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Related Concept Videos

Glaucoma: Overview01:25

Glaucoma: Overview

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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

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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.
Drugs such as carbonic anhydrase inhibitors, α2- and...
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Angle Closure Glaucoma: Treatment01:28

Angle Closure Glaucoma: Treatment

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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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Classification of Illness01:17

Classification of Illness

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
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Related Experiment Video

Updated: Sep 22, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Evaluating machine learning classifiers for glaucoma referral decision support in primary care settings.

Omkar G Kaskar1, Elaine Wells-Gray2, David Fleischman3

  • 1North Carolina State University, Raleigh, NC, 27695, USA.

Scientific Reports
|May 20, 2022
PubMed
Summary
This summary is machine-generated.

Predicting glaucoma risk is possible using non-ocular factors like age and blood pressure, alongside intraocular pressure. This approach could aid early detection in primary care settings.

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Public Health

Background:

  • Glaucoma diagnosis often relies on ocular imaging, requiring specialized datasets and expertise.
  • Existing artificial intelligence (AI) algorithms for glaucoma detection necessitate access to detailed ocular data.
  • A need exists for accessible methods to identify individuals at risk of glaucoma, particularly in non-specialist settings.

Purpose of the Study:

  • To model and evaluate classifiers for predicting self-reported glaucoma.
  • To assess the utility of intraocular pressure (IOP) and non-ocular features for glaucoma risk prediction.
  • To determine the potential of these classifiers for early glaucoma detection in primary care.

Main Methods:

  • Utilized publicly available data from 3015 subjects without initial glaucoma diagnosis.
  • Trained classifiers (Support Vector Machine, Logistic Regression, Adaptive Boosting) using baseline IOP, age, gender, race, BMI, blood pressure, and comorbidities.
  • Evaluated classifier performance in identifying 337 subjects who later self-reported glaucoma, using only enrollment data.

Main Results:

  • Classifiers achieved similar performance, with F1 scores around 0.28-0.31.
  • Logistic regression demonstrated the highest sensitivity (60%) and specificity (69%).
  • Non-ocular features, combined with IOP, showed potential for predicting glaucoma risk.

Conclusions:

  • Predictive models using primarily non-ocular features can identify individuals suspected of having glaucoma.
  • These models may be valuable for screening in non-eye care settings, such as primary care.
  • Further research is needed to incorporate additional features to enhance predictive accuracy.