This study aimed to automate the extraction of local corneal topography (CT) features in myopic children undergoing orthokeratology (OK), evaluate their causal effects on axial length (AL) control, and develop a predictive model for AL progression.We...
The purpose of this study was to develop a machine learning-based model using quantitative color fundus photography (CFP) data to predict myopia risk in school-age children, based on the axial length/corneal curvature radius (AL/CR) ratio, and to ide...
Accurate intraocular lens (IOL) power calculation is critical in cataract surgery, especially in patients with extreme axial myopia where traditional formulas often yield inaccurate results. This study retrospectively evaluated the accuracy of two AI...
BACKGROUND: This study aimed to create machine learning (ML) models to predict the long-term uncorrected distance visual acuity (UDVA) in myopic eyes corrected by small incision lenticule extraction (SMILE).
Myopia is a growing global health concern, and early detection and intervention are crucial for preventing its onset and progression. This study aims to predict myopia onset one year in advance by integrating biometric measurements with multimodal im...
Intensive education systems are believed to contribute to high rates of myopia. This study examined whether near-viewing behaviors in college students differ based on their pre-college educational systems and whether these behaviors can be used to cl...
BACKGROUND: Myopia is a major cause of vision impairment. To improve the efficiency of myopia screening, this paper proposes a deep learning model, X-ENet, which combines the advantages of depthwise separable convolution and dynamic convolution to cl...
OBJECTIVE: This study aims to utilize artificial intelligence technology to conduct an in-depth analysis of fundus data from myopic children and adolescents, thoroughly exploring the correlation between retinal vascular parameters and axial length (A...
This study assessed the efficacy of various diagnostic indicators and machine learning (ML) models in predicting childhood myopia. A total of 2,365 children aged 5-12 years were included in the study. The participants were exposed to non-cycloplegic ...
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