Should Artificial Intelligence Tell Radiologists Which Study to Read Next?
Should Artificial Intelligence Tell Radiologists Which Study to Read Next?
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人工智能应该告诉放射科医生接下来要阅读哪项研究吗？
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
M. Bhalla
中科院分区：
文献类型：
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作者：
S. O'Connor;M. Bhalla
T阅读工作列表显示了要解释的研究，通常组织起来以帮助放射科医生阅读接下来最紧急的研究。然而，有几个变量可用于确定任何给定的检查的紧迫性。许多列表仅使用成像位置（例如，急诊、住院、门诊）和由订购提供者基于患者症状的临床评估分配的优先级。订购提供者也可以对不需要立即翻译但必须快速获取以适应患者的其他预约的检查应用高优先级。虽然这种优先顺序可以作为技术人员确定执行检查的顺序的指南，但其作为阅读优先级指标的应用具有局限性，并且充满了错误。在某些实践中，有经验的成像技术人员可能会提醒放射科医生在扫描时注意到的成像发现，这些发现可能需要立即注意。可替代地，技术专家可以在将检查的优先级发送到放射科医生的阅读工作列表的同时编辑检查的优先级。然而，这种做法在全国并不普遍。在这个质量改进的时代，用人工智能（AI）等其他更复杂的工具来增强这些当前的实践可能是明智的。经过训练的人工智能算法可应用于关键时间依赖性场景的日常实践，例如中风和其他急性颅内发现，例如非中风原因引起的颅内出血（ICH）。中风是全球死亡和残疾的主要原因，每年约有14万美国人死于中风，几乎每4分钟就有一人死亡（1）。使用CT来区分缺血性和出血性卒中对于建立早期治疗方案和将患者分类到适当的管理是至关重要的。做出这一决定的速度极大地影响了治疗的成功，因此，上门诊断时间和放射科医生周转时间（达特）被纳入国家基准和不断发展的报销模式的绩效指标。CT采集的快速性可能会因缺乏适当的优先级而被否定，特别是如果卒中或ICH是非预期的，或者检查是从不太紧急的位置订购的，因为这些研究在放射科医师阅读列表上可能有更长的“保存期”或等待时间。可以根据订购时放置的特定标签或标签或在获得图像后根据请求适当地优先考虑检查（2）。这反过来又通过最小化阅读工作列表中检查的等待时间来加快解释。Osborne等人评价了将研究标记为“卒中方案”的影响，该方案将优先考虑研究而不是急诊CT检查。卒中方案CT的平均达特为6.5分钟，而急诊CT检查的平均TAT为17.3分钟。这一改善是由于“可取件时间”减少，与“放射科医师阅读时间”结合计算达特。阅读优先级可以由技术人员在考试结束时使用模式手动设置，该模式依赖于许多不同的变量。当这些优先级被分配数值时，放射科医生可以很容易地识别下一个最紧急的研究。使用这种方法，Gaskin等人报告了最紧急研究（危重、急诊科/紧急和住院/紧急）的中达特的显著改善，但急诊科研究的变化较小（5%），可能是因为标记有急诊科位置的检查默认首先由放射科医生解释（3）。人工智能工具有可能超越检查前已知的变量，并结合从图像本身收集的信息，以影响放射科医生及其工作流程。Arbabshirani等人首先评估了人工智能是否应该告诉放射科医生下一步要阅读哪个研究？
T reading worklist, which displays studies to be interpreted, is often organized to help radiologists read the most urgent study next. However, there are few variables available to determine the urgency of any given examination. Many lists simply use imaging location (eg, emergency, inpatient, outpatient) and the priority assigned by the ordering provider based on a clinical assessment of the patient’s symptoms. Ordering providers also may apply a high priority to an examination that does not require immediate interpretation but must be acquired quickly to accommodate a patient’s other appointments. Although this sort of prioritization serves as a guide to technologists to determine the order in which to perform the examinations, its application as a reading priority indicator has limitations and is fraught with errors. At some practices, an experienced imaging technologist may alert radiologists about imaging findings noticed while scanning that may need immediate if not urgent attention. Alternatively, the technologist may edit the priority level of the examination while sending it to the radiologists’ reading worklist. This practice, however, is not standard across the country. In this age of quality improvement, it may be prudent to augment these current practices with additional more sophisticated tools such as artificial intelligence (AI). A trained AI algorithm can be applied to everyday practice in critical time-dependent scenarios such as stroke and other acute intracranial findings such as intracranial hemorrhage (ICH) from nonstroke causes. A leading cause of death and disability worldwide, stroke kills approximately 140 000 Americans each year and is responsible for almost one death every 4 minutes (1). The use of CT to distinguish ischemic from hemorrhagic stroke is critical to establish early treatment options and to triage patients to appropriate management. The speed at which this determination is made greatly affects the success of treatment, such that door-to-diagnosis time and radiologist turnaround time (TAT) are included in national benchmarks and performance metrics of evolving reimbursement models. The rapidity of CT acquisition could be negated by the absence of appropriate prioritization, especially if stroke or ICH is unexpected or examinations are ordered from less urgent locations, as these studies could have a longer “shelf life” or wait time on a radiologist reading list. Examinations can be prioritized appropriately based on specific tags or labels placed at the time of ordering or by request after images have been obtained (2). This in turn expedites the interpretation by minimizing the wait time of an examination in the reading worklist. Osborne et al evaluated the impact of tagging studies as “stroke protocol,” which would prioritize them above emergent CT examinations. The average TAT for stroke protocol CT was 6.5 minutes compared with 17.3 minutes for emergent CT examinations. The improvement was the result of decreased “available-to-picked time,” which is combined with “radiologist reading time” to calculate TAT. Reading priorities can be set manually by technologists at end examination using a schema, which relies on many different variables. When these priorities are assigned numerical values, radiologists can easily identify the next most urgent study to read. Using this method, Gaskin et al reported significant improvement in the median TAT for the most urgent studies (critical, emergency department/ urgent, and inpatient/urgent), but the change was less (5%) in emergency department studies, presumably because examinations tagged with the emergency department location are by default interpreted first by radiologists (3). AI tools have the potential to go beyond variables known prior to an examination and incorporate information gleaned from the images themselves to impact radiologists and their workflow. Arbabshirani et al first evaluated Should Artificial Intelligence Tell Radiologists Which Study to Read Next?