Artificial intelligence in myopia in children: current trends and future directions

Area of Science:

• Ophthalmology
• Artificial Intelligence
• Public Health

Background:

• Childhood myopia is a significant global health concern, leading to visual impairment and substantial healthcare costs.
• Early intervention is crucial for slowing myopia progression, necessitating effective management strategies.
• Artificial intelligence (AI) presents a transformative potential for addressing childhood myopia.

Purpose of the Study:

• To review the evolving role of AI in childhood myopia management.
• To highlight AI's application in risk identification, screening, and treatment.
• To discuss future AI modalities and their potential impact on precision medicine and data privacy.

Main Methods:

• Review of recent literature on AI applications in childhood myopia.
• Analysis of AI's use in traditional machine learning and deep learning models.
• Exploration of big data, multimodal data, and advanced imaging in myopia research.

Main Results:

• AI research initially focused on identifying high-risk children using traditional machine learning.
• Recent advancements utilize AI with larger datasets and complex models for large-scale screening and precision treatment.
• AI is being applied to big data, multimodal data, and advanced imaging for myopia progression analysis.

Conclusions:

• AI holds significant promise for revolutionizing childhood myopia management.
• Emerging AI techniques like automated machine learning and federated learning can enhance precision medicine and data privacy.
• Challenges in regulation and clinical integration must be addressed for widespread AI adoption in myopia care.