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Related Concept Videos

Glaucoma: Overview01:25

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: Treatment01:27

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...
Angle Closure Glaucoma: Treatment01:28

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...

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Related Experiment Video

Updated: Jul 3, 2026

Application of Optical Coherence Tomography to a Mouse Model of Retinopathy
08:22

Application of Optical Coherence Tomography to a Mouse Model of Retinopathy

Published on: January 12, 2022

Machine Learning With Optical Coherence Tomography for Glaucoma Diagnosis.

Vital P Costa1, Camila Zangalli1, Edson S Gomi2

  • 1Universidade Estadual de Campinas, Campinas, Brazil.

Journal of Glaucoma
|July 1, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning classifiers accurately diagnosed glaucoma using combined optical coherence tomography (OCT) measurements of retinal nerve fiber layer thickness (RNFLT) and minimum rim width (MRW). These combined parameters improved diagnostic accuracy compared to RNFLT alone.

Keywords:
glaucomamachine learning classifiersoptical coherence tomography

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Last Updated: Jul 3, 2026

Application of Optical Coherence Tomography to a Mouse Model of Retinopathy
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Published on: January 12, 2022

Doppler Optical Coherence Tomography of Retinal Circulation
10:46

Doppler Optical Coherence Tomography of Retinal Circulation

Published on: September 18, 2012

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Glaucoma diagnosis relies on detecting optic nerve damage.
  • Spectral-domain optical coherence tomography (OCT) provides quantitative measurements of the optic nerve head and peripapillary region.
  • Machine learning classifiers (MLC) show promise in analyzing complex medical data for diagnostic purposes.

Purpose of the Study:

  • To evaluate the diagnostic performance of various MLCs.
  • To compare MLCs trained with retinal nerve fiber layer thickness (RNFLT) and minimum rim width (MRW) from OCT scans.
  • To determine if combined RNFLT and MRW data enhance diagnostic accuracy for glaucoma.

Main Methods:

  • Included 113 eyes with mild to moderate glaucoma and 154 healthy eyes.
  • Extracted global average RNFLT and MRW measurements from OCT scans.
  • Trained ten MLC algorithms using RNFLT data only, MRW data only, and combined RNFLT and MRW data.
  • Utilized receiver operating characteristic (ROC) curves and area under the curve (AUC) to assess diagnostic performance.

Main Results:

  • The Random Forest MLC achieved the highest AUC (0.979) when trained with both RNFLT and MRW.
  • Combined RNFLT and MRW data yielded significantly higher AUC than RNFLT alone (P=0.029).
  • Specific sensitivities and specificities were reported for top-performing models using individual and combined parameters.

Conclusions:

  • MLCs integrating RNFLT and MRW measurements demonstrate high diagnostic accuracy for glaucoma.
  • Combining OCT-derived RNFLT and MRW parameters offers superior diagnostic performance compared to RNFLT alone.
  • This approach holds potential for improving early glaucoma detection and management.