Assessment of an Artificial Intelligence Algorithm for Detection of Intracranial Hemorrhage
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...
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Veröffentlicht in: | World neurosurgery 2021-06, Vol.150, p.e209-e217 |
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creator | Rava, Ryan A. Seymour, Samantha E. LaQue, Meredith E. Peterson, Blake A. Snyder, Kenneth V. Mokin, Maxim Waqas, Muhammad Hoi, Yiemeng Davies, Jason M. Levy, Elad I. Siddiqui, Adnan H. Ionita, Ciprian N. |
description | 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.
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.
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.
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. |
doi_str_mv | 10.1016/j.wneu.2021.02.134 |
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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.
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.
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.</description><identifier>ISSN: 1878-8750</identifier><identifier>EISSN: 1878-8769</identifier><identifier>DOI: 10.1016/j.wneu.2021.02.134</identifier><identifier>PMID: 33684578</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Artificial intelligence ; Brain ; Hemorrhagic stroke ; Noncontrast CT</subject><ispartof>World neurosurgery, 2021-06, Vol.150, p.e209-e217</ispartof><rights>2021 Elsevier Inc.</rights><rights>Copyright © 2021 Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c356t-666bab7de269afe2dc218d508570bcb1c781d3c61bd6fb1ed0f913a565c75bc23</citedby><cites>FETCH-LOGICAL-c356t-666bab7de269afe2dc218d508570bcb1c781d3c61bd6fb1ed0f913a565c75bc23</cites><orcidid>0000-0003-3147-181X ; 0000-0003-4500-7954 ; 0000-0001-6456-8445 ; 0000-0002-9117-1563</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.wneu.2021.02.134$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33684578$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Rava, Ryan A.</creatorcontrib><creatorcontrib>Seymour, Samantha E.</creatorcontrib><creatorcontrib>LaQue, Meredith E.</creatorcontrib><creatorcontrib>Peterson, Blake A.</creatorcontrib><creatorcontrib>Snyder, Kenneth V.</creatorcontrib><creatorcontrib>Mokin, Maxim</creatorcontrib><creatorcontrib>Waqas, Muhammad</creatorcontrib><creatorcontrib>Hoi, Yiemeng</creatorcontrib><creatorcontrib>Davies, Jason M.</creatorcontrib><creatorcontrib>Levy, Elad I.</creatorcontrib><creatorcontrib>Siddiqui, Adnan H.</creatorcontrib><creatorcontrib>Ionita, Ciprian N.</creatorcontrib><title>Assessment of an Artificial Intelligence Algorithm for Detection of Intracranial Hemorrhage</title><title>World neurosurgery</title><addtitle>World Neurosurg</addtitle><description>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.
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.
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.
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.</description><subject>Artificial intelligence</subject><subject>Brain</subject><subject>Hemorrhagic stroke</subject><subject>Noncontrast CT</subject><issn>1878-8750</issn><issn>1878-8769</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kD1PwzAURS0Eoqj0DzCgjCwN_mgcR2KpykcrVWKBicFy7JfWVRIXOwHx73HU0hEvz8O5V-8dhG4ITgkm_H6XfrfQpxRTkmKaEjY7Q1dE5GIqcl6cn_4ZHqFJCDscHyMzkbNLNGKMi1mWiyv0MQ8BQmig7RJXJapN5r6zldVW1cmq7aCu7QZaDcm83jhvu22TVM4nj9CB7qxrh1TkvNJetUNoCY3zfqs2cI0uKlUHmBznGL0_P70tltP168tqMV9PNct4N-Wcl6rMDVBeqAqo0ZQIk2GR5bjUJdG5IIZpTkrDq5KAwVVBmMp4pvOs1JSN0d2hd-_dZw-hk40NOm6uWnB9kHRWFKygtBARpQdUexeCh0ruvW2U_5EEy8Gr3MnBqxy8Skxl9BpDt8f-vmzAnCJ_FiPwcAAgXvllwcug7SDNWB8tSePsf_2_h66Klw</recordid><startdate>20210601</startdate><enddate>20210601</enddate><creator>Rava, Ryan A.</creator><creator>Seymour, Samantha E.</creator><creator>LaQue, Meredith E.</creator><creator>Peterson, Blake A.</creator><creator>Snyder, Kenneth V.</creator><creator>Mokin, Maxim</creator><creator>Waqas, Muhammad</creator><creator>Hoi, Yiemeng</creator><creator>Davies, Jason M.</creator><creator>Levy, Elad I.</creator><creator>Siddiqui, Adnan H.</creator><creator>Ionita, Ciprian N.</creator><general>Elsevier Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-3147-181X</orcidid><orcidid>https://orcid.org/0000-0003-4500-7954</orcidid><orcidid>https://orcid.org/0000-0001-6456-8445</orcidid><orcidid>https://orcid.org/0000-0002-9117-1563</orcidid></search><sort><creationdate>20210601</creationdate><title>Assessment of an Artificial Intelligence Algorithm for Detection of Intracranial Hemorrhage</title><author>Rava, Ryan A. ; Seymour, Samantha E. ; LaQue, Meredith E. ; Peterson, Blake A. ; Snyder, Kenneth V. ; Mokin, Maxim ; Waqas, Muhammad ; Hoi, Yiemeng ; Davies, Jason M. ; Levy, Elad I. ; Siddiqui, Adnan H. ; Ionita, Ciprian N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c356t-666bab7de269afe2dc218d508570bcb1c781d3c61bd6fb1ed0f913a565c75bc23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Artificial intelligence</topic><topic>Brain</topic><topic>Hemorrhagic stroke</topic><topic>Noncontrast CT</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rava, Ryan A.</creatorcontrib><creatorcontrib>Seymour, Samantha E.</creatorcontrib><creatorcontrib>LaQue, Meredith E.</creatorcontrib><creatorcontrib>Peterson, Blake A.</creatorcontrib><creatorcontrib>Snyder, Kenneth V.</creatorcontrib><creatorcontrib>Mokin, Maxim</creatorcontrib><creatorcontrib>Waqas, Muhammad</creatorcontrib><creatorcontrib>Hoi, Yiemeng</creatorcontrib><creatorcontrib>Davies, Jason M.</creatorcontrib><creatorcontrib>Levy, Elad I.</creatorcontrib><creatorcontrib>Siddiqui, Adnan H.</creatorcontrib><creatorcontrib>Ionita, Ciprian N.</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>World neurosurgery</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rava, Ryan A.</au><au>Seymour, Samantha E.</au><au>LaQue, Meredith E.</au><au>Peterson, Blake A.</au><au>Snyder, Kenneth V.</au><au>Mokin, Maxim</au><au>Waqas, Muhammad</au><au>Hoi, Yiemeng</au><au>Davies, Jason M.</au><au>Levy, Elad I.</au><au>Siddiqui, Adnan H.</au><au>Ionita, Ciprian N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessment of an Artificial Intelligence Algorithm for Detection of Intracranial Hemorrhage</atitle><jtitle>World neurosurgery</jtitle><addtitle>World Neurosurg</addtitle><date>2021-06-01</date><risdate>2021</risdate><volume>150</volume><spage>e209</spage><epage>e217</epage><pages>e209-e217</pages><issn>1878-8750</issn><eissn>1878-8769</eissn><abstract>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.
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.
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.
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.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>33684578</pmid><doi>10.1016/j.wneu.2021.02.134</doi><orcidid>https://orcid.org/0000-0003-3147-181X</orcidid><orcidid>https://orcid.org/0000-0003-4500-7954</orcidid><orcidid>https://orcid.org/0000-0001-6456-8445</orcidid><orcidid>https://orcid.org/0000-0002-9117-1563</orcidid></addata></record> |
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subjects | Artificial intelligence Brain Hemorrhagic stroke Noncontrast CT |
title | Assessment of an Artificial Intelligence Algorithm for Detection of Intracranial Hemorrhage |
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