Using an artificial intelligence software improves emergency medicine physician intracranial haemorrhage detection to radiologist levels

Abstract

BackgroundTools to increase the turnaround speed and accuracy of imaging reports could positively influence ED logistics. The Caire ICH is an artificial intelligence (AI) software developed for ED physicians to recognise intracranial haemorrhages (ICHs) on non-contrast enhanced cranial CT scans to manage the clinical care of these patients in a timelier fashion.MethodsA dataset of 532 non-contrast cranial CT scans was reviewed by five board-certified emergency physicians (EPs) with an average of 14.8 years of practice experience. The scans were labelled in random order for the presence or absence of an ICH. If an ICH was detected, the reader further labelled all subtypes present (ie, epidural, subdural, subarachnoid, intraparenchymal and/or intraventricular haemorrhage). After a washout period, the five EPs reviewed again the scans individually with the assistance of Caire ICH. The mean accuracy of the EP readings with AI assistance was compared with the mean accuracy of three general radiologists reading the films individually. The final diagnosis (ie, ground truth) was adjudicated by a consensus of the radiologists after their individual readings.ResultsMean EP reader accuracy significantly increased by 6.20% (95% CI for the difference 5.10%–7.29%; p=0.0092) when using Caire ICH to detect an ICH. Mean accuracy of the EP cohort in detecting an ICH using Caire ICH was found to be more accurate than the radiologist cohort prior to discussion; this difference, however, was not statistically significant.ConclusionThe Caire ICH software significantly improved the accuracy and sensitivity of detecting an ICH by the EP to a level comparable to general radiologists. Further prospective research with larger numbers will be needed to understand the impact of Caire ICH on ED logistics and patient outcomes.

Evaluation of the impact of artificial intelligence-assisted image interpretation on the diagnostic performance of clinicians in identifying pneumothoraces on plain chest X-ray: a multi-case multi-reader study

“…10 The potential impact of AI-assisted image interpretation on the diagnostic accuracy of clinicians who are directly involved in interpreting images and delivering care to patients based on their findings in routine clinical practice therefore remains an important research question, and studies have begun to demonstrate potential benefits in this regard in an emergency medicine context. 11 Recent guidance from NICE 12 and AI-specific reporting guidelines have emphasised the importance of conducting evaluations in the clinical context in which they are likely to be cited, including feedback on usability and confidence directly from the intended users. [13][14][15][16][17]…”