论文标题
重新定义放射学质量保证（QA） - 通过限制不平等得分（Aquarius）的基于人工智能（AI）的质量检查
Re-defining Radiology Quality Assurance (QA) -- Artificial Intelligence (AI)-Based QA by Restricted Investigation of Unequal Scores (AQUARIUS)
论文作者
论文摘要
迫切需要简化放射学质量保证（QA）程序，以使其更好，更快。在这里，我们提出了一种新颖的方法，即通过限制不平等得分（Aquarius）的基于人工智能（AI）的质量保证，用于重新定义放射学质量质量质量质量质量质量学质量质量质量质量质量质量质量请访问QA，从而使人类的努力通过现有方法的数量降低了多个数量级。水瓶座通常包括在放射学报告中自动比较基于AI的图像分析与自然语言处理（NLP）。随后，人类专家审查了通常只有不一致读取的案例的一小部分。为了证明水瓶座的临床适用性，我们在1936年在一家大型学术医院进行了一项临床质量检查研究，对颅内出血（ICH）检测进行了临床研究。图像采集后，立即使用市售软件（AIDOC，Tel Aviv，以色列）自动分析扫描以自动分析ICH。在放射学家阅读工作列表中自动标记了AI（ICH-AI+）对ICH呈阳性的病例，其中将标记随机关闭，概率为50％。在最终放射学报告中使用Aquarius与NLP使用Aquarius，并针对仅29个不一致的案例的专家Neuroradiology评论，将人类质量检查的工作减少了98.5％，在那里我们发现6个非报告的真实ICH+案例，放射线医生的ICH检测率缺失了0.52％的0.52％和2.5％的ICH检测率，并且相应地分别为标志性的案例。我们得出的结论是，通过将基于AI的图像分析与基于NLP的基于NLP的案例进行针对人类专家审查的案例的预选，可以有效地识别放射学研究中的错过发现，并在混合人机互操作性方法中显着加速放射学QA QA QA QA。
There is an urgent need for streamlining radiology Quality Assurance (QA) programs to make them better and faster. Here, we present a novel approach, Artificial Intelligence (AI)-Based QUality Assurance by Restricted Investigation of Unequal Scores (AQUARIUS), for re-defining radiology QA, which reduces human effort by up to several orders of magnitude over existing approaches. AQUARIUS typically includes automatic comparison of AI-based image analysis with natural language processing (NLP) on radiology reports. Only the usually small subset of cases with discordant reads is subsequently reviewed by human experts. To demonstrate the clinical applicability of AQUARIUS, we performed a clinical QA study on Intracranial Hemorrhage (ICH) detection in 1936 head CT scans from a large academic hospital. Immediately following image acquisition, scans were automatically analyzed for ICH using a commercially available software (Aidoc, Tel Aviv, Israel). Cases rated positive for ICH by AI (ICH-AI+) were automatically flagged in radiologists' reading worklists, where flagging was randomly switched off with probability 50%. Using AQUARIUS with NLP on final radiology reports and targeted expert neuroradiology review of only 29 discordantly classified cases reduced the human QA effort by 98.5%, where we found a total of six non-reported true ICH+ cases, with radiologists' missed ICH detection rates of 0.52% and 2.5% for flagged and non-flagged cases, respectively. We conclude that AQUARIUS, by combining AI-based image analysis with NLP-based pre-selection of cases for targeted human expert review, can efficiently identify missed findings in radiology studies and significantly expedite radiology QA programs in a hybrid human-machine interoperability approach.
