Kimura-Sandoval Yukiyoshi, Arévalo-Molina Mary E, Cristancho-Rojas César N, Kimura-Sandoval Yumi, Rebollo-Hurtado Victoria, Licano-Zubiate Mariana, Chapa-Ibargüengoitia Mónica, Muñoz-López Gisela
Department of Radiology, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
Department of Radiology, CT Scanner Group, Mexico City, Mexico.
Rev Invest Clin. 2020 Nov 17. doi: 10.24875/RIC.20000451.
Artificial intelligence (AI) in radiology has improved diagnostic performance and shortened reading times of coronavirus disease 2019 (COVID-19) patients' studies.
The objectives pf the study were to analyze the performance of a chest computed tomography (CT) AI quantitative algorithm for determining the risk of mortality/mechanical ventilation (MV) in hospitalized COVID-19 patients and explore a prognostic multivariate model in a tertiary-care center in Mexico City.
Chest CT images of 166 COVID-19 patients hospitalized from April 1 to 20, 2020, were retrospectively analyzed using AI algorithm software. Data were collected from their medical records. We analyzed the diagnostic yield of the relevant CT variables using the area under the ROC curve (area under the curve [AUC]). Optimal thresholds were obtained using the Youden index. We proposed a predictive logistic model for each outcome based on CT AI measures and predetermined laboratory and clinical characteristics.
The highest diagnostic yield of the assessed CT variables for mortality was the percentage of total opacity (threshold >51%; AUC = 0.88, sensitivity = 74%, and specificity = 91%). The AUC of the CT severity score (threshold > 12.5) was 0.88 for MV (sensitivity = 65% and specificity = 92%). The proposed prognostic models include the percentage of opacity and lactate dehydrogenase level for mortality and troponin I and CT severity score for MV requirement.
The AI-calculated CT severity score and total opacity percentage showed good diagnostic accuracy for mortality and met MV criteria. The proposed prognostic models using biochemical variables and imaging data measured by AI on chest CT showed good risk classification in our population of hospitalized COVID-19 patients.
放射学中的人工智能（AI）提高了2019冠状病毒病（COVID-19）患者检查的诊断性能并缩短了阅片时间。
本研究的目的是分析胸部计算机断层扫描（CT）AI定量算法在确定住院COVID-19患者死亡/机械通气（MV）风险方面的性能，并在墨西哥城的一家三级医疗中心探索一种预后多变量模型。
回顾性分析了2020年4月1日至20日住院的166例COVID-19患者的胸部CT图像，使用AI算法软件进行分析。从他们的病历中收集数据。我们使用ROC曲线下面积（曲线下面积[AUC]）分析了相关CT变量的诊断效能。使用约登指数获得最佳阈值。我们基于CT AI测量值以及预先确定的实验室和临床特征，为每个结局提出了一个预测逻辑模型。
评估的CT变量中，对死亡率诊断效能最高的是总实变百分比（阈值>51%；AUC = 0.88，敏感性 = 74%，特异性 = 91%）。CT严重程度评分（阈值>12.5）对MV的AUC为0.88（敏感性 = 65%，特异性 = 92%）。提出的预后模型包括用于死亡率的实变百分比和乳酸脱氢酶水平，以及用于MV需求的肌钙蛋白I和CT严重程度评分。
AI计算的CT严重程度评分和总实变百分比对死亡率显示出良好的诊断准确性，并符合MV标准。所提出的使用生化变量和AI在胸部CT上测量的影像数据的预后模型在我们的住院COVID-19患者群体中显示出良好的风险分类。
