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Evaluation of a novel artificial intelligence-based screening system for diabetic retinopathy in community of China: a real-world study

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Abstract

Purpose

To evaluate the performance of an AI-based diabetic retinopathy (DR) grading model in real-world community clinical setting.

Methods

Participants with diabetes on record in the chosen community were recruited by health care staffs in a primary clinic of Zhengzhou city, China. Retinal images were prospectively collected during December 2018 and April 2019 based on intent-to-screen principle. A pre-validated AI system based on deep learning algorithm was deployed to screen DR graded according to the International Clinical Diabetic Retinopathy scale. Kappa value of DR severity, the sensitivity, specificity of detecting referable DR (RDR) and any DR were generated based on the standard of the majority manual grading decision of a retina specialist panel.

Results

Of the 193 eligible participants, 173 (89.6%) were readable with at least one eye image. Mean [SD] age was 69.3 (9.0) years old. Total of 321 eyes (83.2%) were graded both by AI and the specialist panel. The κ value in eye image grading was 0.715. The sensitivity, specificity and area under curve for detection of RDR were 84.6% (95% CI: 54.6– 98.1%), 98.0% (95% CI: 94.3–99.6%) and 0.913 (95% CI: 0.797–1.000), respectively. For detection of any DR, the upper indicators were 90.0% (95% CI: 68.3–98.8), 96.6% (95% CI: 92.1–98.9) and 0.933 (95% CI: 0.933–1.000), respectively.

Conclusion

The AI system showed relatively good consistency with ophthalmologist diagnosis in DR grading, high specificity and acceptable sensitivity for identifying RDR and any DR.

Translational relevance

It is feasible to apply AI-based DR screening in community.

Precis

Deployed in community real-world clinic setting, AI-based DR screening system showed high specificity and acceptable sensitivity in identifying RDR and any DR. Good DR diagnostic consistency was found between AI and manual grading. These prospective evidences were essential for regulatory approval.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

The authors alone are responsible for the content and writing of the paper. We wish to thank Dr Nian Xue for providing LeTex version of the manuscript.

Funding

This work was supported by Henan Key Laboratory of Ophthalmology and Visual Science and National Natural Science Foundation of China Grants (No. 82071008, 81770949).

Author information

Authors and Affiliations

  1. Department of Ophthalmology, Clinical Research Center, Henan Eye Hospital, Henan Provincial People’s Hospital, Henan Eye Institute, People’s Hospital of Zhengzhou University, No.7 Weiwu Road, Jinshui District, Zhengzhou, 450003, China

    Shuai Ming, Kunpeng Xie, Xiang Lei, Yingrui Yang, Zhaoxia Zhao, Shuyin Li, Xuemin Jin & Bo Lei

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  1. Shuai Ming
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  2. Kunpeng Xie
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  3. Xiang Lei
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  4. Yingrui Yang
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  5. Zhaoxia Zhao
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  6. Shuyin Li
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  7. Xuemin Jin
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  8. Bo Lei
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Contributions

SM helped in protocol development, data management, analysis, results interpretation and manuscript writing. KX contributed to data collection, fundus image reading and results interpretation. XL, ZZ, SL and XJ were involve in fundus image reading, ZZ, YY, XJ and BL contributed to program coordination. BL helped in language proofreading, manuscript revision and funding support.

Corresponding author

Correspondence to Bo Lei.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The study was approved by the Ethical Committee of Henan Eye Hospital.

Informed consent

Informed consents were obtained from the subjects after explanation of the nature of the study.

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Shuai Ming and Kunpeng Xie are considered as co-first author.

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Ming, S., Xie, K., Lei, X. et al. Evaluation of a novel artificial intelligence-based screening system for diabetic retinopathy in community of China: a real-world study. Int Ophthalmol 41, 1291–1299 (2021). https://doi.org/10.1007/s10792-020-01685-x

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  • DOI: https://doi.org/10.1007/s10792-020-01685-x

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