[论文解读] Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis

Ophir Gozes, Maayan Frid-Adar+6 more|arXiv (Cornell University)|Mar 10, 2020

COVID-19 diagnosis using AI被引用 38

一句话总结

论文提出基于AI的自动化CT分析用于检测、量化和追踪COVID-19，在国际数据上实现高诊断性能，并引入用于疾病进展的Corona分数。

ABSTRACT

Purpose: Develop AI-based automated CT image analysis tools for detection, quantification, and tracking of Coronavirus; demonstrate they can differentiate coronavirus patients from non-patients. Materials and Methods: Multiple international datasets, including from Chinese disease-infected areas were included. We present a system that utilizes robust 2D and 3D deep learning models, modifying and adapting existing AI models and combining them with clinical understanding. We conducted multiple retrospective experiments to analyze the performance of the system in the detection of suspected COVID-19 thoracic CT features and to evaluate evolution of the disease in each patient over time using a 3D volume review, generating a Corona score. The study includes a testing set of 157 international patients (China and U.S). Results: Classification results for Coronavirus vs Non-coronavirus cases per thoracic CT studies were 0.996 AUC (95%CI: 0.989-1.00) ; on datasets of Chinese control and infected patients. Possible working point: 98.2% sensitivity, 92.2% specificity. For time analysis of Coronavirus patients, the system output enables quantitative measurements for smaller opacities (volume, diameter) and visualization of the larger opacities in a slice-based heat map or a 3D volume display. Our suggested Corona score measures the progression of disease over time. Conclusion: This initial study, which is currently being expanded to a larger population, demonstrated that rapidly developed AI-based image analysis can achieve high accuracy in detection of Coronavirus as well as quantification and tracking of disease burden.

研究动机与目标

• 开发基于AI的自动化CT影像分析工具，用于检测、量化和追踪Coronavirus。
• 使用深度学习在胸部CT数据上将Coronavirus患者与非患者区分开来。
• 将稳健的2D和3D模型与临床理解相结合，以评估随时间的疾病演变。
• 在来自国际来源的回顾性数据上评估所提出的系统以展示性能。

提出的方法

• 利用从现有AI架构改编的稳健2D和3D深度学习模型。
• 将临床知识融入模型开发。
• 进行回顾性实验以评估对疑似COVID-19 CT特征的检测。
• 使用3D体积评估进行按患者的时序演变分析，以生成Corona分数。

实验结果

研究问题

• RQ1 基于AI的CT分析是否能够在胸部CT扫描中准确区分COVID-19与非COVID-19病例？
• RQ2 系统是否能够随时间在单个患者中量化并可视化疾病负担（体积、直径）？
• RQ3 Corona分数是否能在纵向扫描中可靠地反映疾病进展？
• RQ4 模型在包括来自中国和美国的数据的国际数据集上的表现如何？

主要发现

• 分类性能：在测试数据集上，Coronavirus vs Non-coronavirus病例的AUC为0.996（95% CI: 0.989-1.00）
• 可能的工作点，灵敏度98.2%，特异性92.2%
• 该系统支持对较小病灶的定量测量，并通过基于切片的热力图或3D显示可视化较大病灶
• 引入Corona分数以量化随时间的疾病进展。