Clinical relevance of intracranial stenosis as false-positive findings of a deep learning algorithm trained to detect large vessel occlusions: a retrospective cohort study of a supraregional stroke centre.

OBJECTIVES

This study aims to explore the ability to identify high-grade intracranial arterial stenosis (ICAS) by an artificial intelligence (AI) designed to detect large vessel occlusions (LVO) and the clinical relevance of these 'false positive' findings.

DESIGN

We are presenting a retrospective cohort study.

SETTING

The study was conducted at a supraregional stroke centre of an urban tertiary care provider.

PARTICIPANTS

Consecutive stroke cases treated between January 2023 and December 2023 of patients >18 years of both sexes and any ethnicity were eligible for inclusion. 934 patients (52.7% male) with a mean age of 71.7±13.6 years (25-101 years) were included.

INTERVENTIONS: CT angiographies were analysed by a deep learning algorithm for LVO detection of the anterior circulation. AI results were compared with radiology reports and secondary focused evaluation.

PRIMARY AND SECONDARY OUTCOME MEASURES: Diagnostic accuracies for ICAS detection by the AI were calculated.

RESULTS

Primary reports identified 30 ICAS and nine additional ICAS were detected during secondary evaluation (incidence 4.2%). The sensitivity of radiology reports was 77% (95% CI 0.61 to 0.89), the specificity 99% (95% CI 0.98 to 1.00), negative predictive value (NPV) 99% (95% CI 0.98 to 0.99) and positive predictive value (PPV) 79% (95% CI 0.65 to 0.88). The AI identified 13 of 39 ICAS correctly. 18 false positive cases (neither LVO nor ICAS) were flagged by the AI. The sensitivity of the algorithm was 33% (95% CI 0.19 to 0.50), the specificity 98% (95% CI 0.97 to 0.99), the NPV 97% (95% CI 0.96 to 0.98) and PPV 42% (95% CI 0.28 to 0.58).

CONCLUSION

Detection of high-grade ICAS by an algorithm trained to identify LVO is per se a false positive finding but occurred in 13 of 39 cases. Dedicated training for ICAS might lead to a beneficial tool during the diagnostic work-up for ischaemic stroke.

TRIAL REGISTRATION: German Register for Clinical Trials (DRKS: DRKS00034019 https://drks.de/search/de/trial/DRKS00034019).