Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review

The correct recognition of the etiology of ischemic stroke (IS) allows tempestive interventions in therapy with the aim of treating the cause and preventing a new cerebral ischemic event. Nevertheless, the identification of the cause is often challenging and is based on clinical features and data obtained by imaging techniques and other diagnostic exams. TOAST classification system describes the different etiologies of ischemic stroke and includes five subtypes: LAAS (large-artery atherosclerosis), CEI (cardio embolism), SVD (small vessel disease), ODE (stroke of other determined etiology), and UDE (stroke of undetermined etiology). AI models, providing computational methodologies for quantitative and objective evaluations, seem to increase the sensitivity of main IS causes, such as tomographic diagnosis of carotid stenosis, electrocardiographic recognition of atrial fibrillation, and identification of small vessel disease in magnetic resonance images. The aim of this review is to provide overall knowledge about the most effective AI models used in the differential diagnosis of ischemic stroke etiology according to the TOAST classification. According to our results, AI has proven to be a useful tool for identifying predictive factors capable of subtyping acute stroke patients in large heterogeneous populations and, in particular, clarifying the etiology of UDE IS especially detecting cardioembolic sources.

Automated summary

The correct recognition of ischemic stroke etiology is crucial for effective treatment and prevention of recurrence. However, identifying the cause is often challenging, relying on clinical features and diagnostic exams. AI models can enhance sensitivity in identifying main causes of ischemic stroke, such as carotid stenosis, atrial fibrillation, and small vessel disease. This review aims to provide overall knowledge on the most effective AI models in the differential diagnosis of ischemic stroke according to the TOAST classification. Our results show that AI is a useful tool for subtyping acute stroke patients and clarifying the etiology of undetermined ischemic stroke, particularly identifying cardioembolic sources.