Image quality assessment of retinal fundus photographs for diabetic retinopathy in the machine learning era: a review

This study aimed to evaluate the image quality assessment (IQA) and quality criteria employed in publicly available datasets for diabetic retinopathy (DR). A literature search strategy was used to identify relevant datasets, and 20 datasets were included in the analysis. Out of these, 12 datasets me...

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Bibliographic Details
Published in:Eye (London) 2024-02, Vol.38 (3), p.426-433
Main Authors: Gonçalves, Mariana Batista, Nakayama, Luis Filipe, Ferraz, Daniel, Faber, Hanna, Korot, Edward, Malerbi, Fernando Korn, Regatieri, Caio Vinicius, Maia, Mauricio, Celi, Leo Anthony, Keane, Pearse A., Belfort Jr, Rubens
Format: Article
Language:English
Subjects:
692/699/3161/3175
692/700/1421
Algorithms
Automation
Data Accuracy
Datasets
Deep learning
Diabetes
Diabetes Mellitus
Diabetic retinopathy
Diabetic Retinopathy - diagnostic imaging
Fundus Oculi
Humans
Laboratory Medicine
Machine Learning
Medicine
Medicine & Public Health
Ophthalmology
Pharmaceutical Sciences/Technology
Quality control
Retinopathy
Review Article
Surgery
Surgical Oncology
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Summary:This study aimed to evaluate the image quality assessment (IQA) and quality criteria employed in publicly available datasets for diabetic retinopathy (DR). A literature search strategy was used to identify relevant datasets, and 20 datasets were included in the analysis. Out of these, 12 datasets mentioned performing IQA, but only eight specified the quality criteria used. The reported quality criteria varied widely across datasets, and accessing the information was often challenging. The findings highlight the importance of IQA for AI model development while emphasizing the need for clear and accessible reporting of IQA information. The study suggests that automated quality assessments can be a valid alternative to manual labeling and emphasizes the importance of establishing quality standards based on population characteristics, clinical use, and research purposes. In conclusion, image quality assessment is important for AI model development; however, strict data quality standards must not limit data sharing. Given the importance of IQA for developing, validating, and implementing deep learning (DL) algorithms, it’s recommended that this information be reported in a clear, specific, and accessible way whenever possible. Automated quality assessments are a valid alternative to the traditional manual labeling process, and quality standards should be determined according to population characteristics, clinical use, and research purpose.
ISSN:0950-222X
1476-5454
DOI:10.1038/s41433-023-02717-3