Tun Tin Aung

Li Qi, Zhan Bohan, Liu Tingting, Han Yingxiang, Xin Sanfeng, Chen Zengsheng et al.

Automated OCT-based models show lamina cribrosa depth strongly predicts strain. Glaucomatous eyes exhibit lower strain, suggesting remodeling. This scalable tool offers patient-specific biomechanical insights for improved glaucoma risk stratification and monitoring.

Li Fei, Yang Yifan, Sun Xu, Qiu Zhen, Zhang Shihao, Tun Tin Aung et al.

A 3D deep-learning system effectively detected narrow angles and PAS from OCT scans. This automates glaucoma screening, potentially improving primary eye care for PACG risk.

Cheong Haris, Devalla Sripad Krishna, Pham Tan Hung, Zhang Liang, Tun Tin Aung, Wang Xiaofei et al.

DeshadowGAN, a deep learning tool, effectively removed blood vessel shadows from ONH OCT images, improving image quality. This enhances OCT analysis, potentially aiding diagnosis and prognosis in ocular pathologies.

Wang Xiaofei, Beotra Meghna R, Tun Tin Aung, Baskaran Mani, Perera Shamira, Aung Tin et al.

This study found horizontal eye movements cause significant optic nerve head strains, comparable to those from acute IOP elevation. This suggests eye movement-induced strain might contribute to optic nerve damage.