Artificial intelligence models have immense potential for diagnosing myopia, assessing its risk factors, and predicting its outcomes.
Myopia, or nearsightedness, currently affects over two billion people worldwide. When left uncorrected, it can significantly impair vision, disrupting education, employment opportunities, and overall quality of life. By 2050, nearly half of the global population is expected to be affected by myopia. In particular, high myopia is associated with serious complications that can lead to permanent vision loss, further increasing both the personal and economic burden.
Early diagnosis is essential to prevent long-term visual damage and manage the progression of myopia effectively.
Artificial intelligence (AI) has emerged as a promising tool in addressing this growing public health issue. Technologies within AI, such as machine learning (ML) and deep learning (DL), can analyze complex medical data to support disease diagnosis, identify risk factors and biomarkers, and predict clinical outcomes.
In a recent literature review published on March 18, 2025, in the journal Pediatric Investigation, Dr. Li Li, Dr. Jifeng Yu, and Dr. Nan Liu from the Department of Ophthalmology at Capital Medical University, China, explore the applications and current challenges of AI in the context of myopia. Their review highlights how AI can aid in detection, risk assessment, and the development of predictive models to improve patient care.
AI for Myopia Detection
Interestingly, AI models can be trained using ML/DL to detect myopia from fundus photos and optical coherence tomography images. By feeding a model with a large quantity of fundus images from myopic patients, the AI can be taught to discern minute changes in color and pattern in the retina that are associated with myopia. This allows the model to diagnose future patients from their fundus photos.
In addition, self-monitoring equipment such as SVOne, a handheld device that uses a wavefront sensor to measure eye defects, can use AI algorithms to detect refractive errors in the eyes. The device could access an online database of images, which the AI can use as a reference to diagnose myopia. Moreover, AI can be trained to detect behavioral changes associated with the onset of myopia. Such detection is especially useful for the early detection of myopia in children, which is often ignored otherwise. For example, the Vivior monitor uses ML algorithms to note changes in visual behaviors, such as time spent on near vision activities, in children aged 6–16 years.
Identifying and Assessing Risk Factors
Furthermore, ML methods like support vector machine, logistic regression, and XGBoost can be employed to identify risk factors of myopia.
“An XGBoost-based model can be fed large quantities of longitudinal data, allowing it to learn the outcomes and associated risk factors of myopia in numerous patients. This, in turn, allows the model to assess the risk factors of new patients based on their genetics, family history, environment, and physiological parameters,” explains Dr. Li Li.
Predicting the progression and outcome of myopia can help doctors adjust their clinical approach. Taken on a large scale, it can shape clinical practice and policymaking that help in myopia control. By feeding an AI model large quantities of biometric data, refractive data, treatment responses, and ocular images from numerous myopia patients, the AI can be taught to predict outcomes of myopia in new patients.
Despite the great potential of AI in myopia, several challenges need to be overcome. Firstly, it is important to ensure that the dataset used to train an AI model is correct and of high quality. Bias, false negatives/positives, and poor data quality can negatively impact the diagnostic and prediction accuracy of the model. Secondly, most AI models are trained using data from large hospitals, which may not be representative of patients going to smaller clinics. This creates a discrepancy between real-world and training populations. Thirdly, an AI model is not a trained doctor and may not be able to provide a clinical basis for its diagnosis, which can cause the diagnosis to be rejected by medical professionals. Finally, with such vast quantities of patient data used to train AI models, it is important to ensure the privacy of patients’ medical records.
“While our study highlights the remarkable progress made in the clinical application of AI in myopia, further studies are needed to overcome the technological challenges. By building high-quality datasets, improving the model’s capacity to process multimodal image data, and improving human-computer interaction capability, the AI models can be further improved for widespread clinical application,” concludes Dr. Jifeng Yu.
Reference: “Application of artificial intelligence in myopia prevention and control” by Nan Liu, Li Li and Jifeng Yu, 18 March 2025, Pediatric Investigation.
DOI: 10.1002/ped4.70001
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