Deep learning-based automatic classification of ischemic stroke subtype using diffusion-weighted images

BACKGROUND Accurate classification of ischemic stroke subtype is important for effective secondary prevention of stroke. We used diffusion-weighted imaging (DWI) and atrial fibrillation (AF) data to train a deep learning algorithm to classify stroke subtype. METHODS Model training, validation, and internal testing were done in 2,988 patients with acute ischemic stroke from three stroke centers by using U-net for infarct segmentation and EfficientNetV2 for stroke subtype classification. Experienced vascular neurologists (n=5) determined stroke subtypes for external test datasets, while establishing a consensus for clinical trial datasets using the TOAST classification. Infarcts on DW images were automatically segmented using an artificial intelligence solution that we recently developed, and their masks were fed as inputs to a deep learning algorithm (DWI-only algorithm). Subsequently, another model was trained, with the presence or absence of AF included in the training as a categorical variable (DWI+AF algorithm). These models were tested: a) internally against the opinion of the labeling experts, b) against fresh external DWI data, and also c) against clinical trial DWI data acquired at a later date. RESULTS In the training-and-validation datasets, the mean age was 68.0±12.5 (61.1% male). In internal testing, compared with the experts, the DWI-only algorithm and the DWI+AF algorithm respectively achieved moderate (65.3%) and near-strong (79.1%) agreement. In external testing, both algorithms again showed good agreements (59.3-60.7% and 73.7-74.0%, respectively). In the clinical trial dataset, compared with the expert consensus, percentage agreements and Cohen’s kappa were respectively 58.1% and 0.34 for the DWI-only algorithm vs. 72.9% and 0.57 for the DWI+AF algorithm. The corresponding values between experts were comparable (76.0% and 0.61) to the DWI+AF algorithm. CONCLUSIONS Our deep learning algorithm trained on a large dataset of DWI (both with or without AF information) was able to classify ischemic stroke subtypes as accurately as a consensus of stroke experts. ### Competing Interest Statement Wi-Sun Ryu, Hoyoun Lee, and Dongmin Kim are employees of JLK Inc. ### Funding Statement This study was supported by the Multiministry Grant for Medical Device Development (KMDF\_PR\_20200901_0098), the National Priority Research Center Program Grant (NRF-2021R1A6A1A03038865), and the Basic Science Research Program Grant (NRF-2020R1A2C3008295) of National Research Foundation, funded by the Korean government. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The institutional review board of Dongguk University Hospital approved the study protocol (IRB No. 2017-09-017), and patients or their legal proxies provided a written informed consent. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data generated or analyzed during the study are available from the corresponding author by request. * DWI : diffusion-weighted MRI LAA : large artery atherosclerosis CE : cardioembolism SVO : small vessel occlusion TOAST : The Trial of Org10172 in Acute Stroke ECG : electrocardiography AF : atrial fibrillation AI : artificial intelligence NIHSS : National Institute of Health Stroke Scale ESUS : Embolic Stroke with Undetermined Source RCT : randomized clinical trial