背景:人工智能(AI)辅助图像解释是临床创新的快速发展领域。迄今为止,大多数研究都集中在与放射科医生相比的AI辅助算法的性能上,而不是评估算法对经常在常规临床实践中进行初始图像解释的临床医生的影响。这项研究评估了AI辅助图像解释对一线急性护理临床医生检测气胸(PTX)的诊断性能的影响。
方法:在2021年10月至2022年1月之间进行了多中心盲多病例多读者研究。这项在线研究招募了来自六个不同临床专业的18名临床医生读者,资历不同,在英国的四家医院。该研究包括395张普通CXR图像,189个PTX阳性和206个阴性。参考标准是两名胸部放射科医师的共识意见,第三名担任仲裁员。将通用电气医疗保健重症监护套件(GEHCCCS)PTX算法应用于最终数据集。读者在没有人工智能帮助的情况下单独解释数据集,记录是否存在PTX和置信度。在“冲刷”期间之后,重复这一过程,包括AI输出.
结果:用于检测或排除PTX的算法的性能分析揭示0.939的总体AUROC。总体读者灵敏度增加了11.4%(95%CI4.8,18.0,p=0.002),从66.8%(95%CI57.3,76.2)增加到78.1%(95%CI72.2,84.0,p=0.002),无AI的特异性为93.9%(95%CI90.9,97.0),为95.8%(95%CI93.7,97.9,p=0.247)。初级读者亚组表现出最大的改善,为21.7%(95%CI10.9,32.6),从56.0%(95%CI37.7,74.3)增加到77.7%(95%CI65.8,89.7,p<0.01)。
结论:该研究表明,AI辅助图像解释显着提高了临床医生检测PTX的诊断准确性,特别是受益于经验较少的从业者。虽然整体解释时间保持不变,人工智能的使用提高了诊断的信心和灵敏度,尤其是初级临床医生。这些发现强调了AI在急性护理环境中支持技术较低的临床医生的潜力。
BACKGROUND: Artificial intelligence (AI)-assisted image interpretation is a fast-developing area of clinical innovation. Most research to date has focused on the performance of AI-assisted algorithms in comparison with that of radiologists rather than evaluating the algorithms\' impact on the clinicians who often undertake initial image interpretation in routine clinical practice. This study assessed the impact of AI-assisted image interpretation on the diagnostic performance of frontline acute care clinicians for the detection of pneumothoraces (PTX).
METHODS: A multicentre blinded multi-
case multi-reader study was conducted between October 2021 and January 2022. The online study recruited 18 clinician readers from six different clinical specialties, with differing levels of seniority, across four English hospitals. The study included 395 plain CXR images, 189 positive for PTX and 206 negative. The reference standard was the consensus opinion of two thoracic radiologists with a third acting as arbitrator. General Electric Healthcare Critical Care Suite (GEHC CCS) PTX algorithm was applied to the final dataset. Readers individually interpreted the dataset without AI assistance, recording the presence or absence of a PTX and a confidence rating. Following a \'washout\' period, this process was repeated including the AI output.
RESULTS: Analysis of the performance of the algorithm for detecting or ruling out a PTX revealed an overall AUROC of 0.939. Overall reader sensitivity increased by 11.4% (95% CI 4.8, 18.0, p=0.002) from 66.8% (95% CI 57.3, 76.2) unaided to 78.1% aided (95% CI 72.2, 84.0, p=0.002), specificity 93.9% (95% CI 90.9, 97.0) without AI to 95.8% (95% CI 93.7, 97.9, p=0.247). The junior reader subgroup showed the largest improvement at 21.7% (95% CI 10.9, 32.6), increasing from 56.0% (95% CI 37.7, 74.3) to 77.7% (95% CI 65.8, 89.7, p<0.01).
CONCLUSIONS: The study indicates that AI-assisted image interpretation significantly enhances the diagnostic accuracy of clinicians in detecting PTX, particularly benefiting less experienced practitioners. While overall interpretation time remained unchanged, the use of AI improved diagnostic confidence and sensitivity, especially among junior clinicians. These findings underscore the potential of AI to support less skilled clinicians in acute care settings.