Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis.
J Neurointerv Surg
; 15(10): 995-1000, 2023 Oct.
Article
em En
| MEDLINE
| ID: mdl-36418163
ABSTRACT
BACKGROUND:
This study aimed to investigate the application of deep learning (DL) models for the detection of subdural hematoma (SDH).METHODS:
We conducted a comprehensive search using relevant keywords. Articles extracted were original studies in which sensitivity and/or specificity were reported. Two different approaches of frequentist and Bayesian inference were applied. For quality and risk of bias assessment we used Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2).RESULTS:
We analyzed 22 articles that included 1,997,749 patients. In the first step, the frequentist method showed a pooled sensitivity of 88.8% (95% confidence interval (CI) 83.9% to 92.4%) and a specificity of 97.2% (95% CI 94.6% to 98.6%). In the second step, using Bayesian methods including 11 studies that reported sensitivity and specificity, a sensitivity rate of 86.8% (95% CI 77.6% to 92.9%) at a specificity level of 86.9% (95% CI 60.9% to 97.2%) was achieved. The risk of bias assessment was not remarkable using QUADAS-2.CONCLUSION:
DL models might be an appropriate tool for detecting SDHs with a reasonably high sensitivity and specificity.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Aprendizado Profundo
Tipo de estudo:
Diagnostic_studies
/
Systematic_reviews
Limite:
Humans
Idioma:
En
Revista:
J Neurointerv Surg
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Irã