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

Document Type : Original Article

Authors

1 Professor of Diagnostic Radiology Faculty of Medicine - Benha University

2 Assistant Professor of Diagnostic Radiology Faculty of Medicine - Benha University

3 Fellow of radiology, General Organization for Teaching Hospitals and Institutes

4 Assistant professor of biomedical engineering Faculty of Engineering - Benha University

5 M.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

Benha Medical Journal
Volume 42, Issue 6
(special issue radiology)
June 2025
Pages 41-49

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