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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.
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Related Experiment Video

Updated: Jan 12, 2026

Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter
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The Postoperative Hyperopic Shift Risk Prediction Model for Primary Angle Closure Glaucoma Patients Based on Machine

Di Gong1,2, Yong Liu1,2, Kuanrong Dang1,2

  • 1Southern Medical University, Shenzhen Eye Medical Center, Shenzhen Eye Hospital, Shenzhen, Guangdong, China.

Journal of Glaucoma
|November 3, 2025
PubMed
Summary

This study developed a machine learning model to predict hyperopic shift (HS) after glaucoma surgery in primary angle closure glaucoma (PACG) patients. The model aids in personalized surgical decisions and early warnings for better patient outcomes.

Keywords:
Borutahyperopic shiftmachine learningprediction modelprimary angle closure glaucoma

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Area of Science:

  • Ophthalmology
  • Medical Informatics
  • Glaucoma Research

Background:

  • Primary angle closure glaucoma (PACG) poses unique challenges for intraocular lens (IOL) implantation.
  • Hyperopic shift (HS) is a potential complication after phacoemulsification combined with IOL (PE+IOL) surgery in PACG patients.
  • Accurate prediction of HS is crucial for optimizing surgical outcomes and patient management.

Purpose of the Study:

  • To develop and validate a machine learning-based risk prediction model for HS after PE+IOL surgery in PACG patients.
  • To identify key clinical variables that predict HS risk.
  • To provide a tool for individualized surgical decision-making and early warning.

Main Methods:

  • Retrospective cohort study of 423 PACG patients undergoing PE+IOL surgery.
  • Feature selection using the Boruta algorithm.
  • Development and evaluation of machine learning models (SVM, LR, Random Forest, KNN, XGBoost) using ROC curves and AUC.

Main Results:

  • Key predictive variables for HS included target refraction, BCVA, AL, CCT, ACD, LT, W2W, and pupil diameter.
  • Support Vector Machine (SVM) and Logistic Regression (LR) models demonstrated the best predictive accuracy.
  • Achieved AUCs of 0.704 for SVM and 0.696 for LR, indicating moderate classification ability.

Conclusions:

  • A validated machine learning model effectively predicts HS risk in PACG patients post-PE+IOL surgery.
  • The SVM and LR models show potential for clinical application in personalized postoperative management.
  • This tool can assist surgeons in tailoring treatment strategies for glaucoma surgery patients.