Adopting machine learning to automatically identify candidate patients for corneal refractive surgery. - Tae Keun Yoo; Ik Hee Ryu; Geunyoung Lee; Youngnam Kim; Jin Kuk Kim; In Sik Lee; Jung Sub Kim; Tyler Hyungtaek Rim (npj Digit. Medicine, 2019)

Deep learning can generate traditional retinal fundus photographs using ultra-widefield images via generative adversarial networks. - Tae Keun Yoo; Ik Hee Ryu; Jin Kuk Kim; In Sik Lee; Jung Sub Kim; Hong Kyu Kim; Joon Yul Choi (Comput. Methods Programs Biomed., 2020)

Adopting low-shot deep learning for the detection of conjunctival melanoma using ocular surface images. - Tae Keun Yoo; Joon Yul Choi; Hong Kyu Kim; Ik Hee Ryu; Jin Kuk Kim (Comput. Methods Programs Biomed., 2021)

A deep learning approach for detection of shallow anterior chamber depth based on the hidden features of fundus photographs. - Tae Keun Yoo; Ik Hee Ryu; Jin Kuk Kim; In Sik Lee; Hong Kyu Kim (Comput. Methods Programs Biomed., 2022)

Deep learning prediction of steep and flat corneal curvature using fundus photography in post-COVID telemedicine era. - Joon Yul Choi; Hyungsu Kim; Jin Kuk Kim; In Sik Lee; Ik Hee Ryu; Jung Soo Kim; Tae Keun Yoo (Medical Biol. Eng. Comput., 2024)

Development of a generative deep learning model to improve epiretinal membrane detection in fundus photography. - Joon Yul Choi; Ik Hee Ryu; Jin Kuk Kim; In Sik Lee; Tae Keun Yoo (BMC Medical Informatics Decis. Mak., 2024)