Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Deepinsights Study Group for Artificial Intelligence, Vienna, Austria.
Eur J Radiol. 2020 Dec;133:109402. doi: 10.1016/j.ejrad.2020.109402. Epub 2020 Nov 4.
Computed Tomography is an essential diagnostic tool in the management of COVID-19. Considering the large amount of examinations in high case-load scenarios, an automated tool could facilitate and save critical time in the diagnosis and risk stratification of the disease.
A novel deep learning derived machine learning (ML) classifier was developed using a simplified programming approach and an open source dataset consisting of 6868 chest CT images from 418 patients which was split into training and validation subsets. The diagnostic performance was then evaluated and compared to experienced radiologists on an independent testing dataset. Diagnostic performance metrics were calculated using Receiver Operating Characteristics (ROC) analysis. Operating points with high positive (>10) and low negative (<0.01) likelihood ratios to stratify the risk of COVID-19 being present were identified and validated.
The model achieved an overall accuracy of 0.956 (AUC) on an independent testing dataset of 90 patients. Both rule-in and rule out thresholds were identified and tested. At the rule-in operating point, sensitivity and specificity were 84.4 % and 93.3 % and did not differ from both radiologists (p > 0.05). At the rule-out threshold, sensitivity (100 %) and specificity (60 %) differed significantly from the radiologists (p < 0.05). Likelihood ratios and a Fagan nomogram provide prevalence independent test performance estimates.
Accurate diagnosis of COVID-19 using a basic deep learning approach is feasible using open-source CT image data. In addition, the machine learning classifier provided validated rule-in and rule-out criteria could be used to stratify the risk of COVID-19 being present.
计算机断层扫描（CT）是 COVID-19 管理中的重要诊断工具。考虑到在高病例负荷情况下进行大量检查，自动化工具可以在疾病的诊断和风险分层方面提供便利并节省关键时间。
使用简化的编程方法和一个包含 418 名患者 6868 例胸部 CT 图像的开源数据集，开发了一种新的深度学习衍生的机器学习（ML）分类器，该数据集分为训练集和验证集。然后在独立测试数据集上评估和比较诊断性能，并与经验丰富的放射科医生进行比较。使用接受者操作特征（ROC）分析计算诊断性能指标。确定并验证了具有高阳性（>10）和低阴性（<0.01）似然比的工作点，以分层 COVID-19 存在的风险。
该模型在 90 名患者的独立测试数据集中的总体准确率为 0.956（AUC）。确定并测试了规则内和规则外的阈值。在规则内工作点，敏感性和特异性分别为 84.4％和 93.3％，与两位放射科医生无差异（p＞0.05）。在排除阈值时，敏感性（100％）和特异性（60％）与放射科医生有显著差异（p＜0.05）。似然比和 Fagan 列线图提供了与患病率无关的测试性能估计。
使用基本的深度学习方法使用开源 CT 图像数据准确诊断 COVID-19 是可行的。此外，机器学习分类器提供了经过验证的规则内和规则外标准，可以用于分层 COVID-19 存在的风险。
