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 ob...
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MDPI AG
2023-04-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/11/4/1138 |
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| author | Giuseppe Miceli Maria Grazia Basso Giuliana Rizzo Chiara Pintus Elena Cocciola Andrea Roberta Pennacchio Antonino Tuttolomondo |
| author_facet | Giuseppe Miceli Maria Grazia Basso Giuliana Rizzo Chiara Pintus Elena Cocciola Andrea Roberta Pennacchio Antonino Tuttolomondo |
| author_sort | Giuseppe Miceli |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T05:12:53Z |
| format | Article |
| id | doaj.art-417378a38650480ba02218c2c9044ad5 |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-11T05:12:53Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-417378a38650480ba02218c2c9044ad52023-11-17T18:27:03ZengMDPI AGBiomedicines2227-90592023-04-01114113810.3390/biomedicines11041138Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative ReviewGiuseppe Miceli0Maria Grazia Basso1Giuliana Rizzo2Chiara Pintus3Elena Cocciola4Andrea Roberta Pennacchio5Antonino Tuttolomondo6Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, ItalyDepartment of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, ItalyDepartment of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, ItalyDepartment of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, ItalyDepartment of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, ItalyDepartment of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, ItalyDepartment of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, ItalyThe 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.https://www.mdpi.com/2227-9059/11/4/1138artificial intelligenceischemic strokemachine learningdeep learningtoast classification |
| spellingShingle | Giuseppe Miceli Maria Grazia Basso Giuliana Rizzo Chiara Pintus Elena Cocciola Andrea Roberta Pennacchio Antonino Tuttolomondo Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review Biomedicines artificial intelligence ischemic stroke machine learning deep learning toast classification |
| title | Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review |
| title_full | Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review |
| title_fullStr | Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review |
| title_full_unstemmed | Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review |
| title_short | Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review |
| title_sort | artificial intelligence in acute ischemic stroke subtypes according to toast classification a comprehensive narrative review |
| topic | artificial intelligence ischemic stroke machine learning deep learning toast classification |
| url | https://www.mdpi.com/2227-9059/11/4/1138 |
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