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Related Concept Videos

Glaucoma: Overview01:25

Glaucoma: Overview

584
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...
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Open Angle Glaucoma: Treatment01:27

Open Angle Glaucoma: Treatment

455
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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Angle Closure Glaucoma: Treatment01:28

Angle Closure Glaucoma: Treatment

514
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...
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Related Experiment Video

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Glaucoma Prediction Models Based on Ocular and Systemic Findings.

Daphna Landau Prat1,2,3, Noa Kapelushnik1,3, Mattan Arazi4,5

  • 1Goldschleger Eye Institute, Sheba Medical Center, Ramat Gan, Israel.

Ophthalmic Research
|December 18, 2023
PubMed
Summary

Machine learning models effectively predict glaucoma development within three years using systemic and ocular factors. Key predictors include intraocular pressure, cup-to-disk ratio, age, and novel indicators like mean corpuscular volume trends.

Keywords:
Artificial intelligenceGlaucoma prediction modelMachine learningOcular predictors of glaucomaSystemic factors and glaucoma

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Area of Science:

  • Ophthalmology
  • Medical Informatics
  • Predictive Analytics

Background:

  • Glaucoma prediction is crucial for timely intervention.
  • Identifying predictive systemic and ocular factors aids early diagnosis.
  • Machine learning offers advanced tools for complex medical predictions.

Purpose of the Study:

  • To explore the predictive power of systemic and ocular findings for glaucoma development.
  • To identify key features associated with glaucoma progression using machine learning.
  • To develop robust models for predicting glaucoma onset within a three-year timeframe.

Main Methods:

  • Analysis of 37,692 patient records from 2001-2020 using machine learning algorithms.
  • Inclusion of systemic and ocular features in univariate and multivariate analyses.
  • Application of CatBoost and Light gradient-boosting machine models for prediction.

Main Results:

  • A combined model achieved an ROC AUC of 0.84, identifying intraocular pressure, cup-to-disk ratio, and age as primary predictors.
  • An ocular-only model reached an ROC AUC of 0.86, with age-related macular degeneration and anterior capsular cataract as significant factors.
  • Novel indicators identified include mean corpuscular volume (MCV) trends, urinary system disease, and monocyte count trends.

Conclusions:

  • Both ocular and combined systemic-ocular models demonstrate strong predictive capability for glaucoma within three years.
  • Anterior subcapsular cataracts, urinary disorders, and specific blood test results (MCV, monocyte count) are novel indicators of glaucoma progression.
  • Machine learning models provide a powerful framework for enhancing glaucoma risk assessment.