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TRUVACE RECORD VERSION
record: TRV-2026-1107
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-16T06:55:35.813240Z
status: published
lens: trace
sector: health
headline: Retrospective comparison of three commercial artificial intelligence algorithms for detection of intracranial hemorrhage (ICH) in the emergency radiology department
dek: BackgroundSeveral commercial artificial intelligence (Al) algorithms are available for detecting intracranial hemorrhage (ICH), but independent clinical validation remains limited.PurposeTo compare three commercially available Al algorithms for ICH detection on non- contrast head computed tomography (NCHCT).Material and MethodsIn this retrospective study, 4027 consecutive NCHCT examinations from a large emergency hospital in southwest Sweden were analyzed. Three Al algorithms were applied, with one vendor disclo…
gain_title: Combining Aidoc AI with a human radiologist increased sensitivity for intracranial hemorrhage on non-contrast head CT to 96.0% while maintaining 99.4% specificity, detecting cases missed by radiologists alone.
problem_title: Performance across three commercial ICH detection algorithms varied substantially, with only one system showing clinically relevant accuracy, indicating limited independent validation for routine emergency use.
trace_subject: AI-assisted detection of intracranial hemorrhage on emergency non-contrast head CT
gain_reading: Combining Aidoc AI with a human radiologist increased sensitivity for intracranial hemorrhage on non-contrast head CT to 96.0% while maintaining 99.4% specificity, detecting cases missed by radiologists alone.
gain_evidence: A simulated mathematical combination of Aidoc with a human reader increased sensitivity to 96.0% while maintaining 99.4% specificity | Eight ICH cases missed by both radiologists were detected by at least one Al system | Aidoc performed best, with 90.3% sensitivity and 99.0% specificity
problem_reading: Performance across three commercial ICH detection algorithms varied substantially, with only one system showing clinically relevant accuracy, indicating limited independent validation for routine emergency use.
problem_evidence: Al performance varied substantially, with only one system demonstrating clinically relevant accuracy | independent clinical validation remains limited
quick_read: In a retrospective study of 4027 consecutive non-contrast head CT examinations from an emergency hospital in southwest Sweden, researchers compared three commercial AI algorithms for intracranial hemorrhage detection against reports from two radiologists, using two-tier consensus adjudication as the reference standard for 385 positive or discrepant cases.

By the September 2026 publication date, the observed combination of Aidoc with a human reader reached 96.0% sensitivity at 99.4% specificity, comparable to two radiologists, but the result relied on a simulated logical OR model assuming perfect dismissal of false positives, leaving prospective workflow impact, generalizability beyond a single center, and performance of the other two systems uncertain.
limitation: Human-AI performance was estimated using an idealized model that assumes perfect dismissal of false positives, and the data come from a single-center retrospective cohort, limiting generalizability to prospective clinical implementation.
tag: Dual reading
key_points: Retrospective analysis of 4027 consecutive NCHCT examinations from a large emergency hospital in southwest Sweden, with 3902 evaluable. | Reference standard was two-tier consensus adjudication after manual review of 385 positive or discrepant cases, confirming ICH in 176 cases (4.5% prevalence). | Three commercial AI algorithms were compared; Aidoc had highest standalone performance at 90.3% sensitivity and 99.0% specificity.
rundown: The study applied three commercial AI algorithms to 4027 consecutive non-contrast head CTs from a large emergency hospital in southwest Sweden; 3902 were evaluable and 3517 were consistently negative by all readers.

All 385 positive or discrepant cases underwent expert manual review with two-tier consensus adjudication as reference, confirming 176 ICH cases and excluding 209, with eight cases missed by both radiologists but flagged by AI.
sources:
- peer_reviewed | Acta Radiologica | https://doi.org/10.1177/02841851261484026 | 2026-09-15
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