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; Ciprian N Ionita

doi:10.1016/j.wneu.2021.02.134

Assessment of an Artificial Intelligence Algorithm for Detection of Intracranial Hemorrhage

World Neurosurg. 2021 Jun:150:e209-e217. doi: 10.1016/j.wneu.2021.02.134. Epub 2021 Mar 5.

Authors

Affiliations

¹ 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: [email protected].
² Department of Biomedical Engineering, University at Buffalo, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, New York, USA.
³ Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA.
⁴ Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, New York, USA; Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, University at Buffalo, Buffalo, New York, USA.
⁵ Department of Neurosurgery, University of South Florida, Tampa, Florida, USA.
⁶ Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, University at Buffalo, Buffalo, New York, USA.
⁷ Canon Medical Systems, Interventional X-ray Division, Tustin, California, USA.
⁸ Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, New York, USA; Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, University at Buffalo, Buffalo, New York, USA; Department of Bioinformatics, University at Buffalo, Buffalo, New York, USA.
⁹ Department of Biomedical Engineering, University at Buffalo, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, New York, USA; Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, University at Buffalo, Buffalo, New York, USA.

PMID: 33684578
DOI: 10.1016/j.wneu.2021.02.134

Abstract

Background: Immediate 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.

Methods: Data 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.

Results: Automated 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.

Conclusions: Canon'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.

Keywords: Artificial intelligence; Brain; Hemorrhagic stroke; Noncontrast CT.

MeSH terms

Aged
Algorithms*
Artificial Intelligence*
Cohort Studies
False Positive Reactions
Female
Glasgow Coma Scale
Humans
Image Processing, Computer-Assisted
Intracranial Hemorrhages / diagnosis*
Intracranial Hemorrhages / diagnostic imaging*
Male
Middle Aged
Predictive Value of Tests
Retrospective Studies
Sensitivity and Specificity
Stroke / etiology
Tomography, X-Ray Computed