This article was originally published in a sponsored newsletter.

Myopia is a rising global health concern, with projections indicating that by 2050, nearly half the global population will be myopic.¹ This underscores the urgency for timely diagnosis and management to prevent serious complications like glaucoma, retinal detachment, and cataracts.

Artificial Intelligence (AI), particularly machine learning (ML) and deep learning (DL) algorithms, are showing potential in enhancing diagnosis in ophthalmology.^2-8 Using AI for myopia could lead to early identification of high-risk patients, allowing for more targeted treatment strategies.^9-11

Accurate data collection is vital for personalized myopia treatment. This includes demographic details like age, gender, ethnicity, and genetic data, as well as lifestyle factors such as screen time and outdoor activities. Clinically, this encompasses measurements like refractive errors, axial length, and fundus photography. Current AI platforms often merge imaging technology with DL algorithms, facilitating predictions about myopia onset,^12-13 progression,^14,15estimation of refractive error,16 and associated retinal diseases.^17-19

In childhood myopia management, the aim is to create customized treatment plans. Several methods are available, such as soft contact lenses, low-dose atropine, and red-light therapy. AI’s potential lies in accurately determining the best therapy for individual patients. AI can also assist in designing and fitting myopia control devices. A study even indicated that AI-assisted methods for orthokeratology lens fitting proved more accurate than conventional methods.²⁰

Typically, practitioners rely on clinical experience and lifestyle factors to determine treatment. AI can standardize this process, merging patient-specific data with clinical expertise to optimize outcomes. Additionally, AI-driven platforms can further patient education, leading to better adherence to treatment and regular check-ups.

However, while AI offers transformative benefits, challenges exist. These include ensuring diverse image processing, managing variations in clinician observations, building trust in AI-driven decisions, and standardizing diagnosis and referral criteria.^21-22

In summary, AI has the potential to revolutionize myopia management, offering tailored treatment plans and improving early detection. While its integration promises enhanced visual health outcomes for myopia patients globally, continuous monitoring and validation of AI systems remain essential.

1. Holden BA, Fricke TR, Wilson DA, et al. Global Prevalence of Myopia and High Myopia and Temporal Trends from 2000 to 2050. Ophthalmology. 2016 May;123:1036-1042.

2. Abràmoff MD, Lou Y, Erginay A, et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci. 2016 Oct 1;57:5200-5206.

3. Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning. Ophthalmology. 2017 Jul;124:962-969.

4. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016 Dec 13;316:2402-2410.

5. Burlina PM, Joshi N, Pekala M, Pacheco KD, Freund DE, Bressler NM. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol. 2017 Nov 1;135:1170-1176.

6. Grassmann F, Mengelkamp J, Brandl C, Harsch S, Zimmermann ME, Linkohr B, et al. A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography. Ophthalmology. 2018 Sep;125:1410-1420.

7. Christopher M, Belghith A, Weinreb RN, et al. Retinal Nerve Fiber Layer Features Identified by Unsupervised Machine Learning On Optical Coherence Tomography Scans Predict Glaucoma Progression. Invest Ophthalmol Vis Sci. 2018 Jun 1;59:2748-2756.

8. Honavar SG. Artificial Intelligence in ophthalmology – Machines think! Indian J. Ophthalmology. 2022 Apr;70:1075-1079.

9. Zhang J, Zou H. Artificial intelligence technology for myopia challenges: A review. Front Cell Dev Biol. 2023 Jan 17;11:1124005.

10. Zhang C, Zhao J, Zhu Z, et al. Applications of Artificial Intelligence in Myopia: Current and Future directions. Front Med (Lausanne). 2022 Mar 11;9:840498.

11. Zhang J, Zou H. Insights in artificial intelligence in myopia management: from a data perspective. Graefes Arch Clin Exp Ophthalmol. 2023 May 25:1-15.

12. Lin H, Long E, Ding X, et al. Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study. PLoS Med. 2018 Nov 6;15:e1002674.

13. Yang X, Chen G, Qian Y, et al. Prediction of Myopia in Adolescents through Machine Learning Methods. Int J Environ Res Public Health. 2020 Jan 10;17:463.

14. Li SM, Ren MY, Gan J, et al. Machine Learning to Determine Risk Factors for Myopia Progression in Primary School Children: The Anyang Childhood Eye Study. Ophthalmol Ther. 2022 Apr;11:573-585.

15. Tang T, Yu Z, Xu Q, et al. A machine learning- based algorithm used to estimate the physiological elongation of ocular axial length in myopic children. Eye Vis (Lond). 2020 Oct 22;7:50.

16. Varadarajan AV, Poplin R, Blumer K, et al. Deep Learning for Predicting Refractive Error From Retinal Fundus Images. Invest Ophthalmol Vis Sci. 2018 Jun 1;59:2861-2868.

17. Lu L, Ren P, Tang X, et al. AI-Model for Identifying Pathologic Myopia Based on Deep Learning Algorithms of Myopic Maculopathy Classification and "Plus" Lesion Detection in Fundus Images. Front Cell Dev Biol. 2021 Oct 15;9:719262.

18. Ye X, Wang J, Chen Y, et al. Automatic Screening and Identifying Myopic Maculopathy on Optical Coherence Tomography Images Using Deep Learning. Transl Vis Sci Technol. 2021 Nov 1;10:10.

19. Foo LL. Artificial Intelligence and Fundus Photography Can Predict High Myopia. Rev Myopia Management. 2022 Oct 17.

20. Fan Y, Yu Z, Tang T, et al. Machine learning based strategy surpasses the traditional method for selecting the first trial Lens parameters for corneal refractive therapy in Chinese adolescents with myopia. Cont Lens Anterior Eye. 2021 Jun;45:101330.

21. Gunasekeran DV, Wong TY. Artificial Intelligence in Ophthalmology in 2020: A Technology on the Cusp for Translation and Implementation. Asia Pac J Ophthalmol (Phila). 2020;9:61-66.

22. Foo LL, Ang M, Wong CW, et al. Is artificial intelligence a solution to the myopia pandemic? Br J Ophthalmol. 2021 Jun;105:741-744.