Diagnostic performance of deep-learning-based screening methods for diabetic retinopathy in primary care—A meta-analysis

Diabetic retinopathy (DR) affects 10-24% of patients with diabetes mellitus type 1 or 2 in the primary care (PC) sector. As early detection is crucial for treatment, deep learning screening methods in PC setting could potentially aid in an accurate and timely diagnosis. The purpose of this meta-anal...

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Bibliographic Details
Published in:PloS one 2021-08, Vol.16 (8), p.e0255034-e0255034
Main Authors: Wewetzer, Larisa, Held, Linda A, Steinhäuser, Jost
Format: Article
Language:English
Subjects:
Algorithms
Ambulatory care
Artificial intelligence
Bias
Biology and Life Sciences
Computer and Information Sciences
COVID-19
Datasets
Deep learning
Diabetes
Diabetes mellitus
Diabetes mellitus (insulin dependent)
Diabetic retinopathy
Diagnosis
Diagnostic imaging
Evaluation
Health care
Information technology
Machine learning
Medical diagnosis
Medical research
Medicine and Health Sciences
Meta-analysis
Neural networks
Parameter sensitivity
Patients
Physical Sciences
Primary care
Research and Analysis Methods
Retinopathy
Systematic review
Validity
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Summary:Diabetic retinopathy (DR) affects 10-24% of patients with diabetes mellitus type 1 or 2 in the primary care (PC) sector. As early detection is crucial for treatment, deep learning screening methods in PC setting could potentially aid in an accurate and timely diagnosis. The purpose of this meta-analysis was to determine the current state of knowledge regarding deep learning (DL) screening methods for DR in PC. A systematic literature search was conducted using Medline, Web of Science, and Scopus to identify suitable studies.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0255034