Applications of deep learning in intracranial aneurysm imaging:  A scoping review of detection,  risk prediction, and emerging  prognostic models

Ali  Jafarpour; Matin  Akhbari; Kamyar  Khorsand; Kiana  Naghavi; Mohammad Moein  Ghaemmaghami; Elaheh  Ghaderi; Elham  Yadegarifard; Shamimeh  Arabgol; Rozhan  Esmaeili-Benvidi; Behnaz  Mahmoudvand; Seed Amirabbas  Shahidi-Marnani; Saina  Hasany; Amirhossein  Rigi; Nazanin  Sarvi; Pooria  Sobhanian; Mahsa Asadi-Anar; Melika  Arab-Bafrani; Farbod  Khosravi

doi:10.18502/cjn.v24i1.20302

Ali Jafarpour Department of Neurosurgery, Sina Hospital, Tehran University of Medical Sciences, Tehran, Iran
Matin Akhbari Faculty of Medicine, Istanbul Yeni Yuzyil University, Istanbul, Turkey
Kamyar Khorsand School of Medicine, Ahvaz Jondishapur University of Medical Sciences, Ahvaz, Iran
Kiana Naghavi Student Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Mohammad Moein Ghaemmaghami School of Medicine, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Elaheh Ghaderi Kermanshah University of Medical Sciences, Kermanshah, Iran
Elham Yadegarifard Student Research Committee, School of Medicine, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
Shamimeh Arabgol School of Medicine, Iran University of Medical Sciences, Tehran, Iran
Rozhan Esmaeili-Benvidi School of Medicine, Kashan University of Medical Sciences, Kashan, Iran
Behnaz Mahmoudvand Student Research Committee, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
Seed Amirabbas Shahidi-Marnani School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
Saina Hasany Department of Biotechnology, Islamic Azad University, Tehran Medical Branch, Tehran, Iran
Amirhossein Rigi Department of Radiology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Nazanin Sarvi Department of Medicine, Islamic Azad University, Yazd Branch, Yazd, Iran
Pooria Sobhanian Student Research Committee, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
Mahsa Asadi-Anar Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Melika Arab-Bafrani Students' Scientific Research Center (SSRC), Tehran University of Medical Sciences, Tehran, Iran
Farbod Khosravi School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

DOI: https://doi.org/10.18502/cjn.v24i1.20302

Keywords: Deep Learning; Intracranial Aneurysm; Neurosurgery

Abstract

Background: Intracranial aneurysms (IAs) pose a significant risk of rupture and subarachnoid hemorrhage, necessitating early, accurate detection and risk stratification. With advances in artificial intelligence, deep learning (DL) has emerged as a transformative tool in neurovascular imaging. However, the clinical translation of DL applications remains constrained by variability in model design, data sources, and validation strategies. The aim of the present study was to systematically map and evaluate the landscape of DL applications in the detection, segmentation, risk prediction, and outcome assessment of IAs, with attention to methodological rigor, clinical utility, and translational limitations.

Methods: We conducted a scoping review of studies indexed in PubMed, Scopus, and Web of Science up to August 2023, following PRISMA-ScR guidelines. Eligible studies employed DL algorithms for IA-related diagnostic or prognostic tasks using radiological imaging. Data extraction included model architecture, imaging modality, validation strategy, performance metrics, and thematic focus. Study quality was assessed using the Joanna Briggs Institute (JBI) critical appraisal tools.

Results: Forty-two studies met the inclusion criteria, encompassing over 10,000 patients across diverse imaging platforms and DL architectures. Convolutional neural networks (CNNs) were the most commonly used models, with reported sensitivities ranging from 73% to 99% and AUCs frequently exceeding 0.85. Despite promising results in IA detection and rupture risk prediction, only a minority of studies conducted external validation or addressed post-treatment outcomes. Major gaps include a lack of benchmarking across models, limited explainability, and regulatory or ethical frameworks.

Conclusion: DL algorithms demonstrate strong diagnostic and predictive performance in IA imaging but face critical barriers to clinical integration, including interpretability challenges, dataset heterogeneity, and limited generalizability. Future research should prioritize multicenter validation, explainable AI techniques, and outcome-focused modeling to advance safe and effective deployment in neurosurgical care.