MEYE: Web-app for translational and real-time pupillometry

Abstract

Pupil dynamics alterations have been found in patients affected by a variety of neuropsychiatric conditions, including autism. Studies in mouse models have used pupillometry for phenotypic assessment and as a proxy for arousal. Both in mice and humans, pupillometry is non-invasive and allows for longitudinal experiments supporting temporal specificity, however, its measure requires dedicated setups. Here, we introduce a Convolutional Neural Network that performs online pupillometry in both mice and humans in a web app format. This solution dramatically simplifies the usage of the tool for the non-specialist and non-technical operators. Because a modern web browser is the only software requirement, this choice is of great interest given its easy deployment and set-up time reduction. The tested model performances indicate that the tool is sensitive enough to detect both locomotor-induced and stimulus-evoked pupillary changes, and its output is comparable with state-of-the-art commercial devices.

Significance Statement

Alteration of pupil dynamics is an important biomarker that can be measured non-invasively and across different species. Even though pupil size is driven primarily by light, it can also monitor arousal states and cognitive processes. Here we show an open-source web app that, through Deep Learning, can perform real-time pupil size measurements in both humans and mice, with accuracy similar to commercial-grade eye trackers. The tool requires no installation and pupil images can be captured using infrared webcams, opening the possibility to perform pupillometry widely, cost-effectively, and in a high-throughput manner.