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Fine-Grained Assessment of COVID-19 Severity Based on Clinico-Radiological Data Using Machine Learning

Author

Listed:
  • Haipeng Liu

    (Research Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, UK)

  • Jiangtao Wang

    (Research Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, UK)

  • Yayuan Geng

    (Scientific Research Department, HY Medical Technology, B-2 Building, Dongsheng Science Park, Beijing 100192, China)

  • Kunwei Li

    (Department of Radiology, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, China)

  • Han Wu

    (College of Engineering, Mathematics and Physical Sciences, Streatham Campus, University of Exeter, North Park Road, Exeter EX4 4QF, UK)

  • Jian Chen

    (Department of Radiology, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, China)

  • Xiangfei Chai

    (Scientific Research Department, HY Medical Technology, B-2 Building, Dongsheng Science Park, Beijing 100192, China)

  • Shaolin Li

    (Department of Radiology, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, China
    Guangdong Provincial Key Laboratory of Biomedical Imaging, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, China)

  • Dingchang Zheng

    (Research Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, UK)

Abstract

Background: The severe and critical cases of COVID-19 had high mortality rates. Clinical features, laboratory data, and radiological features provided important references for the assessment of COVID-19 severity. The machine learning analysis of clinico-radiological features, especially the quantitative computed tomography (CT) image analysis results, may achieve early, accurate, and fine-grained assessment of COVID-19 severity, which is an urgent clinical need. Objective: To evaluate if machine learning algorithms using CT-based clinico-radiological features could achieve the accurate fine-grained assessment of COVID-19 severity. Methods: The clinico-radiological features were collected from 78 COVID-19 patients with different severities. A neural network was developed to automatically measure the lesion volume from CT images. The severity was clinically diagnosed using two-type (severe and non-severe) and fine-grained four-type (mild, regular, severe, critical) classifications, respectively. To investigate the key features of COVID-19 severity, statistical analyses were performed between patients’ clinico-radiological features and severity. Four machine learning algorithms (decision tree, random forest, SVM, and XGBoost) were trained and applied in the assessment of COVID-19 severity using clinico-radiological features. Results: The CT imaging features (CTscore and lesion volume) were significantly related with COVID-19 severity ( p < 0.05 in statistical analysis for both in two-type and fine-grained four-type classifications). The CT imaging features significantly improved the accuracy of machine learning algorithms in assessing COVID-19 severity in the fine-grained four-type classification. With CT analysis results added, the four-type classification achieved comparable performance to the two-type one. Conclusions: CT-based clinico-radiological features can provide an important reference for the accurate fine-grained assessment of illness severity using machine learning to achieve the early triage of COVID-19 patients.

Suggested Citation

  • Haipeng Liu & Jiangtao Wang & Yayuan Geng & Kunwei Li & Han Wu & Jian Chen & Xiangfei Chai & Shaolin Li & Dingchang Zheng, 2022. "Fine-Grained Assessment of COVID-19 Severity Based on Clinico-Radiological Data Using Machine Learning," IJERPH, MDPI, vol. 19(17), pages 1-14, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10665-:d:898963
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    References listed on IDEAS

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    1. Giorgio Visani & Enrico Bagli & Federico Chesani & Alessandro Poluzzi & Davide Capuzzo, 2022. "Statistical stability indices for LIME: Obtaining reliable explanations for machine learning models," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(1), pages 91-101, January.
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