A Deep Learning System to Predict Recurrence and Disability Outcomes in Patients with Transient Ischemic Attack or Ischemic Stroke

Ischemic strokes (IS) and transient ischemic attacks (TIA) account for approximately 80% of all strokes and are leading causes of death worldwide. Assessing the risk of recurrence or functional impairment in IS and TIA patients is essential to both acute phase treatment and secondary prevention. Current risk prediction systems that rely on clinical parameters alone without leveraging imaging data have only modest performance. Herein, a deep learning-based risk prediction system (RPS) is developed to predict the probability of stroke recurrence or disability (i.e., deep-learning stroke recurrence risk score, SRR score). Then, Kaplan-Meier analysis to evaluate the ability of SRR score to stratify patients at stroke recurrence risk is discussed. Using 15 166 Third China National Stroke Registry (CNSR-III) cases, the RPS's receiver operating characteristic curve (AUC) values of 0.850 for 14 day TIA recurrence prediction and 0.837 for 3 month IS disability prediction are used. Among patients deemed high risk by SRR score, 22.9% and 24.4% of individuals with TIA and IS respectively have stroke recurrence within 1 year, which are significantly higher than the rates in low-risk individuals. Deep learning-based RPS can outperform conventional risk scores and has the potential to assist accurate prognostication in stroke patients to optimize management.