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Related Experiment Video

Updated: Sep 13, 2025

Trabecular Meshwork Response to Pressure Elevation in the Living Human Eye

Trabecular Meshwork Response to Pressure Elevation in the Living Human Eye

Published on: June 20, 2015

Multimodal-Based Non-Contact High Intraocular Pressure Detection Method.

Zibo Lan¹, Ying Hu², Shuang Yang¹

¹School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China.

Sensors (Basel, Switzerland)

|July 30, 2025

Summary

This summary is machine-generated.

This study introduces a novel deep learning method to detect elevated intraocular pressure (IOP) using eye images and corneal biomechanics. The approach achieves high accuracy for glaucoma screening, offering a non-contact alternative.

Keywords:

Scheimpflug imaging deep learning multi-modal model non-contact high IOP detection

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Last Updated: Sep 13, 2025

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Translaminar Autonomous System Model for the Modulation of Intraocular and Intracranial Pressure in Human Donor Posterior Segments

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Published on: April 24, 2020

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Glaucoma, a leading cause of irreversible blindness, necessitates accurate intraocular pressure (IOP) monitoring.
Traditional IOP measurements are susceptible to corneal biomechanical variations, impacting diagnostic reliability.

Purpose of the Study:

To develop a non-contact, deep learning-based method for detecting elevated IOP by integrating Scheimpflug images and corneal biomechanical data.
To improve the accuracy of high IOP prediction and glaucoma screening.

Main Methods:

A multi-modal deep learning framework utilizing CycleGAN for data augmentation, Swin Transformer for visual feature extraction, and Kolmogorov-Arnold Network (KAN) for data fusion.
Integration of Scheimpflug imaging with clinical parameters for comprehensive data analysis.
Dataset augmentation to address data scarcity and class imbalance.

Main Results:

The proposed model achieved a diagnostic accuracy of 0.91 on a real-world dataset, surpassing traditional methods.
Grad-CAM visualizations highlighted key anatomical features, including corneal thickness and anterior chamber depth, related to IOP fluctuations.

Conclusions:

The study demonstrates the efficacy of a deep learning approach combining ocular imaging and biomechanics for accurate IOP assessment.
Findings emphasize the significance of corneal structure in IOP regulation and suggest potential for non-invasive, biomechanics-informed IOP screening.