AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system

By Bo Wang, Shuo Jin, Qingsen Yan, Haibo Xu, Chuan Luo, Lai Wei, Wei Zhao, Xuexue Hou, Wenshuo Ma, Zhengqing Xu and 9 more. Applied Soft Computing, 2020

331 citations recorded by the source corpus.

The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hope artificial intelligence (AI) to reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. In this paper, we present our experience in building and deploying an AI system that automatically analyzes CT images and provides the probability of infection to rapidly detect COVID-19 pneumonia. The proposed system which consists of classification and segmentation will save about 30%–40% of the detection time for physicians and promote the performance of COVID-19 detection. Specifically, working in an interdisciplinary team of over 30 people with medical and/or AI background, geographically distributed in Beijing and Wuhan, we are able to overcome a series of challenges (e.g. data discrepancy, testing time-effectiveness of model, data security, etc.) in this particular situation and deploy the system in four weeks. In addition, since the proposed AI system provides the priority of each CT image with probability of infection, the physicians can confirm and segregate the infected patients in time. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we are able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases.

Subfields: Health Informatics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b25df9d54e787db387939c9cacbb1801e4b7021b

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging (2020, neutral): “[53] 2D Unet++ and 2D-CNN COVID-19, viral, baterial and Pneu.”
COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images (2020, background): “A number of studies have proposed deep learning systems based on chest CT imaging to distinguish COVID-19 cases from non-COVID-19 cases (which may include both normal and abnormal cases) [16, 20, 21, 22, 23, 24, 25, 26, 27, 17, 28, 29, 30].”
U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications (2020, background): “The versatility of the U-net network has allowed rapid development and deployment of early screening diagnostic algorithms for field use as early as March 2020 [262].”
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19 (2020, methodology): “propose an AI system for fast COVID-19 diagnosis [57] .”