Wang Winston T, Zhang Charlotte L, Wei Kang, Sang Ye, Shen Jun, Wang Guangyu, Lozano Alexander X
Department of Materials Science & Engineering, Stanford University, Stanford, CA 94305, USA.
Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
Precis Clin Med. 2020 Dec 28;4(1):62-69. doi: 10.1093/pcmedi/pbaa040. eCollection 2021 Mar.
Within COVID-19 there is an urgent unmet need to predict at the time of hospital admission which COVID-19 patients will recover from the disease, and how fast they recover to deliver personalized treatments and to properly allocate hospital resources so that healthcare systems do not become overwhelmed. To this end, we have combined clinically salient CT imaging data synergistically with laboratory testing data in an integrative machine learning model to predict organ-specific recovery of patients from COVID-19. We trained and validated our model in 285 patients on each separate major organ system impacted by COVID-19 including the renal, pulmonary, immune, cardiac, and hepatic systems. To greatly enhance the speed and utility of our model, we applied an artificial intelligence method to segment and classify regions on CT imaging, from which interpretable data could be directly fed into the predictive machine learning model for overall recovery. Across all organ systems we achieved validation set area under the receiver operator characteristic curve (AUC) values for organ-specific recovery ranging from 0.80 to 0.89, and significant overall recovery prediction in Kaplan-Meier analyses. This demonstrates that the synergistic use of an artificial intelligence (AI) framework applied to CT lung imaging and a machine learning model that integrates laboratory test data with imaging data can accurately predict the overall recovery of COVID-19 patients from baseline characteristics.
在新冠肺炎疫情期间，存在一个迫切未得到满足的需求，即在患者入院时预测哪些新冠肺炎患者能够康复，以及他们康复的速度有多快，以便提供个性化治疗并合理分配医院资源，从而避免医疗系统不堪重负。为此，我们在一个整合的机器学习模型中，将具有临床意义的CT成像数据与实验室检测数据进行协同整合，以预测新冠肺炎患者特定器官的恢复情况。我们在受新冠肺炎影响的每个单独的主要器官系统（包括肾脏、肺部、免疫、心脏和肝脏系统）的285名患者中对模型进行了训练和验证。为了大幅提高我们模型的速度和实用性，我们应用了一种人工智能方法对CT成像上的区域进行分割和分类，从中可将可解释的数据直接输入到预测性机器学习模型中以进行整体恢复预测。在所有器官系统中，我们针对特定器官恢复的验证集在受试者工作特征曲线（AUC）下的面积值范围为0.80至0.89，并且在Kaplan-Meier分析中对整体恢复有显著预测。这表明，将应用于CT肺部成像的人工智能（AI）框架与将实验室检测数据与成像数据整合的机器学习模型协同使用，可以根据基线特征准确预测新冠肺炎患者的整体恢复情况。
