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

Haipeng Liu, Jiangtao Wang, Yayuan Geng, Kunwei Li, Han Wu, Jian Chen, Xiangfei Chai, Shaolin Li () and Dingchang Zheng ()
Additional contact information
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
Dingchang Zheng: Research Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, UK

IJERPH, 2022, vol. 19, issue 17, 1-14

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.

Keywords: COVID-19; lesion volume measurement; clinico-radiological features; machine learning; fine-grained classification (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/17/10665/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/17/10665/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:17:p:10665-:d:898963

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().