论文标题
放射学中人工智能（TRU-AI）的跟踪结果和利用率：早期共vid-19
Tracking Results and Utilization of Artificial Intelligence (tru-AI) in Radiology: Early-Stage COVID-19 Pandemic Observations
论文作者
论文摘要
目的：引入一种跟踪结果和利用人工智能（TRU-AI）的方法。通过跟踪大规模利用率和AI结果数据，TRU-AI方法旨在计算替代物，以测量随着时间的流逝，例如在COVID-19-19大流行病期间颅内出血的患病率。方法：为了定量研究TRU-AI方法的临床适用性，我们分析了使用商业AI解决方案在头部CT上自动识别颅内出血（ICH）的服务请求。该软件通常用于基于AI的放射学家阅读清单的优先级，以减少具有紧急临床发现（例如ICH或肺栓塞）患者的周转时间。我们分析了n = 9,421个紧急情况下的数据，从2019年11月1日至6月2日，在2019年11月2日，在2019年11月2日相比，在一项主要的使用US医疗系统中获得的非对抗性头部CT研究，并比较从2019年11月1日到2020年2月29日至2920年2月29日，以及（ii）在COVID-19的大流行暴发期间的一个时期，2020年4月1日至30日。结果：尽管在（40.1 +/- 7.9期间（40.1 +/- 7.9）中，每天的CT扫描计数明显降低（44.4 +/- 7.6）比（44.4 +/- 7.6）在COVID-CORBER中比Covid-11的爆发了，这是一定的可能性。 Covid-19-19爆发（p <0.05），大约每天的ICH+病例比统计学上的预期多。结论：我们的结果表明，通过跟踪放射学中的大规模利用和AI结果数据，TRU-AI方法可以作为一种多功能的探索性工具来促进临床价值，旨在更好地了解与大流行有关的医疗保健的影响。
Objective: To introduce a method for tracking results and utilization of Artificial Intelligence (tru-AI) in radiology. By tracking both large-scale utilization and AI results data, the tru-AI approach is designed to calculate surrogates for measuring important disease-related observational quantities over time, such as the prevalence of intracranial hemorrhage during the COVID-19 pandemic outbreak. Methods: To quantitatively investigate the clinical applicability of the tru-AI approach, we analyzed service requests for automatically identifying intracranial hemorrhage (ICH) on head CT using a commercial AI solution. This software is typically used for AI-based prioritization of radiologists' reading lists for reducing turnaround times in patients with emergent clinical findings, such as ICH or pulmonary embolism.We analyzed data of N=9,421 emergency-setting non-contrast head CT studies at a major US healthcare system acquired from November 1, 2019 through June 2, 2020, and compared two observation periods, namely (i) a pre-pandemic epoch from November 1, 2019 through February 29, 2020, and (ii) a period during the COVID-19 pandemic outbreak, April 1-30, 2020. Results: Although daily CT scan counts were significantly lower during (40.1 +/- 7.9) than before (44.4 +/- 7.6) the COVID-19 outbreak, we found that ICH was more likely to be observed by AI during than before the COVID-19 outbreak (p<0.05), with approximately one daily ICH+ case more than statistically expected. Conclusion: Our results suggest that, by tracking both large-scale utilization and AI results data in radiology, the tru-AI approach can contribute clinical value as a versatile exploratory tool, aiming at a better understanding of pandemic-related effects on healthcare.
