Deep learning algorithms for detection of diabetic macular edema in OCT images: A systematic review and meta-analysis.
Eur J Ophthalmol
; 33(1): 278-290, 2023 Jan.
Article
en En
| MEDLINE
| ID: mdl-35473414
ABSTRACT
PURPOSE:
Artificial intelligence (AI) can detect diabetic macular edema (DME) from optical coherence tomography (OCT) images. We aimed to evaluate the performance of deep learning neural networks in DME detection.METHODS:
Embase, Pubmed, the Cochrane Library, and IEEE Xplore were searched up to August 14, 2021. We included studies using deep learning algorithms to detect DME from OCT images. Two reviewers extracted the data independently, and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to assess the risk of bias. The study is reported according to Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA).RESULTS:
Ninteen studies involving 41005 subjects were included. The pooled sensitivity and specificity were 96.0% (95% confidence interval (CI) 93.9% to 97.3%) and 99.3% (95% CI 98.2% to 99.7%), respectively. Subgroup analyses found that data set selection, sample size of training set and the choice of OCT devices contributed to the heterogeneity (all P < 0.05). While there was no association between the diagnostic accuracy and transfer learning adoption or image management (all P > 0.05).CONCLUSIONS:
Deep learning methods, particularly the convolutional neural networks (CNNs) could effectively detect clinically significant DME, which can provide referral suggestions to the patients.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Edema Macular
/
Diabetes Mellitus
/
Retinopatía Diabética
/
Aprendizaje Profundo
Tipo de estudio:
Diagnostic_studies
/
Systematic_reviews
Límite:
Humans
Idioma:
En
Revista:
Eur J Ophthalmol
Asunto de la revista:
OFTALMOLOGIA
Año:
2023
Tipo del documento:
Article