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

a specificity of 92.3 to 99.0%, Aidoc for ICH, an FDA-approved DL tool, is one of the industry's leading support systems for evaluation and warning notification of unenhanced head CT images of ICHs [62–66]. Aidoc for ICH and other DL learning models have been demonstrated to produce inconsistencies in performance when applied to non-native trained clinical sites [64, 67]. Thus, further studies have sought to investigate alternatives including competing commercial software in addition to independently developed models. For instance, McLouth et al. and Rava et al. have validated the diagnostic capabilities of other DL ICH tools such as CINA v1.0 and Canon's AUTOStroke Solution ICH across hospital sites in the United States, finding high accuracy and specificity with medium sensitivity thresholds [68, 69]. Wang et al., winners of the 2019-RSNA Brain CT Hemorrhage Challenge, developed a convolutional neural network (CNN) using a diverse array of datasets sourced from three institutions that achieved accuracy levels similar to that of senior radiologists [67]. Despite the outstanding results of the algorithm, it is important to note that the CNN model's applicability in clinical settings is currently limited by (1) the lack of patient clinical information in the RSNA-challenge provided datasets, thereby obscuring the confounding effects of scanner type, cause of bleeding, and patient demographics, (2) its inapplicability to MRI imaging which is oftentimes crucial for ICH screening and diagnosis, and (3) external validation data are lacking [67].
