Improving stroke diagnosis and treatment with deep learning: a review

Ischemic stroke, a severe cerebrovascular disease, poses significant health challenges due to its high morbidity and mortality rates. Traditional stroke management relies on direct visual inspection of noncontrast magnetic resonance imaging (MRI) or computed tomography (CT) images, which can be affected by equipment artifacts, noise, and the radiologist's experience. To address these limitations, computer-aided diagnostic (CAD) methods, especially deep learning (DL) approaches, have gained prominence over the last decade. With their sophisticated data-processing abilities, DL models are effective for immediate stroke management and prediction. This review explores the impact of DL on stroke diagnosis and treatment, highlighting advantages such as reduced treatment duration, improved patient satisfaction, and increased clinician efficiency. It examines five critical areas: timely stroke diagnosis, automated Alberta stroke program early CT score (ASPECTS) calculation, large vessel occlusion (LVO) detection, ischemic prognosis, and predicting imaging outcomes. These AI algorithms expedite diagnosis, enhance scoring accuracy, efficiently detect occlusions, and provide accurate prognostic information, aiding disease monitoring and treatment evaluation. Despite advancements, challenges remain, including the need for larger, diverse datasets, improved model interpretability, and extensive external validation. Integrating AI tools into clinical workflows and addressing ethical and regulatory challenges are crucial for broader acceptance and application.

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