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

Gonçalves, Mariana Batista; Nakayama, Luis Filipe; Ferraz, Daniel; Faber, Hanna; Korot, Edward; Malerbi, Fernando Korn; Regatieri, Caio Vinicius; ... Belfort Jr, Rubens; + view all (2024) Image quality assessment of retinal fundus photographs for diabetic retinopathy in the machine learning era: a review. Eye , 38 pp. 426-433. 10.1038/s41433-023-02717-3. Green open access

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Abstract

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.

Type: Article
Title: Image quality assessment of retinal fundus photographs for diabetic retinopathy in the machine learning era: a review
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1038/s41433-023-02717-3
Publisher version: https://doi.org/10.1038/s41433-023-02717-3
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Medical imaging, Retinal diseases
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology
URI: https://discovery.ucl.ac.uk/id/eprint/10178499
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