Comprehensive Summary
This article has a focus on the increasing role of AI in detection and management in opthalmology, specifically pediatric retinal diseases. The paper is organized by disease area, summarizing the types of AI models that had started running clinical trials. The primary areas looked over were Retinopathy of Prematurity (ROP), Myopia, Diabetic Retinopathy (DR), and Retinoblastoma. Each section summarizes the issue at hand, comparing different algorithms and photography methods needed to input accurate data. They then compared efficiency, accuracy, and other factors regarding usage to formulate a more structured plan moving foward. When looking towards future directions, the main focus was to develop a model that could be integrated into widely accessible platforms, transforming retinal care through the enhancement of current tools.
Outcomes and Implications
This review highlights how AI usage is on the rise in pediatric retinal care, including conditions such as myopia, diabetic retinopathy, and retinoblastoma. Over time, these AI systems have shown high accuracy in detection of disease, along with the ability to monitor progression. This technology can improve the likelihood of early diagnosis, all while being less of a hassle, both finanically and timewise. Integration into smartphone based software can expand the scope of pediatric retinal care, making it more accessible worldwide, reducing variability between doctors. There are still some issues that need to be addressed around special cases, bias, and integration before being validated for a clinical setting, but the use of AI is trending upwards.