TY  - JOUR
AU  - Wang, Changyu
AU  - Liu, Siru
AU  - Tang, Yu
AU  - Yang, Hao
AU  - Liu, Jialin
PY  - 2023
DA  - 2023/7/21
TI  - Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis
JO  - J Med Internet Res
SP  - e46340
VL  - 25
KW  - COVID-19
KW  - deep learning
KW  - prognostics and health management
KW  - Severity of Illness Index
KW  - accuracy
KW  - AI
KW  - prediction model
KW  - systematic review
KW  - meta-analysis
KW  - disease severity
KW  - prognosis
KW  - digital health intervention
AB  - Background: Deep learning (DL) prediction models hold great promise in the triage of COVID-19. Objective: We aimed to evaluate the diagnostic test accuracy of DL prediction models for assessing and predicting the severity of COVID-19. Methods: We searched PubMed, Scopus, LitCovid, Embase, Ovid, and the Cochrane Library for studies published from December 1, 2019, to April 30, 2022. Studies that used DL prediction models to assess or predict COVID-19 severity were included, while those without diagnostic test accuracy analysis or severity dichotomies were excluded. QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2), PROBAST (Prediction Model Risk of Bias Assessment Tool), and funnel plots were used to estimate the bias and applicability. Results: A total of 12 retrospective studies involving 2006 patients reported the cross-sectionally assessed value of DL on COVID-19 severity. The pooled sensitivity and area under the curve were 0.92 (95% CI 0.89-0.94; I2=0.00%) and 0.95 (95% CI 0.92-0.96), respectively. A total of 13 retrospective studies involving 3951 patients reported the longitudinal predictive value of DL for disease severity. The pooled sensitivity and area under the curve were 0.76 (95% CI 0.74-0.79; I2=0.00%) and 0.80 (95% CI 0.76-0.83), respectively. Conclusions: DL prediction models can help clinicians identify potentially severe cases for early triage. However, high-quality research is lacking. Trial Registration: PROSPERO CRD42022329252; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD 42022329252 
SN  - 1438-8871
UR  - https://www.jmir.org/2023/1/e46340
UR  - https://doi.org/10.2196/46340
UR  - http://www.ncbi.nlm.nih.gov/pubmed/37477951
DO  - 10.2196/46340
ID  - info:doi/10.2196/46340
ER  -