Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Emergency Medicine Research Oxford, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
BMJ Open. 2024 Feb 12;14(2):e079824. doi: 10.1136/bmjopen-2023-079824.
A non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investigation requested in the emergency department. Advances in computer vision have led to development of several artificial intelligence (AI) tools to detect abnormalities on NCCTH. These tools are intended to provide clinical decision support for clinicians, rather than stand-alone diagnostic devices. However, validation studies mostly compare AI performance against radiologists, and there is relative paucity of evidence on the impact of AI assistance on other healthcare staff who review NCCTH in their daily clinical practice.
A retrospective data set of 150 NCCTH will be compiled, to include 60 control cases and 90 cases with intracranial haemorrhage, hypodensities suggestive of infarct, midline shift, mass effect or skull fracture. The intracranial haemorrhage cases will be subclassified into extradural, subdural, subarachnoid, intraparenchymal and intraventricular. 30 readers will be recruited across four National Health Service (NHS) trusts including 10 general radiologists, 15 emergency medicine clinicians and 5 CT radiographers of varying experience. Readers will interpret each scan first without, then with, the assistance of the qER EU 2.0 AI tool, with an intervening 2-week washout period. Using a panel of neuroradiologists as ground truth, the stand-alone performance of qER will be assessed, and its impact on the readers' performance will be analysed as change in accuracy (area under the curve), median review time per scan and self-reported diagnostic confidence. Subgroup analyses will be performed by reader professional group, reader seniority, pathological finding, and neuroradiologist-rated difficulty.
The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved 13 December 2022). The use of anonymised retrospective NCCTH has been authorised by Oxford University Hospitals. The results will be presented at relevant conferences and published in a peer-reviewed journal.
NCT06018545.
非对比 CT 头部扫描(NCCTH)是急诊科最常见的横断面成像检查。计算机视觉的进步促使开发了几种人工智能(AI)工具来检测 NCCTH 上的异常。这些工具旨在为临床医生提供临床决策支持,而不是独立的诊断设备。然而,验证研究大多将 AI 性能与放射科医生进行比较,并且关于 AI 辅助对在日常临床实践中审查 NCCTH 的其他医疗保健人员的影响的证据相对较少。
将编制 150 例 NCCTH 的回顾性数据集,包括 60 例对照病例和 90 例颅内出血、提示梗死的低密区、中线移位、肿块效应或颅骨骨折病例。颅内出血病例将分为硬膜外、硬膜下、蛛网膜下腔、脑实质内和脑室内出血。将招募来自四个国民保健服务(NHS)信托的 30 名读者,包括 10 名普通放射科医生、15 名急诊医生和 5 名具有不同经验的 CT 放射技师。读者将首先在没有 qER EU 2.0 AI 工具的情况下,然后在使用该工具的情况下,对每个扫描进行解释,间隔 2 周的洗脱期。使用一组神经放射科医生作为金标准,评估 qER 的独立性能,并分析其对读者性能的影响,包括准确性(曲线下面积)、每扫描的中位审查时间和自我报告的诊断信心的变化。将按读者专业组、读者资历、病理发现和神经放射科医生评定的难度进行亚组分析。
该研究已获得英国医疗保健研究管理局(IRAS 310995,2022 年 12 月 13 日批准)的批准。牛津大学医院已授权使用匿名回顾性 NCCTH。研究结果将在相关会议上进行介绍,并发表在同行评议的期刊上。
NCT06018545。