Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography

By Kang Zhang, Xiaohong Liu, Jun Shen, Zhihuan Li, Ye Sang, Xingwang Wu, Y. Zha, W. Liang, Chengdi Wang, Ke Wang and 35 more. 2020

686 citations recorded by the source corpus.

Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT) database from 3,777 patients, we developed an AI system that can diagnose NCP and differentiate it from other common pneumonia and normal controls. The AI system can assist radiologists and physicians in performing a quick diagnosis especially when the health system is overloaded. Significantly, our AI system identified important clinical markers that correlated with the NCP lesion properties. Together with the clinical data, our AI system was able to provide accurate clinical prognosis that can aid clinicians to consider appropriate early clinical management and allocate resources appropriately. We have made this AI system available globally to assist the clinicians to combat COVID-19.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans (2020, methodology): “96 [6] CT 3D Yes 2,778 4,356 444,034 1,578‡ 1,614‡ 1,164‡ 389‡ 3D 92.”
Swarm Learning for decentralized and confidential clinical machine learning (2021, methodology): “SL should be explored for image-based diagnosis of COVID-19 from patterns in X-ray images or CT scans 15,16 , structured health records 12 , or data from wearables for disease tracking 12 .”
Segment anything in medical images (2023, positive): “In pulmonary imaging, segmentation of lung fields in chest X-rays or CT images is crucial for diagnosing and monitoring conditions like chronic obstructive pulmonary disease (COPD) and COVID-19 [14].”
A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics (2023, background): “In the second task, that is, adverse clinical outcome prediction for patients with COVID-19, the available clinical data were divided into four categories: demographics (age and gender), the structured chief complaint consisting of comorbidities (7) and symptoms (9) and laboratory test results (19) ”
COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images (2020, methodology): “We also introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation (CNCB) [17] comprising 104,009 images across 1,489 patient cases.”