triage of urgent versus non-urgent non-contrast CT brain findings in emergency department patients using Harrison Enterprise CTB

Source article: Artificial Intelligence Triage of Urgent Versus Non-Urgent CT Brain Findings to Support Expedited Emergency Department Disposition: A Retrospective Validation Study

Objective Evaluate the diagnostic accuracy of an artificial intelligence (AI) model for identifying urgent computed tomography brain (CTB) findings in consecutive emergency department (ED) patients and estimate the proportion eligible for expedited disposition. Methods We retrospectively analysed 3424 consecutive non-contrast CTB scans from adults presenting to a quaternary ED in 2024. The AI model (Harrison Enterprise CTB) classified each scan as "urgent" or "non-urgent." The reference standard was the index co…

By the Truvace deskOctober 1, 20262 min read1 sourcePeer-reviewedTRV-2026-1238· cite & verify

Trace impact readingContested

P 65

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G 67

In brief

Researchers retrospectively tested an AI model that classifies non-contrast CT brain scans as urgent or non-urgent on 3424 consecutive adult scans from a quaternary emergency department in 2024. Against consultant radiologist reports as reference, the model achieved 85.4% sensitivity, 69.1% specificity and 98.3% NPV, with 64% of encounters identified as true negatives potentially eligible for expedited disposition.

The high NPV supports a safety rationale for a prospective trial to incorporate AI triage into ED workflows to speed disposition, but initial false negatives and reliance on a single-center retrospective design with report-based reference and unblinded post hoc re-adjudication leave uncertainty about real-world safety and generalizability.

Main points

Study analyzed 3424 consecutive non-contrast CTB scans from adults presenting to a quaternary ED in 2024.
Reference standard was the index consultant radiologist report alone, classified by two emergency physicians with blinded radiologist tiebreak, all blinded to AI output.
Initial sensitivity was 85.4% (95% CI 80.0%-89.0%) and specificity 69.1% (95% CI 67.0%-71.0%) with 253 (7.4%) urgent findings.
Post hoc re-adjudication of 37 false negatives by a single unblinded assessor reclassified 24 as non-urgent, raising sensitivity to 94.3% and NPV to 99.4%.

The gain

Retrospective validation of Harrison Enterprise CTB on 3424 ED scans showed 98.3% NPV for urgent findings, with 64% of encounters identified as true negatives potentially eligible for expedited disposition and no remaining false negatives requiring urgent intervention during the index presentation.

The problem

The AI model missed 37 scans initially classified as urgent, yielding 85.4% sensitivity, with 13 remaining small or subtle urgent findings after post hoc re-adjudication.

The rundown

The study evaluated Harrison Enterprise CTB on 3424 consecutive adult non-contrast CT brain scans from a quaternary ED in 2024, comparing AI classification of urgent versus non-urgent against consultant radiologist reports classified by emergency physicians blinded to AI output.

Initial results showed 253 urgent cases, sensitivity 85.4% and NPV 98.3%, with 64% true negatives potentially eligible for expedited disposition; post hoc unblinded re-adjudication of 37 false negatives reclassified 24 as non-urgent, leaving 13 small or subtle findings that did not require urgent intervention during the index presentation.

What this doesn’t fix

Retrospective single-center design using radiologist report alone as reference, and post hoc false-negative re-adjudication by a single unblinded assessor with access to medical record, limits generalizability before prospective trial.