Artificial Intelligence assisted detection of large vessel occlusion on CT angiography in acute stroke patients: A multi-reader multi-case study

Objectives: The impact of artificial intelligence software (e-CTA, Brainomix) on clinical decision-making in patients with suspected acute ischemic stroke was assessed.

Methods: A retrospective, multi-reader-multi-case crossover design compared readers' performance with versus without software support. Twenty cases were included, 10 with large vessel occlusion (LVO) and 10 without LVO. Twenty one NHS clinicians, representing intended software users ranging in experience, conducted two sessions (washout period >2 weeks). Outcome measures included LVO detection, collateral scoring, diagnosis, treatment decision, time taken and confidence.

Results: Sensitivity, specificity and accuracy of LVO detection improved with imaging software, with increased confidence and reduced time taken. There was no significant difference in collateral scoring or diagnoses.

Conclusion: e-CTA can improve performance of NHS clinicians when interpreting acute stroke imaging.