MEYE: Web App for Translational and Real-Time Pupillometry

Area of Science:

• Neuroscience
• Computational Biology
• Biomedical Engineering

Background:

• Alterations in pupil dynamics are observed in neuropsychiatric conditions like autism.
• Pupillometry is a valuable noninvasive tool for assessing arousal and enabling longitudinal studies in both human and mouse models.
• Current pupillometry methods often require specialized equipment and technical expertise.

Purpose of the Study:

• To introduce a novel, user-friendly web application for online pupillometry.
• To simplify the application of pupillometry for non-specialist operators.
• To provide a sensitive and accessible tool for measuring pupil dynamics in research settings.

Main Methods:

• Development of a convolutional neural network (CNN) for real-time pupillometry.
• Implementation of the CNN within a web application accessible via a standard web browser.
• Testing the tool's sensitivity to locomotor-induced and stimulus-evoked pupillary changes in both mice and humans.

Main Results:

• The CNN-based web app enables online pupillometry in both mice and humans.
• The tool requires only a modern web browser, reducing deployment time and complexity.
• The system demonstrated sensitivity in detecting pupillary changes and comparable performance to commercial devices.

Conclusions:

• This web-based pupillometry tool significantly lowers the barrier to entry for researchers.
• The accessibility and ease of use facilitate broader application in studying neurological and psychiatric conditions.
• The technology offers a promising, simplified approach for noninvasive pupillary response measurement.