APAO on Demand

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Deep Learning Based Glaucoma Progression Prediction Guided by Generative Algorithms

Author : Shaista Hussain
A Non-invasive Method to Analyze Spontaneous Blink Using a Commercially-Available Camera and Machine Learning Approach

Author : Watsaporn Pornwatanacharoen
Comparison of Retinal, Slit-Lamp and Anterior Diffuse Photograph-Based Deep Learning Models in Detecting Visually Significant Cataract

Author : Yih-Chung Tham
Evaluating the Accuracy of Conditional Class Generation of Synthetic Diabetic Retinopathy Fundus Images Using Style-Based Generative Network

Author : Gilbert Lim
A Multi-Modal Explainable Artificial Intelligence (XAI) System to Personalize Diabetic Retinopathy (DR) Screening

Author : Feihui ZHENG
Application of Artificial Intelligence-Based Dual-Modality Analysis Combining Fundus Photography and Optical Coherence Tomography in Diabetic Retinopathy Screening in Community Hospitals

Author : Qingchen Li
Smartphone-Acquired Anterior Segment Images for Deep Learning Prediction of Anterior Chamber Depth: A Proof-of-Concept Study

Author : Chaoxu Qian
AI-based Clinical Assessment of Optic Nerve Head Robustness Superseding Biomechanical Testing

Author : Fabian Braeu
The Philippine Peso Bill as an Alternative Near Visual Acuity Chart in Filipino Eyes: A Pilot Study

Author : Lee Jerome Briones
Risk of Autoimmune Diseases Following Optic Neuritis: A Nationwide Population-Based Cohort Study

Author : Chee Ming Lee
Automated Classification of Angle Closure Mechanisms Based on Anterior Segment Optical Coherence Tomography Images via Deep Learning

Author : Ye Zhang
Explainable AI for Glaucoma Imaging

Author : Jin Wook Jeoung
Introduction to National Eye Institute (NEI) and Strategic Priorities

Author : Michael CHIANG
Challenges and Unanswered Questions in Myopia

Author : Michael CHIANG
Image Standardization

Author : Aaron Lee

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APAO Secretariat
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c/o APAO Office, State Key Laboratory (Ophthalmology)

Zhongshan Ophthalmic Center, Sun Yat-Sen University

Zhujiang New Town, Tianhe District

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1. Tel: (852) 3943-5827
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