The value of artificial intelligence in the detection of early cerebral changes in acute stroke using non-contrast CT scans
El-Sheikh, H., Khater, H., Al -Tohamy, J., Ahmed, K., Hassan, H. (2025). The value of artificial intelligence in the detection of early cerebral changes in acute stroke using non-contrast CT scans. EKB Journal Management System, 42(6), 41-49. doi: 10.21608/bmfj.2024.329095.2234
Hesham El-Sayed El-Sheikh; Hamada Mohammed Khater; Jehan Ibrahim Al -Tohamy; Khaled El Sayed Ahmed; Heba Ahmed Hassan. "The value of artificial intelligence in the detection of early cerebral changes in acute stroke using non-contrast CT scans". EKB Journal Management System, 42, 6, 2025, 41-49. doi: 10.21608/bmfj.2024.329095.2234
El-Sheikh, H., Khater, H., Al -Tohamy, J., Ahmed, K., Hassan, H. (2025). 'The value of artificial intelligence in the detection of early cerebral changes in acute stroke using non-contrast CT scans', EKB Journal Management System, 42(6), pp. 41-49. doi: 10.21608/bmfj.2024.329095.2234
El-Sheikh, H., Khater, H., Al -Tohamy, J., Ahmed, K., Hassan, H. The value of artificial intelligence in the detection of early cerebral changes in acute stroke using non-contrast CT scans. EKB Journal Management System, 2025; 42(6): 41-49. doi: 10.21608/bmfj.2024.329095.2234

The value of artificial intelligence in the detection of early cerebral changes in acute stroke using non-contrast CT scans

Benha Medical Journal
Article 5, Volume 42, Issue 6, June 2025, Page 41-49  XML PDF (859.04 K)
Document Type: Original Article
DOI: 10.21608/bmfj.2024.329095.2234
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Authors
Hesham El-Sayed El-Sheikh1; Hamada Mohammed Khater2; Jehan Ibrahim Al -Tohamy3; Khaled El Sayed Ahmed4; Heba Ahmed Hassan email 5
1Professor of Diagnostic Radiology Faculty of Medicine - Benha University
2Assistant Professor of Diagnostic Radiology Faculty of Medicine - Benha University
3Fellow of radiology, General Organization for Teaching Hospitals and Institutes
4Assistant professor of biomedical engineering Faculty of Engineering - Benha University
5M.Sc. in Diagnostic Radiology- Benha University Assistant lecturer of Diagnostic Radiology- Benha University
Abstract
Background: Over the last few years, there has been increasing interest in the use of deep learning algorithms to assist with abnormality detection on medical images. Aim and Objective: was to assess the value of artificial intelligence in the detection of early cerebral changes in acute stroke using non-contrast CT scans. Patient & Methods: this cross sectional study included 1095 patients distributed across both training and validation, as well as a separate test set. Using 48 hours follow up non contrast CT images as the main reference standard to diagnose the acute ischemic stroke at the initial CT images & AI. Axial scanning extending from base of the skull up to the vertex with coronal & sagittal reformate images. Results: There were no statistically significant differences found among the diagnosis results of the first and second radiologist’s diagnosis and the AI system diagnosis (p > 0.05). Conclusion: In spite CAD system has established fair accuracy, the need of more accurate algorithm is necessary to determine if it can replicate non contrast CT and radiologist observations.
Keywords
Artificial Intelligence; non contrast computed tomography scan; acute ischemic stroke
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