Sakagianni Aikaterini, Feretzakis Georgios, Kalles Dimitris, Koufopoulou Christina, Kaldis Vasileios
Sismanogleio General Hospital, Intensive Care Unit, Marousi, Greece.
School of Science and Technology, Hellenic Open University, Patras, Greece.
Stud Health Technol Inform. 2020 Jun 26;272:13-16. doi: 10.3233/SHTI200481.
Coronavirus disease (COVID-19) constitutes an ongoing global health problem with significant morbidity and mortality. It usually presents characteristic findings on a chest CT scan, which may lead to early detection of the disease. A timely and accurate diagnosis of COVID-19 is the cornerstone for the prompt management of the patients. The aim of the present study was to evaluate the performance of an automated machine learning algorithm in the diagnosis of Covid-19 pneumonia using chest CT scans. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity, and positive predictive value. The method's average precision was 0.932. We suggest that auto-ML platforms help users with limited ML expertise train image recognition models by only uploading the examined dataset and performing some basic settings. Such methods could deliver significant potential benefits for patients in the future by allowing for earlier disease detection and care.
冠状病毒病（COVID-19）是一个持续存在的全球性健康问题，具有较高的发病率和死亡率。它通常在胸部CT扫描上呈现出特征性表现，这可能有助于疾病的早期发现。及时、准确地诊断COVID-19是对患者进行及时治疗的基石。本研究的目的是评估一种自动化机器学习算法在使用胸部CT扫描诊断COVID-19肺炎中的性能。通过受试者操作特征曲线（AUC）下的面积、敏感性和阳性预测值来评估诊断性能。该方法的平均精度为0.932。我们建议，自动机器学习平台可帮助机器学习专业知识有限的用户，只需上传检查数据集并进行一些基本设置，就能训练图像识别模型。此类方法未来可能通过实现疾病的早期发现和治疗，为患者带来显著的潜在益处。
