AI Innovations Enhance Early Detection and Management of Myopia

The escalating prevalence of myopia, or nearsightedness, has emerged as a significant global health challenge, affecting approximately two billion individuals worldwide. High myopia is particularly concerning, as it increases the risk of severe vision impairment and associated complications. According to the World Health Organization (WHO), projections indicate that by 2050, nearly half of the global population may be myopic, necessitating urgent interventions for early diagnosis and management.

In a recent review published in the *Pediatric Investigation*, Dr. Li Li, Dr. Jifeng Yu, and Dr. Nan Liu from the Department of Ophthalmology at Capital Medical University in China, have emphasized the transformative potential of artificial intelligence (AI) technologies in addressing this pressing health issue. The study, published on March 18, 2025, explores various applications of AI, including risk assessment, prediction models, and early detection techniques for myopia, particularly in children, who are often overlooked in traditional diagnostic processes.

The authors highlight that AI subsets such as machine learning (ML) and deep learning (DL) can analyze extensive datasets to enhance diagnostic accuracy. For instance, AI models can be trained using a vast array of fundus images from myopic patients, enabling them to identify subtle retinal changes indicative of myopia. This capability allows for earlier and more accurate diagnoses, which is crucial in preventing the progression of the condition. Dr. Li Li stated, "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."

One innovative device, known as SVOne, incorporates AI algorithms to detect refractive errors through wavefront sensing. This device could revolutionize how myopia is diagnosed by leveraging an extensive online database of ocular images for reference. Furthermore, the Vivior monitor utilizes ML algorithms to track visual behavior changes in children aged 6-16 years, providing insights into their near-vision activities—a critical factor in the onset of myopia.

Despite these advancements, several challenges remain in the widespread implementation of AI technologies in myopia management. Dr. Jifeng Yu emphasized the necessity for high-quality datasets to train AI models effectively. He expressed concern that biases in data, particularly from large hospitals, may not accurately represent the broader patient population, especially those attending smaller clinics. Moreover, there is a pressing need to ensure patient privacy when handling extensive medical records.

The implications of successfully integrating AI into myopia management are profound, potentially reshaping clinical practices and public health policies aimed at controlling this burgeoning epidemic. As Dr. Nan Liu remarked, "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."

In conclusion, the application of AI in the early detection and management of myopia represents a significant leap forward in ophthalmic care, with the potential to protect millions from vision impairment. Continued research and development are essential to refine these technologies and ensure their efficacy in diverse clinical settings. As the world grapples with the increasing burden of myopia, the innovative use of AI may provide the much-needed solutions to prevent its adverse outcomes and improve quality of life for affected individuals.