Huang Xiaoqin

Predicting Glaucoma Before Onset Using a Large Language Model Chatbot.

The performance of ChatGPT4.0 in forecasting development of glaucoma 1 year before onset was reasonable.

Estimating the Severity of Visual Field Damage From Retinal Nerve Fiber Layer Thickness Measurements With Artificial Intelligence.

The proposed ANN model estimated MD from RNFL measurements better than multivariable linear regression model, random forest, support vector regressor, and 1-D CNN models.

An Objective and Easy-to-Use Glaucoma Functional Severity Staging System Based on Artificial Intelligence.

We discovered that 4 severity levels based on MD thresholds of -2.2, -8.0, and -17.3 dB, provides the optimal number of severity stages based on unsupervised and supervised machine learning.

Identifying Factors Associated With Fast Visual Field Progression in Patients With Ocular Hypertension Based on Unsupervised Machine Learning.

Unsupervised clustering can objectively identify OHT subtypes including those with fast VF worsening without human expert intervention.

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