Contrast CT classification of asymptomatic and symptomatic carotids in stroke and transient ischaemic attack with deep learning and interpretability

Abstract

Background Convolutional neural networks (CNNs), part of deep learning, are used widely for computer vision tasks and in some medical domains, such as mammography interpretation. The application of deep learning to carotid artery imaging is scarce. We investigated the ability of deep learning to correctly classify contrast CT images of the carotid arteries without the need for prior feature selection. Purpose (1) To assess the ability of deep learning to differentiate symptomatic patients (had prior stroke or transient ischaemic attack [TIA]) from asymptomatic patients (no prior stroke/TIA) using contrast CT scans alone. (2) To investigate whether deep learning can further discriminate between culprit and non-culprit carotid arteries in symptomatic patients. (3) To assess the interpretability of the deep learning models. Methods Carotid contrast CT scans of consented research subjects were included in the study. Symptomatic patients had confirmed carotid artery-related ischaemic stroke or TIA in the 7 days before CT imaging, and asymptomatic patients had no prior cerebrovascular events. The dataset comprised 1148 axial symptomatic slices (covering a 3cm area of each carotid artery in 41 patients; 41 culprit and 41 non-culprit carotids) and 700 asymptomatic slices (from the bilateral carotid arteries of 25 patients). The dataset was split such that 75% was used for training and 25% for testing. A 30x30 bounding box was used to create patches of the carotid arteries from these axial slices for use as input to the CNN, a modified VGG16 architecture initialised with ImageNet weights to leverage transfer learning (the application of a model trained in one domain to a different domain) implemented in Python. Data augmentation was applied to the training set and the model was trained for 100 epochs using a cyclic learning rate, the RMSProp optimizer and binary cross-entropy loss. Class activation heatmaps were generated using the GradCAM method to highlight the areas of the image that were most important to the model for making its classification decision. Results The deep learning model was 92% accurate in correctly identifying carotid arteries from symptomatic patients versus those from asymptomatic patients. Discriminating between culprit versus non-culprit carotid arteries in symptomatic patients alone was 71% accurate. The class activation heatmaps demonstrated how the model learnt to localise the carotid artery within the image patch, and to ignore the arterial lumen when making its classification decision. Conclusions Deep learning can be used to differentiate between symptomatic and asymptomatic carotid CT scans from stroke/TIA subjects without the need for prior feature engineering. The model learns to identify relevant features in the image that predict the patients' symptom state. If further validated, this approach could be used to identify high-risk patients for intensive medical therapy. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): EPVL is undertaking a PhD funded by the Cambridge School of Clinical Medicine and the Medical Research Council's Doctoral Training Partnership

International Union of Angiology (IUA) consensus paper on imaging strategies in atherosclerotic carotid artery imaging: From basic strategies to advanced approaches

“…The ML-based algorithm demonstrated an AUC of 0.83 for differentiating thrombi with a high fraction of red blood cells (sensitivity and specificity of 77% and 74%, respectively) and an AUC of 0.84 for differentiating fibrin-rich thrombi (sensitivity and specificity of 81% and 73%, respectively) [181]. Another research investigated the ability of a DL-based model to identify symptomatic patients from asymptomatic patients and further discriminate between culprit and non-culprit carotid arteries in symptomatic patients [182]. This proposed model was 92% accurate in differentiating between symptomatic and asymptomatic patients, and 71% accurate in discriminating between culprit versus non-culprit carotid arteries in symptomatic patients [182].…”

“…Another research investigated the ability of a DL-based model to identify symptomatic patients from asymptomatic patients and further discriminate between culprit and non-culprit carotid arteries in symptomatic patients [182]. This proposed model was 92% accurate in differentiating between symptomatic and asymptomatic patients, and 71% accurate in discriminating between culprit versus non-culprit carotid arteries in symptomatic patients [182]. The relationship between carotid vessel image parameters and stroke risk was also investigated by Lal et al using an artificial intelligence algorithm for risk stratification in carotid atherosclerosis incorporating a combination of carotid plaque geometry, plaque composition, patient demographics, and clinical information [183] AI is able to mesh a large amount of quantitative imaging data to clinical parameters, that may be a new frontier of AI in carotid plaque risk assessment improving diagnosis and decision-making in daily clinical practice.…”