Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis.
J Med Internet Res
; 25: e46340, 2023 07 21.
Article
em En
| MEDLINE
| ID: mdl-37477951
ABSTRACT
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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Aprendizado Profundo
/
COVID-19
Tipo de estudo:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
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Risk_factors_studies
/
Systematic_reviews
Limite:
Humans
Idioma:
En
Revista:
J Med Internet Res
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China