AI Advancements in Early Myopia Detection and Management

The increasing prevalence of myopia is a significant global health issue, with high myopia posing a serious risk of vision damage. This necessitates the use of advanced technologies, particularly artificial intelligence (AI), for early diagnosis, prevention, and management of myopia. A recent review in the journal *Pediatric Investigation* highlights the potential applications and challenges of AI in this field.

Myopia, or nearsightedness, affects two billion people globally. If left uncorrected, myopia can impair vision, disrupt education, and negatively impact career prospects and quality of life. By 2050, nearly half of the global population is expected to become myopic. High myopia is often associated with complications that can lead to visual impairment, affecting patients' quality of life and increasing the global medical and economic burden. Therefore, early diagnosis of myopia is essential for preventing vision damage.

Artificial intelligence has opened new frontiers in the medical field, offering solutions to this global health concern. Machine learning (ML) and deep learning (DL) can analyze data to diagnose diseases, predict risk factors, biomarkers, and outcomes. The review, conducted by Dr. Li Li, Dr. Jifeng Yu, and Dr. Nan Liu from the Department of Ophthalmology at Capital Medical University, China, summarizes the applications and challenges of AI in myopia, including detection, risk factor assessment, and prediction models.

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 learn 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.

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 can access an online database of images, which the AI can use as a reference to diagnose myopia. Additionally, AI can be trained to detect behavioral changes associated with the onset of myopia. The Vivior monitor, for example, uses ML algorithms to note changes in visual behaviors, such as time spent on near vision activities, in children aged 6-16 years.

Machine learning methods like support vector machine, logistic regression, and XGBoost can identify risk factors of myopia. Dr. Li Li explains, '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.'

Predicting the progression and outcome of myopia can help doctors adjust their clinical approach. On a large scale, it can shape clinical practice and policymaking for 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 predict outcomes in new patients.

Despite the great potential of AI in myopia, several challenges need to be addressed. Ensuring the dataset used to train an AI model is correct and of high quality is crucial. Bias, false negatives/positives, and poor data quality can negatively impact diagnostic and prediction accuracy. Most AI models are trained using data from large hospitals, which may not be representative of patients in smaller clinics, creating a discrepancy between real-world and training populations. Additionally, AI models may not provide a clinical basis for their diagnosis, which can be rejected by medical professionals. Protecting patient privacy with vast quantities of data is also essential.

Dr. Jifeng Yu concludes, '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 enhancing human-computer interaction, AI models can be further improved for widespread clinical application.'

In summary, AI is a promising tool in the early detection and management of myopia. Addressing the challenges will be crucial for its widespread adoption in clinical settings, ultimately improving patient outcomes and reducing the global burden of myopia.