Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis

By Ophir Gozes, Maayan Frid-Adar, Hayit Greenspan, P. Browning, Huangqi Zhang, W.-B. Ji, Adam Bernheim, E. Siegel. arXiv.org, 2020

824 citations recorded by the source corpus.

Purpose: Develop AI-based automated CT image analysis tools for detection, quantification, and tracking of Coronavirus; demonstrate they can differentiate coronavirus patients from non-patients. Materials and Methods: Multiple international datasets, including from Chinese disease-infected areas were included. We present a system that utilizes robust 2D and 3D deep learning models, modifying and adapting existing AI models and combining them with clinical understanding. We conducted multiple retrospective experiments to analyze the performance of the system in the detection of suspected COVID-19 thoracic CT features and to evaluate evolution of the disease in each patient over time using a 3D volume review, generating a Corona score. The study includes a testing set of 157 international patients (China and U.S). Results: Classification results for Coronavirus vs Non-coronavirus cases per thoracic CT studies were 0.996 AUC (95%CI: 0.989-1.00) ; on datasets of Chinese control and infected patients. Possible working point: 98.2% sensitivity, 92.2% specificity. For time analysis of Coronavirus patients, the system output enables quantitative measurements for smaller opacities (volume, diameter) and visualization of the larger opacities in a slice-based heat map or a 3D volume display. Our suggested Corona score measures the progression of disease over time. Conclusion: This initial study, which is currently being expanded to a larger population, demonstrated that rapidly developed AI-based image analysis can achieve high accuracy in detection of Coronavirus as well as quantification and tracking of disease burden.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/00a407540a8bdd6d7425bd8a561eb21d69682511

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Citations in the corpus

COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images (2020, neutral): “Literature Review: Since revealing the potentials of computed tomography (CT) scans and X-ray images in detecting COVID-19 and weakness of the human-centered diagnosis, there have been several studies [5] - [7] trying to develop automatic COVID-19 classification systems, mainly using Convolutional N”
COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images (2020, background): “Motivated by the need for faster interpretation of radiography images, a number of artificial intelligence (AI) systems based on deep learning [7] have been proposed and results have shown to be quite promising in terms of accuracy in detecting patients infected with COVID-19 via radiography imaging”
Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection (2020, methodology): “With this Bayesian Deep Learning based classiﬁcation, studies correlating with multi "omics" dataset [11], and treatment responses could further reveal insights about imaging markers and ﬁndings towards improved diagnosis and treatment for Covid-19.”
Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images (2020, methodology): “Besides, deep learning algorithms have been proposed for COVID-19monitoring [13], screening [14] and prediction of the hospital stay [15].”
COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios (2020, positive): “[39] CT NCP detection and analysis using images taken from 157 patients 2D and 3D deep learning models, and other AI models”
The Role of Imaging in the Detection and Management of COVID-19: A Review (2020, background): “Similarly, Gozes et al. [84] used 157 patients from both China and the US as the testing set.”
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, methodology): “Within the last several months, we witnessed several interesting advances: the ability of AI to rapidly adapt from existing pretrained models to new disease manifestations of COVID-19, using the many tools described in this article and in [117] and [241].”
COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches (2020, neutral): “Recently, several Deep Learning (DL) based methods have been presented and shown to be highly accurate for COVID-19 detection from CT images [7,12,17].”