*Artificial Intelligence: Development and Applications in Neurosurgery DOI: http://dx.doi.org/10.5772/intechopen.113034*

approaches. In 2020, Shi et al. developed a 3D CNN trained on 1177 digital subtraction angiography verified bone-removal CTA cases, which when tested on a cohort of suspected acute ischemic stroke (AIS) patients found that the model could exclude IA-negative cases with 99.0% confidence [85]. Limitations in their study include a relatively small sample of positive cases in the validation cohorts as well as the experimentally reasonable exclusion of CTA data with head trauma and arteriovenous malformation/fistula (AVM/AVF). In 2021, Yang et al. proposed a 3D CNN algorithm for detecting cerebral aneurysms using head CTA images, achieving a very high sensitivity of 97.5% (633 of 649) while revealing 8 intracranial aneurysms overlooked in initial reports [86]. When the model was paired with expert radiologists, their overall weighted alternative free-response receiver operating characteristic (wAFROC) curve improved by 0.01 (*P* < .05), demonstrating the viability for physician-machine adjunct usage.
