Saturday, March 6, 2021

Assessment of an Artificial Intelligence Algorithm for Detection of Intracranial Hemorrhage

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Assessment of an Artificial Intelligence Algorithm for Detection of Intracranial Hemorrhage

Samantha E.SeymourHSD12Meredith E.LaQueHSD12Blake A.PetersonBS3Kenneth V.SnyderMD, PhD234MaximMokinMD, PhD5MuhammadWaqasMBBS24YiemengHoiPhD6Jason M.DaviesMD, PhD2347Elad I.LevyMD, MBA234Adnan H.SiddiquiMD, PhD234Ciprian N.IonitaPhD1234
1
Department of Biomedical Engineering, University at Buffalo, Buffalo, NY 14260, US
2
Canon Stroke and Vascular Research Center, Buffalo, NY, 14203, US
3
Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, 14203, US
4
Department of Neurosurgery, University at Buffalo, Buffalo, NY 14203, US
5
Department of Neurosurgery, University of South Florida, Tampa, FL, 33606, US
6
Canon Medical Systems USA Inc., Tustin, CA 92780, US
7
Department of Bioinformatics, University at Buffalo, Buffalo, NY 14214, US

Received 18 January 2021, Revised 26 February 2021, Accepted 27 February 2021, Available online 5 March 2021.

ABSTRACT

Background

Immediate and accurate detection of intracranial hemorrhages (ICHs) is essential to provide a good clinical outcome for ICH patients. Artificial intelligence has the potential to provide this, but 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. ICH patients had at least one of the following hemorrhage types: intraparenchymal (n=181), intraventricular (n=45), subdural (n=13), or subarachnoid (n=19). Non-contrast 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 ICH patients (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. 95% (245/258) of ICH volumes were correctly triaged while 88.2% (90/102) 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 ICH patients.(By how much?)

 
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