• World Neurosurg · Jun 2021

    Assessment of an Artificial Intelligence Algorithm for Detection of Intracranial Hemorrhage.

    • Ryan A Rava, Samantha E Seymour, Meredith E LaQue, Blake A Peterson, Kenneth V Snyder, Maxim Mokin, Muhammad Waqas, Yiemeng Hoi, Jason M Davies, Elad I Levy, Adnan H Siddiqui, and Ciprian N Ionita.
    • Department of Biomedical Engineering, University at Buffalo, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, New York, USA. Electronic address: ryanrava@buffalo.edu.
    • World Neurosurg. 2021 Jun 1; 150: e209-e217.

    BackgroundImmediate and accurate detection of intracranial hemorrhages (ICHs) is essential to provide a good clinical outcome for patients with ICH. Artificial intelligence has the potential to provide this, but the assessment of these methods needs to be investigated in depth. This study aimed to assess the ability of Canon's AUTOStroke Solution ICH detection algorithm to accurately identify patients both with and without ICHs present.MethodsData from 200 ICH and 102 non-ICH patients who presented with stroke-like symptoms between August 2016 and December 2019 were collected retrospectively. Patients with ICH had at least one of the following hemorrhage types: intraparenchymal (n = 181), intraventricular (n = 45), subdural (n = 13), or subarachnoid (n = 19). Noncontrast computed tomography scans were analyzed for each patient using Canon's AUTOStroke Solution ICH algorithm to determine which slices contained hemorrhage. The algorithm's ability to detect ICHs was assessed using sensitivity, specificity, positive predictive value, and negative predictive value. Percentages of cases correctly identified as ICH positive and negative were additionally calculated.ResultsAutomated analysis demonstrated the following metrics for identifying hemorrhage slices within all 200 patients with ICH (95% confidence intervals): sensitivity = 0.93 ± 0.03, specificity = 0.93 ± 0.01, positive predictive value = 0.85 ± 0.02, and negative predictive value = 0.98 ± 0.01. A total of 95% (245 of 258) of ICH volumes were correctly triaged, whereas 88.2% (90 of 102) of non-ICH cases were correctly classified as ICH negative.ConclusionsCanon's AUTOStroke Solution ICH detection algorithm was able to accurately detect intraparenchymal, intraventricular, subdural, and subarachnoid hemorrhages in addition to accurately determine when an ICH was not present. Having this automated ICH detection method could drastically improve treatment times for patients with ICH.Copyright © 2021 Elsevier Inc. All rights reserved.

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