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Articles by Gilbert Lim
Functional blepharoptosis screening with generative augmented deep learning from external ocular photography
ABSTRACT Purpose To develop and validate a deep learning model for the detection of functional blepharoptosis from external ocular photographs, and to quantify the impact of augmenting the training data with synthetic images on model performance. Methods External ocular photographs of 771 eyes from patients aged ≥ 21 years seen at a tertiary oculoplastic clinic. including 639 with clinically diagnosed functional blepharoptosis and 132 without, were obtained and cropped.
Automated Tool Support for Glaucoma Identification With Explainability Using Fundus Images
Loading [a11y]/accessibility-menu.js Automated Tool Support for Glaucoma Identification With Explainability Using Fundus Images | IEEE Journals & Magazine | IEEE Xplore Skip to Main Content Deep Learning based Glaucoma Identification with Explainability Using Fundus Images Abstract:Glaucoma is a progressive eye condition that causes irreversible vision loss due to damage to the optic nerve.
Prediction of posterior elevation stability in keratoconus
Introduction Keratoconus is a corneal disease featuring progressive ectasia and thinning and typically occurs in teenagers. Aggravating irregular corneal astigmatism can lead to vision loss and compromise of life quality with the progression of keratoconus (Ferdi et al., 2019). It is one of the major causes of blindness.
The accuracy of using vergence formula to screen myopia in children: a cross-sectional study
Zhen Yi1* Chang Hong2 Huang Haikuo3 Wang Xinxin3 1National Engineering Research Center for Ophthalmology, Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Engineering Research Center of the Ministry of Education for Ophthalmic Diagnosis and Treatment Equipment and Materials, Beijing Tongren Hospital, Capital Medical University, Beijing, China 2Beijing He Eye Specialist Hospital, Beijing, China 3Beijing Minghao Technology Development Company, Beijing, China Objective: To...
Deep learning for detecting visually impaired cataracts using fundus images
1 Introduction Worldwide, the incidence of visual impairment is increasing (GBD, 2019 Blindness and Vision Impairment Collaborators, 2021), which is an important public health problem, with cataracts being the leading cause of visual impairment (Flaxman et al., 2017).
Comparison of bilateral differential characteristics of corneal biomechanics between keratoconus and normal eyes
1Department of Ophthalmology and Optometry, Eye and ENT Hospital, Fudan University, Shanghai, China 2NHC Key Laboratory of Myopia, Key Laboratory of Myopia, Fudan University, Chinese Academy of Medical Sciences, Shanghai, China 3Shanghai Research Center of Ophthalmology and Optometry, Shanghai, China 4Shanghai Engineering Research Center of Laser and Autostereoscopic 3D for Vision Care (20DZ2255000), Shanghai, China 5Shangqiu First People’s Hospital, Shangqiu, China Purpose: To compare...
Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy
Abstract Deep learning (DL) has been shown to be effective in developing diabetic retinopathy (DR) algorithms, possibly tackling financial and manpower challenges hindering implementation of DR screening. However, our systematic review of the literature reveals few studies studied the impact of different factors on these DL algorithms, that are important for clinical deployment in real-world settings.
Deep learning in estimating prevalence and systemic risk factors for diabetic retinopathy: a multi-ethnic study
Abstract In any community, the key to understanding the burden of a specific condition is to conduct an epidemiological study. The deep learning system (DLS) recently showed promising diagnostic performance for diabetic retinopathy (DR). This study aims to use DLS as the grading tool, instead of human assessors, to determine the prevalence and the systemic cardiovascular risk factors for DR on fundus photographs, in patients with diabetes.
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