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Deep-learning-based prediction of glaucoma conversion in normotensive glaucoma suspects
  1. Ahnul Ha1,
  2. Sukkyu Sun2,
  3. Young Kook Kim3,4,
  4. Jin Wook Jeoung3,4,
  5. Hee Chan Kim5,
  6. Ki Ho Park3,4
  1. 1 Department of Ophthalmology, Jeju National University, Jeju, Korea (the Republic of)
  2. 2 Department of AI Software Convergence, Dongguk University, Seoul, Korea (the Republic of)
  3. 3 Department of Ophthalmology, Seoul National University Hospital, Seoul, South Korea
  4. 4 Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Korea (the Republic of)
  5. 5 Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea (the Republic of)
  1. Correspondence to Professor Ki Ho Park, Department of Ophthalmology, Seoul National University Hospital, Jongno-gu, 03080, Korea (the Republic of); kihopark{at}snu.ac.kr; Professor Hee Chan Kim; hckim{at}snu.ac.kr

Abstract

Background/aims To assess the performance of deep-learning (DL) models for prediction of conversion to normal-tension glaucoma (NTG) in normotensive glaucoma suspect (GS) patients.

Methods Datasets of 12 458 GS eyes were reviewed. Two hundred and ten eyes (105 eyes showing NTG conversion and 105 without conversion), followed up for a minimum of 7 years during which intraocular pressure (IOP) was lower than 21 mm Hg, were included. The features of two fundus images (optic disc photography and red-free retinal nerve fibre layer (RNFL) photography) were extracted by convolutional auto encoder. The extracted features as well as 15 clinical features including age, sex, IOP, spherical equivalent, central corneal thickness, axial length, average circumpapillary RNFL thickness, systolic/diastolic blood pressure and body mass index were used to predict NTG conversion. Prediction was performed using three machine-learning classifiers (ie, XGBoost, Random Forest, Gradient Boosting) with different feature combinations.

Results All three algorithms achieved high diagnostic accuracy for NTG conversion prediction. The AUCs ranged from 0.987 (95% CI 0.978 to 1.000; Random Forest trained with both fundus images and clinical features) and 0.994 (95% CI 0.984 to 1.000; XGBoost trained with both fundus images and clinical features). XGBoost showed the best prediction performance for time to NTG conversion (mean squared error, 2.24). The top three important clinical features for time-to-conversion prediction were baseline IOP, diastolic blood pressure and average circumpapillary RNFL thickness.

Conclusion DL models, trained with both fundus images and clinical data, showed the potential to predict whether and when normotensive GS patients will show conversion to NTG.

  • Glaucoma

Data availability statement

All data relevant to the study are included in the article or uploaded as online supplemental information.

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Data availability statement

All data relevant to the study are included in the article or uploaded as online supplemental information.

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Footnotes

  • AH and SS are joint first authors.

  • Contributors HCK and KHP had full access to all the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. AH and SS contributed equally to this work. Concept and design: HCK, YKK, JWJ and KHP. Acquisition, analysis or interpretation of data: all authors. Drafting of the manuscript: AH and SS. Critical revision of the manuscript for important intellectual content: all authors. Statistical analysis: SS and HCK. KHP is guarantor.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • 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.

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