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Related Concept Videos

Glaucoma: Overview01:25

Glaucoma: Overview

926
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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Visual System01:26

Visual System

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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
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Related Experiment Video

Updated: Sep 28, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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A Structure-Related Fine-Grained Deep Learning System With Diversity Data for Universal Glaucoma Visual Field

Xiaoling Huang1, Kai Jin1, Jiazhu Zhu2,3,4

  • 1Department of Ophthalmology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Frontiers in Medicine
|April 4, 2022
PubMed
Summary

An artificial intelligence system, FGGDL, accurately grades glaucoma visual field loss, aiding diagnosis. This deep learning tool shows promise for telemedicine and patient self-assessment.

Keywords:
artificial intelligencedeep learningglaucomagradingtelemedicinevisual field

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Glaucoma is a leading cause of irreversible blindness globally.
  • Current diagnosis and treatment are challenged by the lack of effective grading measures.
  • Accurate visual field assessment is crucial for glaucoma management.

Purpose of the Study:

  • To develop an artificial intelligence system for assessing glaucoma patients.
  • To propose a fine-grained grading deep learning system (FGGDL) for visual field loss.
  • To evaluate the system's ability to aid in glaucoma diagnosis and grading.

Main Methods:

  • Collected 16,356 visual fields (VFs) from Octopus perimeters and Humphrey Field Analyzer (HFA).
  • Developed FGGDL, a deep learning system, to evaluate VF loss and compared its performance to ophthalmologists.
  • Discussed the relationship between structural and functional damage for comprehensive glaucoma evaluation.

Main Results:

  • FGGDL achieved high accuracy (85-90%) and AUC (0.90-0.93) on HFA and Octopus data.
  • The system outperformed medical students and performed comparably to ophthalmic clinicians (p=0.614).
  • Cross-validation demonstrated improved diagnostic accuracy (p < 0.05).

Conclusions:

  • A deep learning system (FGGDL) effectively grades glaucoma visual fields with high accuracy.
  • The system's credible interface supports telemedicine and patient self-assessment.
  • This AI tool offers a solution for adequate glaucoma patient assessment and management.