Commercial AI Model Diagnostic Accuracy for Intracranial Large- and Medium-Vessel Occlusion in Emergency CT Angiography
Andersson, Henrik LU
; Hansen, Björn
LU
and Wassélius, Johan
LU
(2026)
In Radiology: Artificial Intelligence
- Abstract
The diagnostic accuracy of AIDOC-VO, the first commercial artificial intelligence tool for intracranial large-and medium-vessel occlusion (LVO/MeVO) detection on head-and-neck CT angiography (CTA), was evaluated in a multicenter emergency setting. A prospective diagnostic-accuracy study of 3,031 adult CT angiograms (mean age, 67.3 years ± 16.4 [SD]; 1,549 females) acquired March-July 2024 across a ten-hospital region was performed. The AI model was compared with clinical radiology reporting. Examinations flagged positive or doubt by either the AI model or report underwent blinded rereading for reference-standard establishment. Of 3,031 CT angiograms, valid AI model output was yielded for 2,804 (92.5%), of which 224/2,804 (8.0%) had... (More)
The diagnostic accuracy of AIDOC-VO, the first commercial artificial intelligence tool for intracranial large-and medium-vessel occlusion (LVO/MeVO) detection on head-and-neck CT angiography (CTA), was evaluated in a multicenter emergency setting. A prospective diagnostic-accuracy study of 3,031 adult CT angiograms (mean age, 67.3 years ± 16.4 [SD]; 1,549 females) acquired March-July 2024 across a ten-hospital region was performed. The AI model was compared with clinical radiology reporting. Examinations flagged positive or doubt by either the AI model or report underwent blinded rereading for reference-standard establishment. Of 3,031 CT angiograms, valid AI model output was yielded for 2,804 (92.5%), of which 224/2,804 (8.0%) had vessel occlusion (VO) on referencestandard reading. For VO detection within intended use (218/224), sensitivity was 81.7% (178/218) (clinical report: 81.2% [177/218]; P =.91), and specificity was 99.6% (2,569/2,580) (clinical report: 99.3% [2,561/2,580]; P =.12). LVO sensitivity was 92.8% (64/69) (clinical report: 87.0% [60/69]; P =.42) and MeVO sensitivity was 76.1% (121/159) (clinical report: 79.2% [126/159]; P =.55). The AI model identified VOs missed by radiologists in 42 examinations, for an enhanced detection rate of 18.8% (42/224; 15 per 1,000 CT angiograms), and generated 11 false alerts (3.9 per 1,000 CT angiograms). Performance did not differ significantly from clinical radiology reporting. ©RSNA, 2026.
(Less)
- author
- Andersson, Henrik
LU
; Hansen, Björn
LU
and Wassélius, Johan
LU
- organization
- publishing date
- 2026-04-15
- type
- Contribution to journal
- publication status
- epub
- subject
- in
- Radiology: Artificial Intelligence
- article number
- e250749
- publisher
- Radiological Society of North America Inc.
- external identifiers
-
- pmid:41983922
- ISSN
- 2638-6100
- DOI
- 10.1148/ryai.250749
- language
- English
- LU publication?
- yes
- id
- 0c7d1722-3a64-4916-9bb2-2176f9b99670
- date added to LUP
- 2026-04-16 16:45:56
- date last changed
- 2026-04-16 16:45:56
@article{0c7d1722-3a64-4916-9bb2-2176f9b99670,
abstract = {{<p>The diagnostic accuracy of AIDOC-VO, the first commercial artificial intelligence tool for intracranial large-and medium-vessel occlusion (LVO/MeVO) detection on head-and-neck CT angiography (CTA), was evaluated in a multicenter emergency setting. A prospective diagnostic-accuracy study of 3,031 adult CT angiograms (mean age, 67.3 years ± 16.4 [SD]; 1,549 females) acquired March-July 2024 across a ten-hospital region was performed. The AI model was compared with clinical radiology reporting. Examinations flagged positive or doubt by either the AI model or report underwent blinded rereading for reference-standard establishment. Of 3,031 CT angiograms, valid AI model output was yielded for 2,804 (92.5%), of which 224/2,804 (8.0%) had vessel occlusion (VO) on referencestandard reading. For VO detection within intended use (218/224), sensitivity was 81.7% (178/218) (clinical report: 81.2% [177/218]; P =.91), and specificity was 99.6% (2,569/2,580) (clinical report: 99.3% [2,561/2,580]; P =.12). LVO sensitivity was 92.8% (64/69) (clinical report: 87.0% [60/69]; P =.42) and MeVO sensitivity was 76.1% (121/159) (clinical report: 79.2% [126/159]; P =.55). The AI model identified VOs missed by radiologists in 42 examinations, for an enhanced detection rate of 18.8% (42/224; 15 per 1,000 CT angiograms), and generated 11 false alerts (3.9 per 1,000 CT angiograms). Performance did not differ significantly from clinical radiology reporting. ©RSNA, 2026.</p>}},
author = {{Andersson, Henrik and Hansen, Björn and Wassélius, Johan}},
issn = {{2638-6100}},
language = {{eng}},
month = {{04}},
publisher = {{Radiological Society of North America Inc.}},
series = {{Radiology: Artificial Intelligence}},
title = {{Commercial AI Model Diagnostic Accuracy for Intracranial Large- and Medium-Vessel Occlusion in Emergency CT Angiography}},
url = {{http://dx.doi.org/10.1148/ryai.250749}},
doi = {{10.1148/ryai.250749}},
year = {{2026}},
}