Diagnostic performance of deep-learning-based screening methods for diabetic retinopathy in primary care-A meta-analysis.
<h4>Background</h4>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...
Enregistré dans:
Auteurs principaux: | Larisa Wewetzer, Linda A Held, Jost Steinhäuser |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Public Library of Science (PLoS)
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/bc6fd489bd594fc7aeafbd9d7a999f11 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Barriers and facilitators to diabetic retinopathy screening within Australian primary care
par: Matthew J. G. Watson, et autres
Publié: (2021) -
Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy
par: Michelle Y. T. Yip, et autres
Publié: (2020) -
Feasibility of a multifaceted implementation intervention to improve attendance at diabetic retinopathy screening in primary care in Ireland: a cluster randomised pilot trial
par: Susan M Smith, et autres
Publié: (2021) -
Screening Referable Diabetic Retinopathy Using a Semi-automated Deep Learning Algorithm Assisted Approach
par: Yueye Wang, et autres
Publié: (2021) -
DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity
par: Omneya Attallah
Publié: (2021)