AI based analysis of imaging, clinical & lab data to triage COVID19 patients.

Heatmap of radiomics features of COVID19 patients showing the reatlinships between severity (ICU admission, Machine Ventilator use (MV) and death). 

We took advantage of a large cohort of Chinese PCR-confirmed COVID19 patients to embark on a biomedical data fusion project to triage COVID19 patients. We develop a machine learning approach for risk stratification based on CT-based radiomics features and clinical data for COVID-19 patients in terms of stable or severe disease (requiring ICU) on admission, then developed specific outcome prediction (MV/death) models for critically ill patients. We also provided insights into estimating time to the progression (ICU/MV/death) for COVID-19 patients. Furthermore, we identified key clinical prognostic indicators and CT-based predictive features for outcome prediction (ICU/MV/death). This work is valuable for delivering timely treatment and optimizing the use of limited medical resources during the COVID-19 pandemic.


CT images of COVID19 patients: Left (a, c, e): original images; Right (b, d, f): pulmonary lobes (colored lines) and opacities segmentation (blue area).

The models are available here:

You can find more thoughts on this work and biomedical data fusion in the context of quantitative imaging here


Xu Q, Zhan X, Zhou Z, Li Y, Xie P, Zhang S, Li X, Yu Y, Zhou C, Zhang L, Gevaert O, Lu G. AI-based analysis of CT images for rapid triage of COVID-19 patients. NPJ Digit Med. 2021 Apr 22;4(1):75. doi: 10.1038/s41746-021-00446-z. PMID: 33888856; PMCID: PMC8062628.