Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label

Abstract

Accurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learning-based model for automatic COVID-19 detection on chest CT is helpful to counter the outbreak of SARS-CoV-2. A weakly-supervised deep learning-based software system was developed using 3D CT volumes to detect COVID-19. For each patient, the lung region was segmented using a pre-trained UNet; then the segmented 3D lung region was fed into a 3D deep neural network to predict the probability of COVID-19 infectious. 499 CT volumes collected from Dec. 13, 2019, to Jan. 23, 2020, were used for training and 131 CT volumes collected from Jan 24, 2020, to Feb 6, 2020, were used for testing. The deep learning algorithm obtained 0.959 ROC AUC and 0.976 PR AUC. There was an operating point with 0.907 sensitivity and 0.911 specificity in the ROC curve. When using a probability threshold of 0.5 to classify COVID-positive and COVID-negative, the algorithm obtained an accuracy of 0.901, a positive predictive value of 0.840 and a very high negative predictive value of 0.982. The algorithm took only 1.93 seconds to process a single patient's CT volume using a dedicated GPU. Our weaklysupervised deep learning model can accurately predict the COVID-19 infectious probability in chest CT volumes without the need for annotating the lesions for training. The easily-trained and highperformance deep learning algorithm provides a fast way to identify COVID-19 patients, which is beneficial to control the outbreak of SARS-CoV-2. The developed deep learning software is available at https://github.com/sydney0zq/covid-19-detection.

A model to predict SARS-CoV-2 infection based on the first three-month surveillance data in Brazil

“…After evaluating all the variables, exclusions were made until obtaining a model including only covariates with p <0. 10.…”

“…However, its limited availability and the strict laboratory requirements delay diagnosis, which represents an unprecedented challenge to control transmission and provide timely health care. 10,11 The incorporation of predictive diagnostic models based on surveillance data could help identify patients who could need specific treatment and early isolation. Consequently, we aimed to describe the profile of COVID-19 patients and to obtain a multiple model to predict the diagnosis among suspected cases reported in Brazil based on data routinely collected by the surveillance system.…”

Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19

“…To segment ROIs in CT, deep learning methods are widely used. The popular segmentation networks for COVID-19 include classic U-Net [50][51][52][53][54][55], UNet++ [56,57], VB-Net [58]. Compared with CT, X-ray is more easily accessible around the world.…”

Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection

“…Barstugan, Ozkaya, et al 31 unclear unclear unclear high Chen, Wu, et al 26 high unclear low high *1 Gozes, Frid-Adar, et al 25 unclear unclear high high Jin, Chen, et al 11 high unclear unclear high *2 Jin, Wang, et al 33 high unclear high high *1 Li, Qin, et al 34 low unclear low high Shan, Gao, et al 28 unclear unclear high high *2 Shi, Xia, et al 36 high unclear low high Wang, Kang, et al 29 high unclear low high Xu, Jiang, et al 27 high unclear high high Ying, Zheng, et al 23 unclear unclear low high Zheng, Deng, et al 38 unclear unclear high high…”