[论文解读] Using artificial intelligence to detect chest X-rays with no significant findings in a primary health care setting in Oulu, Finland

Tommi Keski-Filppula, Marko Nikki+3 more|arXiv (Cornell University)|May 17, 2022

Radiomics and Machine Learning in Medical Imaging被引用 7

一句话总结

本研究评估了一款商用人工智能系统在芬兰基层医疗环境中检测正常（无显著异常）胸部X光片的表现。通过对9,579例回顾性病例分析，该AI系统在识别正常影像方面实现了99.8%的敏感度和36.4%的特异度，仅出现九例假阴性结果——且无一涉及危及生命的发现——表明其具有极高的安全性，并有潜力显著减轻基层医疗影像中的放射科医生工作负担。

ABSTRACT

Objectives: To assess the use of artificial intelligence-based software in ruling out chest X-ray cases, with no significant findings in a primary health care setting. Methods: In this retrospective study, a commercially available artificial intelligence (AI) software was used to analyse 10 000 chest X-rays of Finnish primary health care patients. In studies with a mismatch between an AI normal report and the original radiologist report, a consensus read by two board-certified radiologists was conducted to make the final diagnosis. Results: After the exclusion of cases not meeting the study criteria, 9579 cases were analysed by AI. Of these cases, 4451 were considered normal in the original radiologist report and 4644 after the consensus reading. The number of cases correctly found nonsignificant by AI was 1692 (17.7% of all studies and 36.4% of studies with no significant findings). After the consensus read, there were nine confirmed false-negative studies. These studies included four cases of slightly enlarged heart size, four cases of slightly increased pulmonary opacification and one case with a small unilateral pleural effusion. This gives the AI a sensitivity of 99.8% (95% CI= 99.65-99.92) and specificity of 36.4 % (95% CI= 35.05-37.84) for recognising significant pathology on a chest X-ray. Conclusions: AI was able to correctly rule out 36.4% of chest X-rays with no significant findings of primary health care patients, with a minimal number of false negatives that would lead to effectively no compromise on patient safety. No critical findings were missed by the software.

研究动机与目标

• 评估基于人工智能的软件在基层医疗环境中识别无显著异常胸部X光片的性能。
• 评估人工智能在排除正常病例时的安全性与准确性，最大限度降低漏诊严重病变的风险。
• 将人工智能结果与原始放射科医生报告及两名具备资质的放射科医生达成的共识读片结果进行比较，以确定诊断的可靠性。
• 量化人工智能系统在真实基层医疗人群中检测正常放射影像表现的敏感度与特异度。
• 识别假阴性病例，并评估人工智能系统是否遗漏了关键病变。

提出的方法

• 对芬兰奥卢地区基层医疗患者拍摄的10,000张胸部X光片进行回顾性分析。
• 使用一款商用人工智能软件，依据预设算法将每张X光片分类为正常或异常。
• 对人工智能输出与原始放射科医生报告存在差异的病例，由两名具备资质的放射科医生进行共识读片，以确定最终诊断。
• 采用敏感度、特异度及95%置信区间对人工智能性能进行统计分析。
• 排除不符合研究标准的病例（如数据不全、非诊断性影像等），以确保数据质量。
• 最终评估聚焦于人工智能正确识别正常影像的能力及其对假阴性病例的检测能力。

实验结果

研究问题

• RQ1 该人工智能系统在基层医疗环境中检测无显著异常胸部X光片的敏感度与特异度是多少？
• RQ2 该人工智能系统产生了多少例假阴性病例？其漏诊的病变类型为何？
• RQ3 在无显著放射学异常的情况下，人工智能系统是否遗漏了任何关键或危及生命的病变？
• RQ4 与原始放射科医生报告相比，人工智能系统在两名专家放射科医生达成共识读片结果中的表现如何？
• RQ5 在不损害患者安全的前提下，人工智能系统在基层医疗环境中能多大程度上安全地排除正常病例？

主要发现

• 在共识读片后，人工智能系统正确识别出4,644例无显著异常病例中的1,692例，敏感度为99.8%（95%置信区间：99.65–99.92）。
• 识别正常病例的特异度为36.4%（95%置信区间：35.05–37.84），表明人工智能仅将相对较低比例的真实正常病例识别为正常。
• 经共识读片后仅发现九例假阴性病例，包括四例心脏轻度增大、四例肺部轻微密度增高，以及一例单侧少量胸腔积液。
• 人工智能未遗漏任何关键或危及生命的病变，表明其在临床筛查中具有出色的安全部表现。
• 人工智能系统在排除正常病例方面表现出高度可靠性，漏诊严重病变的风险极低。
• 结果表明，人工智能可安全地用于基层医疗环境中的胸部X光片初步筛查，有助于减轻放射科医生的工作负担，同时保持高水平的诊断安全性。