RITeS (Real-world Independent Testing of e-ASPECTS) Collaboration
Expert stroke CT interpretation is not always available. Artificial intelligence (AI) software might assist less experienced readers. We compared a medical student with software assistance against expert interpretation for intracranial haemorrhage detection and associations with outcomes.
We included all RITeS patients (from 9 stroke studies) with acute intracranial haemorrhage on baseline CT. We tested diagnostic accuracy of student aided by e-ASPECTs software (v10.0, Brainomix, UK) and agreement with masked experts for presence of intracranial haemorrhage in 3 anatomical regions (intraparenchymal, extra-axial, or intraventricular) using Cohen’s kappa, κ. We sought associations of haemorrhage location (5 regions) with baseline Glasgow Coma Scale (GCS), and 90-day modified Rankin Scale (mRS) in multivariable ordinal logistic regression models including age, sex, number of affected regions (odds ratio, OR, 95% confidence interval).
From 651 patients (mean age 72 years, 53% male, median GCS 14), 628 CTs were analysed, 23 were excluded (not processed or contrast-enhanced). Not all cases had required data available. Student-software agreement with reference standard was κ=0.81, with diagnostic accuracy (n=314) of sensitivity 84.01%, specificity 97.88%, positive 96.67% and negative 85.92% predictive values. Using student-software results: worse GCS (n=388) was associated with intraventricular haemorrhage (OR=0.26, 0.15-0.46) and number of affected compartments (OR=0.61, 0.44-0.84); worse mRS (n=436) was associated with lobar, deep, posterior fossa, intraventricular haemorrhage, and number of affected compartments (OR range=2.22-6.92).
An inexperienced CT reader with AI software achieved substantial agreement and diagnostic accuracy with reference standard for brain haemorrhage location, and identified clinically relevant outcomes.
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