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. 2022 Jan 3;8(1):1.
doi: 10.1186/s40942-021-00352-2.

Diabetic retinopathy classification for supervised machine learning algorithms

Luis Filipe Nakayama  1 , Lucas Zago Ribeiro  2 , Mariana Batista Gonçalves  2   3   4 , Daniel A Ferraz  2   3   4 , Helen Nazareth Veloso Dos Santos  2 , Fernando Korn Malerbi  2 , Paulo Henrique Morales  2   3 , Mauricio Maia  2 , Caio Vinicius Saito Regatieri  2 , Rubens Belfort Mattos Jr  2   3
Affiliations

Diabetic retinopathy classification for supervised machine learning algorithms

Luis Filipe Nakayama et al. Int J Retina Vitreous. .

Abstract

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.

Keywords: Artificial intelligence; Datasets; Diabetic retinopathy classifications.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Direct retinal findings manual annotation example, in Labelbox software
See this image and copyright information in PMC

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