Ran An Ran

A 3D Deep Learning System for Detecting Referable Glaucoma Using Full OCT Macular Cube Scans.

A 3D deep learning algorithm trained on macular OCT volumes without retinal disease to detect referable glaucoma performs better with retinal segmentation preprocessing and performs reasonably well across all levels of myopia.

Deep Learning for Glaucoma Detection and Identification of Novel Diagnostic Areas in Diverse Real-World Datasets.

A 3D convolutional neural network (CNN) trained on SD-OCT ONH cubes can distinguish glaucoma from normal cases in diverse datasets obtained from four different countries.

High Myopia Normative Database of Peripapillary Retinal Nerve Fiber Layer Thickness to Detect Myopic Glaucoma in a Chinese Population.

The HM-specific normative database is more capable of detecting HMG eyes than the SS OCT built-in database, which may be an effective tool for differential diagnosis between HMG and HM.

Can a Natural Image-Based Foundation Model Outperform a Retina-Specific Model in Detecting Ocular and Systemic Diseases?

DINOv2, a natural image-based model, outperformed retina-specific RETFound in ocular disease detection, while RETFound excelled in systemic disease prediction. This highlights the need to match model selection with specific clinical tasks.

Developing a privacy-preserving deep learning model for glaucoma detection: a multicentre study with federated learning.

The 3D FL model could leverage all the datasets and achieve generalisable performance, without data exchange across centres.