Abstract Number: LBA25 Validating Artificial Intelligence Model to Optimize Radiologist Detection of Intracerebral Hemorrhage
Introduction Artificial intelligence (AI) has shown to be able to alert the radiologist to the presence of ischemic stroke secondary to large artery occlusion (LVO) as fast as 1–2 minutes from scan completion hence leading to faster diagnosis and treatment. In addition to acute LVO, AI has become in...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-03-01
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| Series: | Stroke: Vascular and Interventional Neurology |
| Online Access: | https://www.ahajournals.org/doi/10.1161/SVIN.03.suppl_1.LBA25 |
| _version_ | 1827925223654031360 |
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| author | Mona P Roshan Italo Linfante Thompson Antony Raihan Noman Jamie Clarke Seema Azim Sean Britton Kevin Abrams Charif Sidani |
| author_facet | Mona P Roshan Italo Linfante Thompson Antony Raihan Noman Jamie Clarke Seema Azim Sean Britton Kevin Abrams Charif Sidani |
| author_sort | Mona P Roshan |
| collection | DOAJ |
| description | Introduction Artificial intelligence (AI) has shown to be able to alert the radiologist to the presence of ischemic stroke secondary to large artery occlusion (LVO) as fast as 1–2 minutes from scan completion hence leading to faster diagnosis and treatment. In addition to acute LVO, AI has become increasingly used for various intracranial pathologies. In particular, accurate and timely detection of intracerebral hemorrhage (ICH) is crucial to provide prompt life‐saving interventions. Therefore, we aimed to validate a new AI application called Viz.ai ICH with the intent to improve diagnostic identification of suspected ICH. Methods We performed a retrospective database analysis of 4,203 consecutive non‐contrast brain CT reports between September 2021 to December 2021 within a single institution. The reports were made by experienced neuroradiologists who reviewed each case for the presence of ICH. Medical students reviewed the neuroradiologists’ reports and identified cases with positive findings for ICH. Each positive case was categorized based on subtype, timing, and size/volume via imaging review by a neuroradiologist. The Viz.ai ICH output was reviewed for positive cases by medical students. This AI model was validated by using descriptive analysis and assessing its diagnostic performance with Viz.ai ICH as the index test compared to the neuroradiologists’ interpretation as the gold standard. Results 387 of 4,203 non‐contrast brain CT reports were positive for ICH according to neuroradiologists. The overall sensitivity of Viz.ai ICH was 68%, specificity was 99%, positive predictive value (PPV) was 90%, and negative predictive value (NPV) was 97%. Subgroup analysis was performed based on hemorrhage subtypes of intraparenchymal, subarachnoid, subdural, and intraventricular. Sensitivities were calculated to be 86%, 57%, 56%, and 42% respectively. Further stratification revealed sensitivity improves with higher acuity and volume/size across all ICH subtypes. Meningioma was found to be a common false‐positive finding (3 of 22, 14%). Table 1 provides a summary of the results. Conclusions Our analysis seems to indicate that AI can accurately detect the presence of ICH particularly for large volume/size ICH. |
| first_indexed | 2024-03-13T05:22:28Z |
| format | Article |
| id | doaj.art-a6d0be7507ad4c53bc7ef57cf7cd8365 |
| institution | Directory Open Access Journal |
| issn | 2694-5746 |
| language | English |
| last_indexed | 2024-03-13T05:22:28Z |
| publishDate | 2023-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Stroke: Vascular and Interventional Neurology |
| spelling | doaj.art-a6d0be7507ad4c53bc7ef57cf7cd83652023-06-15T10:40:49ZengWileyStroke: Vascular and Interventional Neurology2694-57462023-03-013S110.1161/SVIN.03.suppl_1.LBA25Abstract Number: LBA25 Validating Artificial Intelligence Model to Optimize Radiologist Detection of Intracerebral HemorrhageMona P Roshan0Italo Linfante1Thompson Antony2Raihan Noman3Jamie Clarke4Seema Azim5Sean Britton6Kevin Abrams7Charif Sidani8Florida International University Herbert Wertheim College of Medicine Miami Florida United States of AmericaMiami Neuroscience Institute at Baptist Health of South Florida Miami Florida United States of AmericaFlorida International University Herbert Wertheim College of Medicine Miami Florida United States of AmericaFlorida International University Herbert Wertheim College of Medicine Miami Florida United States of AmericaUniversity of Miami Leonard M. Miller School of Medicine Miami Florida United States of AmericaFlorida International University Herbert Wertheim College of Medicine Miami Florida United States of AmericaFlorida State University Tallahassee Florida United States of AmericaRadiology Associates of South Florida and Baptist Health of South Florida Miami Florida United States of AmericaRadiology Associates of South Florida and Baptist Health of South Florida Miami Florida United States of AmericaIntroduction Artificial intelligence (AI) has shown to be able to alert the radiologist to the presence of ischemic stroke secondary to large artery occlusion (LVO) as fast as 1–2 minutes from scan completion hence leading to faster diagnosis and treatment. In addition to acute LVO, AI has become increasingly used for various intracranial pathologies. In particular, accurate and timely detection of intracerebral hemorrhage (ICH) is crucial to provide prompt life‐saving interventions. Therefore, we aimed to validate a new AI application called Viz.ai ICH with the intent to improve diagnostic identification of suspected ICH. Methods We performed a retrospective database analysis of 4,203 consecutive non‐contrast brain CT reports between September 2021 to December 2021 within a single institution. The reports were made by experienced neuroradiologists who reviewed each case for the presence of ICH. Medical students reviewed the neuroradiologists’ reports and identified cases with positive findings for ICH. Each positive case was categorized based on subtype, timing, and size/volume via imaging review by a neuroradiologist. The Viz.ai ICH output was reviewed for positive cases by medical students. This AI model was validated by using descriptive analysis and assessing its diagnostic performance with Viz.ai ICH as the index test compared to the neuroradiologists’ interpretation as the gold standard. Results 387 of 4,203 non‐contrast brain CT reports were positive for ICH according to neuroradiologists. The overall sensitivity of Viz.ai ICH was 68%, specificity was 99%, positive predictive value (PPV) was 90%, and negative predictive value (NPV) was 97%. Subgroup analysis was performed based on hemorrhage subtypes of intraparenchymal, subarachnoid, subdural, and intraventricular. Sensitivities were calculated to be 86%, 57%, 56%, and 42% respectively. Further stratification revealed sensitivity improves with higher acuity and volume/size across all ICH subtypes. Meningioma was found to be a common false‐positive finding (3 of 22, 14%). Table 1 provides a summary of the results. Conclusions Our analysis seems to indicate that AI can accurately detect the presence of ICH particularly for large volume/size ICH.https://www.ahajournals.org/doi/10.1161/SVIN.03.suppl_1.LBA25 |
| spellingShingle | Mona P Roshan Italo Linfante Thompson Antony Raihan Noman Jamie Clarke Seema Azim Sean Britton Kevin Abrams Charif Sidani Abstract Number: LBA25 Validating Artificial Intelligence Model to Optimize Radiologist Detection of Intracerebral Hemorrhage Stroke: Vascular and Interventional Neurology |
| title | Abstract Number: LBA25 Validating Artificial Intelligence Model to Optimize Radiologist Detection of Intracerebral Hemorrhage |
| title_full | Abstract Number: LBA25 Validating Artificial Intelligence Model to Optimize Radiologist Detection of Intracerebral Hemorrhage |
| title_fullStr | Abstract Number: LBA25 Validating Artificial Intelligence Model to Optimize Radiologist Detection of Intracerebral Hemorrhage |
| title_full_unstemmed | Abstract Number: LBA25 Validating Artificial Intelligence Model to Optimize Radiologist Detection of Intracerebral Hemorrhage |
| title_short | Abstract Number: LBA25 Validating Artificial Intelligence Model to Optimize Radiologist Detection of Intracerebral Hemorrhage |
| title_sort | abstract number lba25 validating artificial intelligence model to optimize radiologist detection of intracerebral hemorrhage |
| url | https://www.ahajournals.org/doi/10.1161/SVIN.03.suppl_1.LBA25 |
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