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Through a close collaboration with medical doctors and AI researchers, high-quality research has been conducted for developing a new clinical decision support system for ophtalmic clinicians.
Research Partner
Our partner, Lux Mind, is a medical AI company specializing in ophthalmic diagnosis and clinical decision support. Their work focuses on multimodal learning and human-centered medical AI to advance accurate and innovative eye-care technologies.
Research Goal
Our main research goal is to develop a novel Clinical Decision Support System (CDSS) for ophthalmic clinicians. For this purpose, we predict various diseases in the retina or segment retinal tissue to detect pathological tissue, and also to observe and predict prognosis after administering certain drugs to dialysis patients using SD-OCT images.
Members - Medical Experts
Duck-Jin Hwang
Duck-Jin Hwang
The Global RETFound Consortium
Head of Retina Center /
Head of AI Big Data Center /
Professor
Hangil Eye Hospital
daniel.dj.hwang@gmail.com
Ji In Park
Ji In Park
Professor
Kangwon National University Hospital
parkjiin@gmail.com
Jeong Mo Han
Jeong Mo Han
Chief Director
Seoul Bombit Eye Clinic
joehan712@gmail.com
Joon Seo Hwang
Joon Seo Hwang
Chief Director
Seoul Plus Eye Clinic
poppn78@daum.net
Members - AI Researchers
Jinyoung Han
Jinyoung Han
The Global RETFound Consortium
Associate Professor
jinyounghan@skku.edu
Post-doc @ DSAIL
CDSS
mgyang@g.skku.edu
Hyunseon Won
Hyunseon Won
M. Sc. Student
CDSS
dprth1014@g.skku.edu
JiAn Park
JiAn Park
M. Sc. Student
Retina & Diseases
peugeot@g.skku.edu
JeeEun Kim
JeeEun Kim
M. Sc. Student
Retina & Diseases
wldms15@g.skku.edu
Alumni
Ph. D.
CSC Classification / CDSS
Current: CTO @ Raondata
yoonjeewoo@g.skku.edu
M. Sc. / Ph. D. Student
AMD Classification
Current: CEO @ Raondata
choiseong@g.skku.edu
M. Sc.
3D Retina Disease Classification
Current: ML engineer @ Healing Paper
sta06167@g.skku.edu
Jiwon Kim
Jiwon Kim
M. Sc.
Retina Segmentation
Current: AI engineer @ LG Electronics
jjeon416@g.skku.edu
M. Sc.
Retina Segmentation
Current: AI research scientist @ VUNO
jhjeon9@g.skku.edu
Seoyoon Jeong
Seoyoon Jeong
M. Sc. Student
Retina & Diseases
Current: Master of Biomedical Engineering @ Columbia University
jsyoon0503@g.skku.edu
News
[November 27, 2024]
Our research has been featured in Ophthalmology Times, a premier media platform dedicated to advancing the field of ophthalmology through the latest news, insights, and cutting-edge developments. This research holds immense promise for clinical applications, enabling ophthalmologists to tailor treatment strategies to individual patients' needs more effectively and offering hope for improved outcomes in a condition that remains a leading cause of blindness worldwide. We are proud to contribute to the future of personalized medicine in ophthalmology and to have our work spotlighted by Ophthalmology Times, a trusted voice in the global ophthalmology community.
Publications
[Report Generation] "LASOR: Towards Clinically Transparent and Explainable Ophthalmic Report Generation via Lesion-Aware Segmentation," WACV, 2026.
[Medical Foundation Model] "Building the world’s first truly global medical foundation model", Nature medicine, 2025.
[Segmentation] "CAMEL: Confidence-Aware Multi-task Ensemble Learning with Spatial Information for Retina OCT Image Classification and Segmentation," WACV, 2025.
[Recurrence] "Prediction of neovascular age-related macular degeneration recurrence using optical coherence tomography images with a deep neural network", Scientific Reports, 2024.
[CDSS] "Developing and Evaluating an AI-Based Computer-Aided Diagnosis System for Retinal Disease: Diagnostic Study for Central Serous Chorioretinopathy", Journal of Medical Internet Research (JMIR), 2023.
[MyopicCNV] "Prediction of Visual Acuity in Pathologic Myopia with Myopic Choroidal Neovascularization Treated with Anti-Vascular Endothelial Growth Factor Using a Deep Neural Network Based on Optical Coherence Tomography Images", Biomedicines, 2023.
[CSC + nAMD] "Detecting Macular Disease Based on Optical Coherence Tomography Using a Deep Convolutional Network", Journal of Clinical Medicine, 2023.
[CSC] "Assessing central serous chorioretinopathy with deep learning and multiple optical coherence tomography images", Scientific Reports, 2022.
[CSC] "Classifying central serous chorioretinopathy subtypes with a deep neural network using optical coherence tomography images: a cross-sectional study", Scientific Reports, 2022.
[nAMD] "Classifying neovascular age-related macular degeneration with a deep convolutional neural network based on optical coherence tomography images", Scientific Reportsm 2022.
[nAMD] "Distinguishing retinal angiomatous proliferation from polypoidal choroidal vasculopathy with a deep neural network based on optical coherence tomography", Scientific Reports, 2021.
[CSC] "Optical coherence tomography-based deep-learning model for detecting central serous chorioretinopathy", Scientific Reports, 2020.
Funding
This work is supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2023R1A2C2007625).
※ MSIT : Ministry of Science and ICT
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