Application of convolutional network models in detection of intracranial aneurysms: A systematic review and meta-analysis

Abstract

Introduction Intracranial aneurysms have a high prevalence in human population. It also has a heavy burden of disease and high mortality rate in the case of rupture. Convolutional neural network(CNN) is a type of deep learning architecture which has been proven powerful to detect intracranial aneurysms. Methods Four databases were searched using artificial intelligence, intracranial aneurysms, and synonyms to find eligible studies. Articles which had applied CNN for detection of intracranial aneurisms were included in this review. Sensitivity and specificity of the models and human readers regarding modality, size, and location of aneurysms were sought to be extracted. Random model was the preferred model for analyses using CMA 2 to determine pooled sensitivity and specificity. Results Overall, 20 studies were used in this review. Deep learning models could detect intracranial aneurysms with a sensitivity of 90/6% (CI: 87/2–93/2%) and specificity of 94/6% (CI: 0/914–0/966). CTA was the most sensitive modality (92.0%(CI:85/2–95/8%)). Overall sensitivity of the models for aneurysms more than 3 mm was above 98% (98%-100%) and 74.6 for aneurysms less than 3 mm. With the aid of AI, the clinicians’ sensitivity increased to 12/8% and interrater agreement to 0/193. Conclusion CNN models had an acceptable sensitivity for detection of intracranial aneurysms, surpassing human readers in some fields. The logical approach for application of deep learning models would be its use as a highly capable assistant. In essence, deep learning models are a groundbreaking technology that can assist clinicians and allow them to diagnose intracranial aneurysms more accurately.

Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis

“…Another recent systematic review and meta-analysis included 20 CNN studies to identify cerebral aneurysms. 77 Our systematic review emphasizes the current low level of evidence which undermines the performance accuracy of reported studies including those using CNNs, whereas the review by Abdollahifard et al does not raise any concerns regarding the bias or applicability of the studies. The discrepancy is potentially because we systematically applied PRISMA-DTA and QUADAS-2 methodology, which is the standard used for diagnostic accuracy studies.…”

“…Our systematic review and meta-analysis provide evidence for the quality and performance accuracy of all published studies using AI CAD for aneurysm detection. Another recent systematic review and meta-analysis included 20 CNN studies to identify cerebral aneurysms 77. Our systematic review emphasizes the current low level of evidence which undermines the performance accuracy of reported studies including those using CNNs, whereas the review by Abdollahifard et al does not raise any concerns regarding the bias or applicability of the studies.…”

Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis

“…To further highlight this conclusion, we can see that AI strengthens the performance of human operators such as radiologists in other fields such as screening of mammograms and breast cancer 44 45. The implications oare also observed to be highly appliable in neuroradiology since AI models have proven to improve the metrics of physicians when used as an assistant tool 15…”

“…This study demonstrated that the higher the size of the hematoma, the higher the detection rate is. Although DL is a black box, and the way it segments and detects lesions has not been well understood, similar articles have reported relatively similar results 1 5 26 …”

“…DL models consist of numerous architectures such as deep neural networks (DNN), recurrent neural networks (RNN), and convolutional neural networks (CNN) 13. As a subcategory of DL, CNNs are algorithms that use convolution and are most effective in image processing tasks such as one being discussed here 14 15…”