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: Uncorrected refractive error affects approximately 19 million children globally, resulting in preventable vision loss. However, cycloplegic refraction, the gold standard for assessment, remains largely inaccessible in low-resource setting...
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).
PURPOSE: To assess the accuracy of artificial intelligence (AI)-based intraocular lens (IOL) power calculation formulas compared with traditional methods in highly myopic eyes, and to evaluate their performance across varying axial lengths and cornea...
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 ...
PURPOSE: We developed an artificial intelligence program for calculating intraocular lenses and analyzed its accuracy rate via ultrasonic biometry. This endeavor is aimed at enhancing precision and efficacy in the selection of intraocular lenses, par...
PURPOSE: To systematically assess and compare the accuracy of artificial intelligence (AI) -based intraocular lens (IOL) power calculation formulas with traditional IOL formulas in patients with short eye length.
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