BACKGROUND: The evaluation of refraction is indispensable in ophthalmic clinics, generally requiring a refractor or retinoscopy under cycloplegia. Retinal fundus photographs (RFPs) supply a wealth of information related to the human eye and might pro...
Diabetic retinopathy (DR) at risk of vision loss (referable DR) needs to be identified by retinal screening and referred to an ophthalmologist. Existing automated algorithms have mostly been developed from images acquired with high cost mydriatic ret...
PURPOSE: To develop and validate machine learning (ML) models for predicting cycloplegic refractive error and myopia status using noncycloplegic refractive error and biometric data.
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
39378966
PURPOSE: To describe choroidal thickness measurements using a sequential deep learning segmentation in adults who received childhood atropine treatment for myopia control.
Optometry and vision science : official publication of the American Academy of Optometry
39993303
SIGNIFICANCE: Machine learning random forest algorithms were used to predict objective refractive outcomes after cycloplegic refraction using noncycloplegic clinical data. A classification model predicted post-cycloplegic myopia and could be useful i...