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

Abstract

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.

Keywords: artificial intelligence; deep learning; ischemic stroke; machine learning; toast classification.

Figure 1

Artificial intelligence in stroke diagnosis according to TOAST classification. Artificial intelligence path over recent years has been a stairway to matching human complexity, introducing increasingly complex networks of data processing, among which deep learning is nowadays one of the leading figures. These complex algorithms allow for elaborate input data (e.g., MRI, ECG leads), which are acquired, read, and finally processed and interpreted. The possibility to elaborate data from ECG, MRI, CT images, and ultrasound allow for an elaborate algorithm for stroke subtype diagnosis.