, spatio-temporal autoencoder reconstruction errors were capable of detecting and localising wall motion abnormalities in specific cardiac regions without prior knowledge of abnormal segments. Conclusions The trained model enables the classification and detection of spatio-temporal abnormalities in stress echocardiograms. Therefore, the proposed networks have the potential of assisting in the global and regional assessment of stress echocardiograms.

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