June 2023
Volume 64, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2023
High Myopia Risk Profiling in Childhood: Artificial Intelligence Modality Integrating Fundus Imaging with Clinical Data
Author Affiliations & Notes
  • Li Lian Foo
    Singapore National Eye Centre, Singapore, Singapore, Singapore
    Duke-NUS Medical School, Singapore, Singapore
  • Gilbert Lim
    Singapore Eye Research Institute, Singapore, Singapore
    Duke-NUS Medical School, Singapore, Singapore
  • Daniel SW Ting
    Singapore National Eye Centre, Singapore, Singapore, Singapore
    Duke-NUS Medical School, Singapore, Singapore
  • Seang-Mei Saw
    Singapore Eye Research Institute, Singapore, Singapore
  • Marcus Ang
    Singapore National Eye Centre, Singapore, Singapore, Singapore
    Duke-NUS Medical School, Singapore, Singapore
  • Footnotes
    Commercial Relationships   Li Lian Foo None; Gilbert Lim None; Daniel Ting None; Seang-Mei Saw None; Marcus Ang None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2023, Vol.64, 5093. doi:
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    • Get Citation

      Li Lian Foo, Gilbert Lim, Daniel SW Ting, Seang-Mei Saw, Marcus Ang; High Myopia Risk Profiling in Childhood: Artificial Intelligence Modality Integrating Fundus Imaging with Clinical Data. Invest. Ophthalmol. Vis. Sci. 2023;64(8):5093.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To identify children at risk of developing high myopia for timely assessment and intervention, in order to prevent myopia progression and development of complications in adulthood.

Methods : Using school-based cohort in Singapore, comprising of 998 children, aged 6-12 years old, we trained and performed primary validation of a deep learning system using 7456 baseline fundus images of 1878 eyes and performed external validation using an independent test dataset comprising of 821 baseline fundus images of 189 eyes; with clinical data (age, gender, race, parental myopia and baseline spherical equivalent (SE)). Three distinct algorithms: image, clinical and mixed (image + clinical) models, comprising of fundus photo, age, race, gender and SE, were derived to predict the development of high myopia (SE ≤ -6.00 dioptre) during teenage years (5 years later, aged 11-17). Model performance was evaluated based on area under the receiver operating curve (AUC).

Results : All baseline models achieved clinically acceptable performance, with image models (AUC 0.93-0.95 in primary dataset; 0.91-0.93 in test dataset), clinical models (AUC 0.90-0.97 in primary dataset; 0.93-0.94 in test dataset) and mixed (image + clinical) models (AUC 0.97 in primary dataset; 0.97-0.98 in test dataset).The addition of 1 year SE progression achieved a marginal improvement or decline in performance for the clinical model (AUC 0.98 versus 0.97 in primary dataset; 0.97 versus 0.94 in test dataset) and mixed model (AUC 0.99 versus 0.97 in primary dataset; 0.95 versus 0.98 in test dataset).

Conclusions : Our deep learning system was able to predict the development of high myopia in school-going children by teenage years, which has the potential to be utilized as a clinical-decision support tool to identify ‘at risk’ children for early intervention.

This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.

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