Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning.

BACKGROUND: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. METHODS: An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with 27 confirmed general pneumonia patients from Ruian People's Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented based on ground-glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray-level co-occurrence matrix (GLCM) features, 15 gray-level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High-dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each feature was the average weights calculated by ReliefF in n times. Features with relevance larger than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using tenfold cross-validation. RESULTS AND CONCLUSIONS: The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.

CT findings of patients infected with SARS-CoV-2.

BACKGROUND: We aimed to describe the chest CT findings in sixty-seven patients infected by Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: We retrospectively reviewed 67 patients hospitalized in Ruian People's Hospital. All the patients received the positive diagnosis of SARS-CoV-2 infection. The CT and clinical data were collected between January 23rd, 2020 and February 10th, 2020. The CT images were analyzed by the senior radiologists. RESULTS: There are 54 patients with positive CT findings and 13 patients with negative CT findings. The typical CT findings in hospitalized patients with SARS-CoV-2 infection were ground glass opacities (42/54), lesions located in the peripheral area (50/54), multiple lesions (46/54), and lesions located in the lower lobes (42/54). There were less typical CT findings, including air bronchogram (18/54), pleural thickening or pleural effusion (14/54), consolidation (12/54), lesions in the upper lobes (12/54), interlobular septal thickening (11/54), reversed halo sign (9/54), single lesion (8/54), air cavities (4/54), bronchial wall thickening (3/54), and intrathoracic lymph node enlargement (2/54). CONCLUSIONS: CT features can play an important role in the early diagnosis and follow-up of COVID-19 patients.