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Angle Closure Glaucoma: Treatment

Angle Closure Glaucoma: Treatment

Angle-closure glaucoma, or closed-angle glaucoma, is an eye condition where the iris bulges out and blocks the iridocorneal angle, resulting in a buildup of aqueous humor and increased intraocular pressure. Immediate medical attention is necessary due to the sudden onset of symptoms. The treatment for angle-closure glaucoma includes short-term and long-term approaches. Short-term treatment involves using eye drops like pilocarpine to lower intraocular pressure by increasing aqueous humor...

Glaucoma: Overview

Glaucoma: Overview

Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...

Open Angle Glaucoma: Treatment

Open Angle Glaucoma: Treatment

In open-angle glaucoma, the iridocorneal angle remains open, but the trabecular meshwork becomes stiff, slowing down the outflow of aqueous humor. This causes a buildup of aqueous humor in the anterior chamber, leading to a sudden increase in intraocular pressure. The treatment for open-angle glaucoma focuses on reducing the elevated intraocular pressure by either decreasing the secretion of aqueous humor or increasing its outflow.
Drugs such as carbonic anhydrase inhibitors, α2- and...

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Related Experiment Video

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Full-Circle Cauterization of Limbal Vascular Plexus for Surgically Induced Glaucoma in Rodents

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Developing and validating an explainable clinlabomics-based machine-learning model for screening primary

Zhuqing Li¹, Jun Ren¹, Jianing Wu¹

¹Department of Clinical Laboratory, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China.

The EPMA Journal

|September 15, 2025

Summary

A new machine learning model uses routine blood test results to predict primary angle-closure glaucoma (PACG) risk. This clinlabomics approach enables early screening and personalized monitoring, improving glaucoma care accessibility.

Keywords:

AI Clinlabomics Glaucoma risk Improved individual outcomes Machine learning Patient stratification Predictive preventive personalized medicine (PPPM/ 3PM)Primary angle-closure glaucoma SHAP Screening

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Area of Science:

Ophthalmology and Artificial Intelligence
Predictive diagnostics using clinlabomics data

Background:

Primary angle-closure glaucoma (PACG) is a leading cause of irreversible blindness.
Current screening methods are reactive, detecting damage after symptoms appear and requiring specialized, resource-intensive imaging.
There is a critical need for early, accessible screening tools to prevent vision loss.

Purpose of the Study:

To develop and validate a novel clinlabomics-based machine learning model for early screening of PACG.
To stratify individuals at high risk for PACG, enabling targeted ophthalmic evaluations.
To align with predictive, preventive, and personalized medicine (PPPM/3PM) principles for glaucoma management.

Main Methods:

A multicenter, retrospective study involving over 3,700 participants (normal subjects and PACG patients).
Utilized clinical laboratory data from digital medical records for model development and validation (discovery, internal, external cohorts).
Compared 12 machine learning models, employed SHAP for feature reduction and model explanation, evaluating performance using AUC, AUCPR, sensitivity, specificity, and accuracy.

Main Results:

A final LightGBM (LGBM) model, utilizing six key features (TT, PDW, MCV, APTT, TC, PT), demonstrated high accuracy in screening PACG (AUC=0.91, accuracy=0.84).
The model maintained strong performance across internal (AUC=0.87) and external (AUC=0.85) validation cohorts.
The model was successfully translated into an accessible web application for practical dissemination.

Conclusions:

A clinically applicable clinlabomics-based model using routine blood parameters can effectively screen for PACG.
The model facilitates early risk identification, cost-effective population screening, and personalized risk assessment via explainable AI.
This approach enhances glaucoma care accessibility, particularly in resource-limited settings, by leveraging routine blood tests as predictive indicators.