Diabetic retinopathy classification for supervised machine learning algorithms

Background Artificial intelligence and automated technology were first reported more than 70 years ago and nowadays provide unprecedented diagnostic accuracy, screening capacity, risk stratification, and workflow optimization. Diabetic retinopathy is an important cause of preventable blindness world...

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Vydané v:International journal of retina and vitreous Ročník 8; číslo 1; s. 1 - 5
Hlavní autori: Nakayama, Luis Filipe, Ribeiro, Lucas Zago, Gonçalves, Mariana Batista, Ferraz, Daniel A., dos Santos, Helen Nazareth Veloso, Malerbi, Fernando Korn, Morales, Paulo Henrique, Maia, Mauricio, Regatieri, Caio Vinicius Saito, Mattos, Rubens Belfort
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London BioMed Central 03.01.2022
Springer Nature B.V
BMC
Predmet:
Accuracy
Algorithms
Artificial intelligence
Automation
Classification
Commentary
Consortia
Datasets
Deep learning
Diabetes
Diabetic retinopathy
Diabetic retinopathy classifications
Edema
Health services
Labeling
Machine learning
Medical screening
Medicine
Medicine & Public Health
Neural networks
Ophthalmology
Quality control
Quality standards
United Kingdom > UK
Scotland
Datasets
Diabetic retinopathy classifications
Artificial intelligence
ISSN:2056-9920, 2056-9920
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Popis
Shrnutí:Background Artificial intelligence and automated technology were first reported more than 70 years ago and nowadays provide unprecedented diagnostic accuracy, screening capacity, risk stratification, and workflow optimization. Diabetic retinopathy is an important cause of preventable blindness worldwide, and artificial intelligence technology provides precocious diagnosis, monitoring, and guide treatment. High-quality exams are fundamental in supervised artificial intelligence algorithms, but the lack of ground truth standards in retinal exams datasets is a problem. Main body In this article, ETDRS, NHS, ICDR, SDGS diabetic retinopathy grading, and manual annotation are described and compared in publicly available datasets. The various DR labeling systems generate a fundamental problem for AI datasets. Possible solutions are standardization of DR classification and direct retinal-finding identifications. Conclusion Reliable labeling methods also need to be considered in datasets with more trustworthy labeling.
Bibliografia:SourceType-Scholarly Journals-1
ObjectType-Commentary-1
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ObjectType-Correspondence-1
ISSN:2056-9920
2056-9920
DOI:10.1186/s40942-021-00352-2