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Glaucoma: Overview

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...

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Updated: May 10, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Comparing No-Code Platforms and Deep Learning Models for Glaucoma Detection From Fundus Images.

Mauro Gobira¹, Luis F Nakayama², Caio Vinicius S Regatieri²

¹Ophthalmology, Vision Institute - Instituto Paulista de Estudos e Pesquisas em Oftalmologia (IPEPO), São Paulo, BRA.

|April 24, 2025

Summary

This summary is machine-generated.

No-code AI platforms like Create ML and Teachable Machine show strong performance in classifying glaucoma from fundus images, though ResNet200d remains superior. These tools democratize AI in healthcare.

Keywords:

acrima dataset create ml deep learning fundus images glaucoma detection resnet200d teachable machine

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Glaucoma diagnosis relies on interpreting optic nerve fundus images.
Traditional deep learning models require significant expertise and computational resources.
No-code machine learning platforms offer accessible AI development for medical applications.

Purpose of the Study:

To compare the diagnostic performance of Google's Teachable Machine (TM) and Apple's Create ML against a traditional ResNet200d model.
To evaluate the efficacy of no-code platforms in classifying glaucoma from optic nerve fundus images using the ACRIMA dataset.

Main Methods:

A comparative analysis of 705 labeled fundus images from the ACRIMA dataset.
Training and validation of Teachable Machine, Create ML, and ResNet200d models.
Assessment of performance metrics including sensitivity, specificity, and F1 score.

Main Results:

ResNet200d achieved the highest accuracy (99.29%), sensitivity (98.57%), and specificity (100%).
Create ML demonstrated high specificity (98.48%) and an F1 score of 95.83%.
Teachable Machine showed higher sensitivity (95.71%) with an F1 score of 95.04%.

Conclusions:

No-code platforms, Create ML and TM, exhibit robust capabilities for glaucoma detection in fundus images.
While ResNet200d offers superior diagnostic accuracy, no-code tools democratize AI in healthcare, particularly in resource-limited settings.
Further validation with diverse datasets is recommended to confirm the potential of these accessible AI tools.