目的:评估人工智能(AI)辅助的双重阅读系统,用于检测常规报告的胸部X光片的临床相关遗漏发现。
方法:在两个机构进行了回顾性研究,二级护理医院和三级转诊肿瘤中心。商用AI软件使用深度学习和自然语言处理算法对胸部X光片和放射科医生授权报告进行了比较分析。分别。外部放射科医生评估了AI检测到的图像和报告之间的差异发现的临床相关性。作为AI供应商提供的商业服务的一部分。选定的遗漏发现随后返回给该机构的放射科医生进行最终审查。
结果:总计,包括21,039例患者的25,104例胸片(平均年龄61.1岁±16.2[SD];10,436例男性)。AI软件检测到成像和报告之间的差异为21.1%(25,104中的5289)。经过外部放射科医生的检查,0.9%(5289中的47例)的病例被认为包含临床相关的遗漏发现。该机构的放射科医生确认了47例漏诊中的35例(74.5%)与临床相关(占所有病例的0.1%)。漏检结果包括肺结节(71.4%,25of35),气胸(17.1%,35人中的6人)和合并(11.4%,4of35)。
结论:AI辅助双读系统能够在报告授权后识别胸部X光片的遗漏发现。该方法需要外部放射科医生检查AI检测到的差异。放射科医师的临床相关遗漏发现的数量非常低。
结论:AI辅助的双阅读器工作流程被证明可以检测诊断错误,并且可以用作质量保证工具。尽管临床相关的遗漏发现很少见,鉴于胸部X线摄影的普遍使用,有潜在的影响。
结论:•评估了由人工智能支持的市售双读系统,以检测来自两个机构的胸部X光片(n=25,104)的报告错误。•在0.1%的胸片中发现了临床相关的漏诊结果,并包括未报告的肺结节。气胸和合并。•在报告授权后应用AI软件作为辅助阅读器可以帮助减少诊断错误,而不会中断放射科医师的阅读工作流程。然而,AI检测到的差异数量相当多,需要放射科医师进行审查以评估其相关性.
OBJECTIVE: To evaluate an artificial intelligence (AI)-assisted double reading system for detecting clinically relevant missed findings on routinely reported chest radiographs.
METHODS: A retrospective study was performed in two institutions, a secondary care hospital and tertiary referral oncology centre. Commercially available AI software performed a comparative analysis of chest radiographs and radiologists\' authorised reports using a deep learning and natural language processing algorithm, respectively. The AI-detected discrepant findings between images and reports were assessed for clinical relevance by an external radiologist, as part of the commercial service provided by the AI vendor. The selected missed findings were subsequently returned to the institution\'s radiologist for final review.
RESULTS: In total, 25,104 chest radiographs of 21,039 patients (mean age 61.1 years ± 16.2 [SD]; 10,436 men) were included. The AI software detected discrepancies between imaging and reports in 21.1% (5289 of 25,104). After review by the external radiologist, 0.9% (47 of 5289) of cases were deemed to contain clinically relevant missed findings. The institution\'s radiologists confirmed 35 of 47 missed findings (74.5%) as clinically relevant (0.1% of all cases). Missed findings consisted of lung nodules (71.4%, 25 of 35), pneumothoraces (17.1%, 6 of 35) and consolidations (11.4%, 4 of 35).
CONCLUSIONS: The AI-assisted double reading system was able to identify missed findings on chest radiographs after report authorisation. The approach required an external radiologist to review the AI-detected discrepancies. The number of clinically relevant missed findings by radiologists was very low.
CONCLUSIONS: The AI-assisted double reader workflow was shown to detect diagnostic errors and could be applied as a quality assurance tool. Although clinically relevant missed findings were rare, there is potential impact given the common use of chest radiography.
CONCLUSIONS: • A commercially available double reading system supported by artificial intelligence was evaluated to detect reporting errors in chest radiographs (n=25,104) from two institutions. • Clinically relevant missed findings were found in 0.1% of chest radiographs and consisted of unreported lung nodules, pneumothoraces and consolidations. • Applying AI software as a secondary reader after report authorisation can assist in reducing diagnostic errors without interrupting the radiologist\'s reading workflow. However, the number of AI-detected discrepancies was considerable and required review by a radiologist to assess their relevance.