A retrospective analysis of the diagnostic performance of an FDA approved software for the detection of intracranial hemorrhage

ObjectiveTo determine the sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of Rapid ICH, a commercially available AI model, in detecting intracranial hemorrhage (ICH) on non-contrast computed tomography (NCCT) examinations of the head at a single regional medical center. MethodsRapidAIs Rapid ICH is incorporated into real time hospital workflow to assist radiologists in the identification of ICH on NCCT examinations of the head. 412 examinations from August 2022 to January 2023 were pulled for analysis. Scans in which it was unclear if ICH was present or not, as well as scans significantly affected by motion artifact were excluded from the study. The sensitivity, specificity, accuracy, PPV, and NPV of the software were then assessed retrospectively for the remaining 406 NCCT examinations using prior radiologist report as the ground-truth. A two tailed z test with = 0.05 was preformed to determine if the sensitivity and specificity of the software in this study were significantly different from Rapid ICHs reported sensitivity and specificity. Additionally, the softwares performance was analyzed separately for the male and female populations and a chi-square test of independence was used to determine if model correctness significantly depended on sex. ResultsOf the 406 scans assessed, Rapid ICH flagged 82 ICH positive cases and 324 ICH negative cases. There were 80 examinations (19.7%) truly positive for ICH and 326 examinations (80.3%) negative for ICH. This resulted in a sensitivity of 71.3%, 95% CI [61.3%-81.2%], a specificity of 92.3%, 95% CI [89.4%-95.2%], an accuracy of 88.2%, 95% CI [85.0%-91.3%], a PPV of 69.5%, 95% CI [59.5%-79.5%], and an NPV of 92.9%, 95% CI [90.1%-95.7%]. Two examinations were excluded due to no existing information on patient sex in the electronic medical record. The resulting sensitivity was significantly different from the sensitivity reported by Rapid ICH (95%), z = 2.60, p = .009 although the resulting specificity was not significantly different from the specificity reported by Rapid ICH (94%), z = 0.65, p = .517. The model performance did not depend on sex per the chi-square test of independence: X2 (1 degree of freedom, N = 404) = 1.95, p = .162 (p > 0.05). ConclusionRapid ICH demonstrates exceptional capability in the identification of ICH, but its performance when used at this site differs from the values advertised by the company, and from assessments of the models performance by other research groups. Specifically, the sensitivity of the software at this site is significantly different from the sensitivity reported by the company. These results underscore the necessity for independent evaluation of the software at institutions where it is implemented.