Evaluation of an artificial intelligence-based software device for detection of intracranial haemorrhage in teleradiology practice

Abstract

Objectives Artificial Intelligence (AI) algorithms have the potential to assist radiologists in the reporting of head CT scans. We investigated the performance of an AI-based software device used in a large teleradiology practice for intracranial haemorrhage (ICH) detection. Methods A randomly selected subset of all noncontrast CT head (NCCTH) scans from patients aged ≥ 18 years referred for urgent teleradiology reporting from 44 different hospitals within the UK over a 4-month period was considered for this evaluation. 30 auditing radiologists evaluated the NCCTH scans and the AI output retrospectively. Agreement between AI and auditing radiologists is reported along with failure analysis. Results A total of 1315 NCCTH scans from as many distinct patients were evaluated. 112 (8.5%) scans had ICH. Overall agreement, positive percent agreement, negative percent agreement, and Gwet’s AC1 of AI with radiologists were found to be 93.5% (95% CI: 92.1–94.8), 85.7% (77.8–91.6), 94.3% (92.8–95.5) and 0.92 (0.90–0.94) respectively in detecting ICH. 9 out of 16 false negative outcomes were due to missed subarachnoid haemorrhages and these were predominantly subtle haemorrhages. The most common reason for false positive results was due to motion artefacts. Conclusions AI demonstrated very good agreement with the radiologists in the detection of ICH.