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Predicting Intraocular Pressure From Glaucoma Patients Receiving Medication Treatment Using Explainable Machine

Robert T James1, Wenke Liu2, Gadi Wollstein1,3,4

  • 1Departments of Ophthalmology and Radiology, Tech4Health Institute and Neuroscience Institute, New York University Grossman School of Medicine, NYU Langone Health, New York University, New York, New York, USA, nyu.edu.

Biomed Research International
|February 2, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Explainable AI predicts glaucoma treatment success by analyzing patient data. Key factors like Insulin-like Growth Factor 1 and LDL cholesterol impact intraocular pressure outcomes.

Keywords:
explainable machine learningglaucomainsulin-like growth factor 1 (IGF-1)intraocular pressure (IOP)low-density lipoprotein (LDL)

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Area of Science:

  • Ophthalmology
  • Medical Informatics
  • Computational Biology

Background:

  • Glaucoma is a neurodegenerative disease causing vision loss, with treatment aiming to reduce intraocular pressure.
  • Some patients do not respond to treatment, risking progressive vision loss.
  • Explainable machine learning offers tools for predicting treatment outcomes and identifying influencing factors.

Purpose of the Study:

  • To predict intraocular pressure in glaucoma patients undergoing medication treatment using explainable machine learning.
  • To identify key physiological and metabolic factors influencing treatment efficacy.

Main Methods:

  • Utilized UK Biobank data from 161 glaucoma patients (290 eyes).
  • Employed explainable machine learning, including eXtreme Gradient Boosting (XGBoost), to predict intraocular pressure.
  • Calculated SHapley Additive exPlanation (SHAP) values to determine feature importance and interactions.

    • Main Results:

    • XGBoost achieved an Area Under the Curve (AUC) of 0.708 when using combined demographic, physiometabolic, and medication data.
    • Insulin-like Growth Factor 1 (IGF-1), low-density lipoprotein (LDL), and lymphocyte count were the most significant predictors.
    • Strong interactions were observed between LDL and IGF-1 in predicting treatment outcomes.

    Conclusions:

    • Explainable AI, specifically XGBoost, can effectively predict intraocular pressure outcomes in glaucoma patients.
    • Blood levels of LDL and IGF-1 are crucial factors influencing the success of intraocular pressure-lowering treatments.