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

Eye volume 38, pages 426–433 (2024)Cite this article

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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.

摘要

本研究旨在评估糖尿病视网膜病变 (DR) 公开数据集中的图像质量评估 (IQA) 和质量标准。使用文献检索识别相关数据集, 纳入分析20个数据集。其中, 12个数据集提到执行IQA, 但只有8个数据集明确了所用的质量标准。所报告的图像质量标准因数据集而异, 获取信息往往具有挑战性。研究结果强调了IQA对人工智能模型开发的重要性, 同时强调了明确和可访问IQA信息报告的必要性。研究表明, 自动化的图像质量评估可以有效替代手动标签的方法, 并强调建立基于人口特征、临床使用和研究目的制定质量标准的重要性。总之, 图像质量评估对人工智能模型的开发非常重要, 然而, 严格的数据质量标准不能限制数据共享。鉴于IQA对于开发、验证和实现深度学习(DL)算法的重要性, 建议尽可能以清晰、具体和可访问的方式报告这些信息。自动化质量评估是传统手工标记过程的有效替代方案, 质量标准应根据人口特征、临床应用和研究目的来确定。

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Fig. 2: Datasets assessment.

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Acknowledgements

PAK is supported by a Moorfields Eye Charity Career Development Award (R190028A) and a UK Research & Innovation Future Leaders Fellowship (MR/T019050/1). LFN is a researcher supported by Lemann Foundation, Instituto da Visão-IPEPO, São Paulo, Brazil.

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Authors and Affiliations

  1. Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil

    Mariana Batista Gonçalves, Luis Filipe Nakayama, Daniel Ferraz, Fernando Korn Malerbi, Caio Vinicius Regatieri, Mauricio Maia & Rubens Belfort Jr.

  2. Instituto Paulista de Estudos e Pesquisas em Oftalmologia, IPEPO, Vision Institute, São Paulo, SP, Brazil

    Mariana Batista Gonçalves, Daniel Ferraz & Rubens Belfort Jr.

  3. NIHR Biomedical Research Centre for Ophthalmology, Moorfield Eye Hospital, NHS Foundation Trust, and UCL Institute of Ophthalmology, London, UK

    Mariana Batista Gonçalves, Daniel Ferraz & Pearse A. Keane

  4. Massachusetts Institute of Technology, Laboratory for Computational Physiology, Cambridge, MA, USA

    Luis Filipe Nakayama & Leo Anthony Celi

  5. Department of Ophthalmology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Hanna Faber

  6. Department of Ophthalmology, University of Tuebingen, Tuebingen, Germany

    Hanna Faber

  7. Retina Specialists of Michigan, Grand Rapids, MI, USA

    Edward Korot

  8. Stanford University Byers Eye Institute Palo Alto, Palo Alto, CA, USA

    Edward Korot

  9. Harvard TH Chan School of Public Health, Department of Biostatistics, Boston, MA, USA

    Leo Anthony Celi

  10. Beth Israel Deaconess Medical Center, Department of Medicine, Boston, MA, USA

    Leo Anthony Celi

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Contributions

MBG: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing-original draft, Writing-review & editing. LFN: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing-original draft, Writing-review & editing. DF: Writing-review & editing. HF: Writing-review & editing. EK: Writing-review & editing. FKM: Writing-review & editing. CVSR: Writing-review & editing. MM: Writing-review & editing. LAC: Conceptualization, Methodology, Supervision, Writing-review & editing. PK: Supervision, Writing-review & editing. RBJ: Conceptualization, Methodology, Supervision, Writing-review & editing

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Correspondence to Luis Filipe Nakayama.

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PAK has acted as a consultant for Google, DeepMind, Roche, Novartis, Apellis, and BitFount and is an equity owner in Big Picture Medical. He has received speaker fees from Heidelberg Engineering, Topcon, Allergan, and Bayer. EK: Sanro Health: Owner, Alimera sciences: Consultant, Genentech: consultant (in past six months, not current). The other authors declare no competing interests.

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Gonçalves, M.B., Nakayama, L.F., Ferraz, D. et al. Image quality assessment of retinal fundus photographs for diabetic retinopathy in the machine learning era: a review. Eye 38, 426–433 (2024). https://doi.org/10.1038/s41433-023-02717-3

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