Applications of Artificial Intelligence in Choroid Visualization for Myopia: A Comprehensive Scoping Review.

Abstract

Numerous artificial intelligence (AI) models, including deep learning techniques, are being developed to segment choroids in optical coherence tomography (OCT) images. However, there is a need for consensus on which specific models to use, requiring further synthesis of their efficacy and role in choroid visualization in myopic patients. A systematic literature search was conducted on three main databases (PubMed, Web of Science, and Scopus) using the search terms: "Machine learning" OR "Artificial Intelligence" OR "Deep learning" AND "Myopia" AND "Choroid" OR "Choroidal" from inception to February 2024 removing duplicates. A total of 12 studies were included. The populations included myopic patients with varying degrees of myopia. The AI models applied were primarily deep learning models, including U-Net with a bidirectional Convolutional Long Short-Term Memory module, LASSO regression, Attention-based Dense U-Net network, ResNeSt101 architecture training five models, and Mask Region-Based Convolutional Neural Network. The reviewed AI models demonstrated high diagnostic accuracy, including sensitivity, specificity, and area under the curve values, in identifying and assessing myopia-related changes. Various biomarkers were assessed, such as choroidal thickness, choroidal vascularity index, choroidal vessel volume, luminal volume, and stromal volume, providing valuable insights into the structural and vascular changes associated with the condition. The integration of AI models in ophthalmological imaging represents a significant advancement in the diagnosis and management of myopia. The high diagnostic accuracy and efficiency of these models underscore their potential to revolutionize myopia care, improving patient outcomes through early detection and precise monitoring of disease progression. Future studies should focus on standardizing AI methodologies and expanding their application to broader clinical settings to fully realize their potential in ophthalmology.