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A Systematic Review of Deep-Learning Methods for Intracranial Aneurysm Detection in CT Angiography
1Laboratory of Imaging Technologies, Faculty of Electrical Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia.
Insights
Artificial intelligence algorithms show high accuracy in detecting large cerebral aneurysms on CT angiography. However, detecting smaller aneurysms remains a challenge, requiring further research and standardized metrics for improved diagnostic accuracy.
Area of Science:
- Neurology
- Radiology
- Medical Imaging
Background:
- Subarachnoid hemorrhage from cerebral aneurysm rupture is a major cause of death and disability.
- Early aneurysm detection via Computed Tomography Angiography (CTA) is critical for patient outcomes.
- Artificial intelligence (AI) can enhance aneurysm detection rates and reduce variability.
Purpose of the Study:
- To systematically review and meta-analyze the diagnostic accuracy of deep-learning AI algorithms for detecting cerebral aneurysms on CTA.
- To assess the performance of AI in identifying aneurysms of varying sizes.
Main Methods:
- Systematic search of PubMed, Embase, and Cochrane Library (Jan 2015-July 2023).
- Inclusion of studies using automated/semi-automatic deep-learning for aneurysm detection on CTA.
- Assessment using PRISMA and QUADAS-2 guidelines; meta-analysis of sensitivity, specificity, and false positives.
- Utilized enhanced FROC curves for study comparison.
Main Results:
- Fifteen studies were included, showing high pooled lesion-level sensitivity (0.87) for intracranial aneurysms.
- Sensitivity for small aneurysms (<3 mm) was notably low (0.56).
- Limited data on patient-level sensitivity and specificity due to definitional inconsistencies and lack of control groups.
Conclusions:
- Deep-learning AI demonstrates high accuracy for detecting cerebral aneurysms >3 mm on CTA.
- Significant need for research focused on improving detection of smaller aneurysms (<3 mm).
- Call for standardized test datasets and consistent performance metrics for AI in aneurysm detection.
Abstract:
Background: Subarachnoid hemorrhage resulting from cerebral aneurysm rupture is a significant cause of morbidity and mortality. Early identification of aneurysms on Computed Tomography Angiography (CTA), a frequently used modality for this purpose, is crucial, and artificial intelligence (AI)-based algorithms can improve the detection rate and minimize the intra- and inter-rater variability. Thus, a systematic review and meta-analysis were conducted to assess the diagnostic accuracy of deep-learning-based AI algorithms in detecting cerebral aneurysms using CTA. Methods: PubMed (MEDLINE), Embase, and the Cochrane Library were searched from January 2015 to July 2023. Eligibility criteria involved studies using fully automated and semi-automatic deep-learning algorithms for detecting cerebral aneurysms on the CTA modality. Eligible studies were assessed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. A diagnostic accuracy meta-analysis was conducted to estimate pooled lesion-level sensitivity, size-dependent lesion-level sensitivity, patient-level specificity, and the number of false positives per image. An enhanced FROC curve was utilized to facilitate comparisons between the studies. Results: Fifteen eligible studies were assessed. The findings indicated that the methods exhibited high pooled sensitivity (0.87, 95% confidence interval: 0.835 to 0.91) in detecting intracranial aneurysms at the lesion level. Patient-level sensitivity was not reported due to the lack of a unified patient-level sensitivity definition. Only five studies involved a control group (healthy subjects), whereas two provided information on detection specificity. Moreover, the analysis of size-dependent sensitivity reported in eight studies revealed that the average sensitivity for small aneurysms (<3 mm) was rather low (0.56). Conclusions: The studies included in the analysis exhibited a high level of accuracy in detecting intracranial aneurysms larger than 3 mm in size. Nonetheless, there is a notable gap that necessitates increased attention and research focus on the detection of smaller aneurysms, the use of a common test dataset, and an evaluation of a consistent set of performance metrics.
