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Clinical science
Crystalline lens nuclear age prediction as a new biomarker of nucleus degeneration
  1. Mengjie Guo1,2,
  2. Risa Higashita3,4,
  3. Chen Lin5,
  4. Lingxi Hu1,
  5. Wan Chen6,
  6. Fei Li6,
  7. Gilda Wing Ki Lai7,
  8. Anwell Nguyen8,
  9. Rei Sakata9,
  10. Keiichiro Okamoto4,
  11. Bo Tang1,
  12. Yanwu Xu10,
  13. Huazhu Fu11,
  14. Fei Gao2,
  15. Makoto Aihara9,
  16. Xiulan Zhang6,
  17. Jin Yuan6,
  18. Shan Lin8,12,
  19. Christopher Kai-Shun Leung7,13,
  20. Jiang Liu1,3,14
  1. 1 Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, Guangdong, China
  2. 2 School of Information Science and Technology, ShanghaiTech University, Shanghai, China
  3. 3 Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
  4. 4 Tomey Corporation, Nagoya, Aichi, Japan
  5. 5 Shenzhen People's Hospital, Shenzhen, Guangdong, China
  6. 6 Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-Sen University, Guangzhou, Guangdong, China
  7. 7 Department of Ophthalmology, The University of Hong Kong, Hong Kong, Hong Kong
  8. 8 Department of Ophthalmology, University of California, San Francisco, California, USA
  9. 9 Department of Ophthalmology, The University of Tokyo, Tokyo, Japan
  10. 10 Intelligent Healthcare Unit, Baidu Inc, Beijing, China
  11. 11 Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore
  12. 12 Glaucoma Center of San Francisco, San Francisco, California, USA
  13. 13 Department of Ophthalmology and Visual Sciences, The Chinese University, Hong Kong, Hong Kong
  14. 14 Cixi Institute of Biomedical Engineering, Chinese Academy of Sciences, Cixi, Zhejiang, China
  1. Correspondence to Professor Jiang Liu, Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China; liuj{at}sustech.edu.cn; Dr Risa Higashita; lisahigashita{at}gmail.com; Dr Yanwu Xu; ywxu{at}ieee.org; Dr Huazhu Fu; hzfu{at}ieee.org; Dr Fei Gao; gaofei{at}shanghaitech.edu.cn; Dr Jin Yuan; yuanjincornea{at}126.com

Abstract

Background The crystalline lens is a transparent structure of the eye to focus light on the retina. It becomes muddy, hard and dense with increasing age, which makes the crystalline lens gradually lose its function. We aim to develop a nuclear age predictor to reflect the degeneration of the crystalline lens nucleus.

Methods First we trained and internally validated the nuclear age predictor with a deep-learning algorithm, using 12 904 anterior segment optical coherence tomography (AS-OCT) images from four diverse Asian and American cohorts: Zhongshan Ophthalmic Center with Machine0 (ZOM0), Tomey Corporation (TOMEY), University of California San Francisco and the Chinese University of Hong Kong. External testing was done on three independent datasets: Tokyo University (TU), ZOM1 and Shenzhen People’s Hospital (SPH). We also demonstrate the possibility of detecting nuclear cataracts (NCs) from the nuclear age gap.

Findings In the internal validation dataset, the nuclear age could be predicted with a mean absolute error (MAE) of 2.570 years (95% CI 1.886 to 2.863). Across the three external testing datasets, the algorithm achieved MAEs of 4.261 years (95% CI 3.391 to 5.094) in TU, 3.920 years (95% CI 3.332 to 4.637) in ZOM1-NonCata and 4.380 years (95% CI 3.730 to 5.061) in SPH-NonCata. The MAEs for NC eyes were 8.490 years (95% CI 7.219 to 9.766) in ZOM1-NC and 9.998 years (95% CI 5.673 to 14.642) in SPH-NC. The nuclear age gap outperformed both ophthalmologists in detecting NCs, with areas under the receiver operating characteristic curves of 0.853 years (95% CI 0.787 to 0.917) in ZOM1 and 0.909 years (95% CI 0.828 to 0.978) in SPH.

Interpretation The nuclear age predictor shows good performance, validating the feasibility of using AS-OCT images as an effective screening tool for nucleus degeneration. Our work also demonstrates the potential use of the nuclear age gap to detect NCs.

  • degeneration
  • imaging
  • lens and zonules

Data availability statement

Because of ethical restrictions, the data cannot be made freely available in the manuscript, the appendix, or a public repository. Data are available to researchers who meet the criteria for access to confidential data; requests should be made to Risa Higashita (lisahigashita@gmail.com). The prediction models with Python implementation will also be available from Risa Higashita on request.

http://dx.doi.org/10.1136/bjo-2023-323176

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    Data availability statement

    Because of ethical restrictions, the data cannot be made freely available in the manuscript, the appendix, or a public repository. Data are available to researchers who meet the criteria for access to confidential data; requests should be made to Risa Higashita (lisahigashita@gmail.com). The prediction models with Python implementation will also be available from Risa Higashita on request.

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    Footnotes

    • Contributors MG, RH, YX, HF and JL conceived and designed the study. RH, CL, WC, FL, GWKL, AN, RS, KO, MA, XZ, JY, SL and CK-SL collected the data. MG, RH, YX and HF developed the algorithm. MG, RH, CL, LH, YX and HF analysis and interpretation of the data. MG drafted the manuscript. RH, BT, YX, HF, FG, SL, CL and JL critically revised the manuscript for important intellectual content. MG, RH, YX, HF and JL were responsible for the decision to submit the manuscript. JL was the guarantor.

    • Funding This work was supported in part by the Guangdong Provincial Department of Education (2020ZDZX3043), Guangdong Provincial Key Laboratory (2020B121201001) and Shenzhen Natural Science Fund (JCYJ20200109140820699 and the Stable Support Plan Programme 20200925174052004).

    • Competing interests MA, XZ, SL and CL have received support from Tomey in the form of instrument and speaker honorarium. YX is an employee of Baidu and owns stock in Baidu. SL is a consultant for IRIDEX and Eyenovia, a speaker for Bausch and Lomb, a consultant and speaker for Aerie Pharmaceuticals, and research support for Tomey. Aerie, Iridex, Bausch & Lomb and Allergan. CL has received research support in the form of research grants and instruments from Carl Zeiss Meditec, Topcon and Heidelberg Engineering and has been on the advisory board as a member for Alcon, Novartis, Santen, Aerie and Allergan.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.