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Congress: ECR25
Poster Number: C-18980
Type: Poster: EPOS Radiologist (scientific)
Authorblock: S. Romeijn1, J. Banken2, K. G. Van Leeuwen1; 1Utrecht/NL, 2Nijmegen/NL
Disclosures:
Stephan Romeijn: Nothing to disclose
Jimmy Banken: Nothing to disclose
Kicky Gerhilde Van Leeuwen: Nothing to disclose
Keywords: Artificial Intelligence, Computer applications, CT, Technology assessment, Haemorrhage
Purpose This study aims to evaluate the clinical value of commercially available AI products for intracranial hemorrhage (ICH) management by assessing the available scientific evidence. In addition, it seeks to indicate the current status, shed light on knowledge gaps, and assist in making informed decisions regarding their integration into clinical practice, considering their impact on diagnostic accuracy, workflow efficiency, patient outcomes, and overall healthcare system benefits.
Read more Methods and materials We identified all CE-marked AI products developed for intracranial hemorrhage (ICH) management using the Health AI Register (healthairegister.com). Peer-reviewed papers on the products were collected using the papers listed on the product pages on Health AI Register and through a PubMed search using the company and/or product name. Study design, population characteristics, outcome measures, disclosed vendor affiliations, were extracted from the articles. Outcome measures were categorized according to hierarchical model of efficacy of diagnostic imaging.  
Read more Results A total of 11 AI products for intracranial hemorrhage (ICH) management were identified, with 19 supporting scientific publications (Annalise Enterprise CTB, BrainScan CT, CINA-ICH, DeepCT, Deepstroke, Intracranial Hemorrhage (ICH)*, JBS-04K, qER, Rapid ICH, StrokeViewer, Viz ICH). Eleven out of the 19 publications were related to a single AI product, two products had no publications identified. Diagnostic accuracy efficacy (level 2) was addressed by 15 studies (Figure 1). Higher levels (3-6) demonstrating impact on the clinical workflow was addressed by 9...
Read more Conclusion AI applications for ICH management demonstrate high diagnostic accuracy, particularly in assisting radiologists. However, the broader impact on workflow and therapeutic outcomes remains unclear. Studies indicate potential benefits, but the lack of independent, higher-level efficacy research limits conclusions on their clinical utility, highlighting the need for robust validation and real-world clinical impact assessments.
Read more References Buchlak QD, Tang CHM, Seah JCY, Johnson A, Holt X, Bottrell GM, et al. Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy. European Radiology. 2024;34(2):810-22. Chekmeyan M, Baccei SJ, Garwood ER. Cross-Check QA: A Quality Assurance Workflow to Prevent Missed Diagnoses by Alerting Inadvertent Discordance Between the Radiologist and Artificial Intelligence in the Interpretation of High-Acuity CT Scans. Journal of the American College of Radiology. 2023;20(12):1225-30. Chien H-WC, Yang T-L, Juang W-C, Chen Y-YA, Li Y-CJ,...
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