Ryan A Rava1, Samantha E Seymour2, Meredith E LaQue2, Blake A Peterson3, Kenneth V Snyder4, Maxim Mokin5, Muhammad Waqas6, Yiemeng Hoi7, Jason M Davies8, Elad I Levy4, Adnan H Siddiqui4, Ciprian N Ionita9. 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: ryanrava@buffalo.edu. 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.
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.
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-ICHpatients 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.
Authors: Samantha E Seymour; Ryan A Rava; Dennis J Swetz; Andre Monteiro; Ammad Baig; Kurt Schultz; Kenneth V Snyder; Muhammad Waqas; Jason M Davies; Elad I Levy; Adnan H Siddiqui; Ciprian N Ionita Journal: Proc SPIE Int Soc Opt Eng Date: 2022-04-04
Authors: Dennis Swetz; Samantha E Seymour; Ryan A Rava; Mohammad Mahdi Shiraz Bhurwani; Andre Monteiro; Ammad A Baig; Muhammad Waqas; Kenneth V Snyder; Elad I Levy; Jason M Davies; Adnan H Siddiqui; Ciprian N Ionita Journal: Proc SPIE Int Soc Opt Eng Date: 2022-04-04
Authors: Almut Kundisch; Alexander Hönning; Sven Mutze; Lutz Kreissl; Frederik Spohn; Johannes Lemcke; Maximilian Sitz; Paul Sparenberg; Leonie Goelz Journal: PLoS One Date: 2021-11-29 Impact factor: 3.240
Authors: Javier Bravo; Arvin R Wali; Brian R Hirshman; Tilvawala Gopesh; Jeffrey A Steinberg; Bernard Yan; J Scott Pannell; Alexander Norbash; James Friend; Alexander A Khalessi; David Santiago-Dieppa Journal: Cureus Date: 2022-03-30