Related Experiment Video
Updated: Sep 16, 2026
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
Published on: April 13, 2013
Prospective Shadow-Mode Evaluation of an Artificial Intelligence Tool for Intracranial Aneurysm Detection on CT
Shlomit Goldberg-Stein1, Maria X Sanmartin2, Rachel Saks3
1Northwell Health, New Hyde Park, New York; Department of Radiology, Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York; Director of Artificial Intelligence, Department of Radiology, Northwell Health, New Hyde Park, New York; Chair, Quality and Safety Committee, New York State Radiological Society; Co-Chair, ACR Recognized Center for Healthcare-AI (ARCH-AI) Learning Community Committee.
Objective:
To evaluate the operational performance of an FDA-cleared artificial intelligence (AI) algorithm for brain CT angiography (CTA) aneurysm detection and the incremental value of combining AI with radiologists.
Methods:
Prospective shadow-mode study of consecutive brain CTAs (November 7, 2023, to December 19, 2023) was performed with radiologists blinded to AI. Aidoc's (Tel Aviv, Israel) AI algorithm processed CTAs, and natural language processing extracted radiology report results. Discordances underwent neuroradiologist adjudication with AI unblinding. Performance metrics included AI:radiologist incremental detection ratio, relative enhanced detection rate (rEDR), gain-to-pain ratio (GPR), and number-needed-to-examine (NNE). Performance metrics are estimated under a hybrid reference standard wherein only discordances are adjudicated.
Results:
Among 3,856 CTAs, examination positive rate was 5.1% (195 of 3,856). Radiologist-AI concordance was 96.3% (3,714 of 3,856). Sensitivity of AI alone (0.846, 0.787-0.894) exceeded radiologist alone (0.718, 0.649-0.779), with similar specificity (radiologist: 0.985, 0.981-0.989; AI: 0.987, 0.983-0.991). AI surfaced additional aneurysms not identified by radiologists, yielding an rEDR of 39% (55 AI-only true-positives per 140 radiologist true-positives) and projecting a higher combined sensitivity for radiologists with AI. AI:radiologist incremental detection ratio was 1.83 (55 of 30), favorable for AI. Operationally, GPR was favorable at 1.20 (55 of 46), and NNE was 70.1 (3,856 of 55). Operational metrics were most favorable in inpatients (rEDR 78.3%, GPR 2.57, NNE 28.9) and emergency (rEDR 37.1%, GPR 1.0, NNE 85.3) and were unfavorable in outpatients (rEDR 14.1%, GPR 0.67, NNE 130.3). Most AI-only aneurysms were <3 mm (31 of 55, 56.4%); radiologist-only were mostly 3 to 5 mm (18 of 33, 54.5%).
Conclusions:
The AI tool demonstrated favorable operational performance, supporting clinical deployment in real-world practice, although setting-specific metrics were variable.
More Related Videos
06:59Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation
Published on: June 3, 2018
09:32Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging
Published on: December 9, 2021