A mobile vision testing application based on dynamic distance determination from the human corneal limbus.

Area of Science:

• Ophthalmology
• Biomedical Engineering
• Computer Vision

Background:

• Accurate visual acuity assessment is crucial for diagnosing and monitoring eye conditions.
• Current methods for visual acuity testing can be time-consuming and require specialized equipment.
• There is a need for accessible, at-home solutions for regular eye health monitoring.

Purpose of the Study:

• To develop and validate a mobile application for precise measurement of the distance between an optical sensor and the human corneal limbus.
• To enable accurate visual acuity assessment using a smartphone-based system.
• To facilitate remote monitoring of chronic retinal diseases.

Main Methods:

• Utilized digital image processing and randomized circle detection algorithms to identify the cornea.
• Implemented a reference scaling measurement for accurate sensor-to-user distance calculation.
• Validated the application using static images from diverse ethnic groups and live image streams.

Main Results:

• Achieved an average absolute corneal radius error of 6.36% on static images.
• Demonstrated an average absolute distance error of less than 1% on live image streams.
• Successfully scaled letter sizes for a Snellen Chart-based visual acuity assessment.

Conclusions:

• The mobile application provides an accurate and reliable method for measuring corneal distance.
• This technology offers a convenient and accessible tool for visual acuity assessment and remote eye health monitoring.
• The system has the potential to improve patient management for chronic retinal diseases.