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DeepDRiD: Diabetic Retinopathy-Grading and Image Quality Estimation Challenge.

Ruhan Liu1,2, Xiangning Wang3, Qiang Wu3

  • 1Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.

Patterns (New York, N.Y.)
|June 27, 2022
PubMed

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Summary

A challenge focused on diabetic retinopathy (DR) grading and image quality estimation spurred deep learning model development. The released DeepDRiD dataset aids automatic DR screening systems.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Diabetic retinopathy (DR) poses a significant threat to vision globally.
  • Accurate DR grading and image quality assessment are crucial for effective screening and diagnosis.
  • Automated systems can potentially improve the efficiency and consistency of DR evaluation.

Purpose of the Study:

  • To organize a challenge focused on deep learning for DR grading and image quality estimation.
  • To introduce and release the DeepDRiD dataset for research and development.
  • To evaluate the performance of state-of-the-art deep learning algorithms in DR assessment.

Main Methods:

  • The Diabetic Retinopathy (DR)-Grading and Image Quality Estimation Challenge was held in conjunction with ISBI 2020.
Keywords:
artificial intelligencechallengedeep learningdiabetic retinopathyfundus imageimage quality analysisretinal imagescreeningultra-widefield

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  • Three sub-challenges were designed to assess DR grading and image quality.
  • The DeepDRiD dataset, comprising 2,000 regular and 256 ultra-widefield DR images with annotations, was provided.

    • Main Results:

    • 34 submissions from 574 registrations demonstrated strong community engagement.
    • Top algorithms achieved weighted kappa scores from 0.82 to 0.93 for DR grading.
    • Accuracy for image quality evaluation ranged from 0.65 to 0.70.

    Conclusions:

    • Deep learning models show promise for automated DR grading and image quality assessment.
    • Image quality assessment is a valuable area for further research in DR screening.
    • The publicly released DeepDRiD dataset will facilitate the development of automated DR diagnostic systems.