Advancing Intracranial Aneurysm Detection: A Comprehensive Systematic Review and Meta-analysis of Deep Learning Models Performance, Clinical Integration, and Future Directions.
Authors
Affiliations (8)
Affiliations (8)
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; Neuraitex Research Center, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
- Student Research Committee, Faculty of Medicine, Mazandaran University of Medical Sciences, Mazandaran, Iran.
- Doctor of Medicine (MD), Iran University of Medical Sciences, Tehran, Iran.
- Student Research Committee, School of Medicine, Iran University of Medical Science, Tehran, Iran.
- Department of Neurosurgery, Hazrat Rasool Hospital, Iran University of Medical Sciences, Tehran, Iran.
- Student Research Committee, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; Department of Electrical and Computer Engineering University of Waterloo, Waterloo, Canada. Electronic address: [email protected].
- Department of Neurosurgery, Sina Hospital, Tehran University of Medical Sciences, Tehran, Iran; Gammaknife Center, Yas Hospital, Tehran University of Medical Sciences, Tehran, Iran. Electronic address: [email protected].
Abstract
Cerebral aneurysms pose a significant risk to patient safety, particularly when ruptured, emphasizing the need for early detection and accurate prediction. Traditional diagnostic methods, reliant on clinician-based evaluations, face challenges in sensitivity and consistency, prompting the exploration of deep learning (DL) systems for improved performance. This systematic review and meta-analysis assessed the performance of DL models in detecting and predicting intracranial aneurysms compared to clinician-based evaluations. Imaging modalities included CT angiography (CTA), digital subtraction angiography (DSA), and time-of-flight MR angiography (TOF-MRA). Data on lesion-wise sensitivity, specificity, and the impact of DL assistance on clinician performance were analyzed. Subgroup analyses evaluated DL sensitivity by aneurysm size and location, and interrater agreement was measured using Fleiss' κ. DL systems achieved an overall lesion-wise sensitivity of 90 % and specificity of 94 %, outperforming human diagnostics. Clinician specificity improved significantly with DL assistance, increasing from 83 % to 85 % in the patient-wise scenario and from 93 % to 95 % in the lesion-wise scenario. Similarly, clinician sensitivity also showed notable improvement with DL assistance, rising from 82 % to 96 % in the patient-wise scenario and from 82 % to 88 % in the lesion-wise scenario. Subgroup analysis showed DL sensitivity varied with aneurysm size and location, reaching 100 % for aneurysms larger than 10 mm. Additionally, DL assistance improved interrater agreement among clinicians, with Fleiss' κ increasing from 0.668 to 0.862. DL models demonstrate transformative potential in managing cerebral aneurysms by enhancing diagnostic accuracy, reducing missed cases, and supporting clinical decision-making. However, further validation in diverse clinical settings and seamless integration into standard workflows are necessary to fully realize the benefits of DL-driven diagnostics.