Smartphone Pupillometry for Diagnosis of Neurological Disease Using Machine Learning

The pupillary light reflex (PLR) is a non-invasive biomarker associated with brain health. It is altered in diseases and conditions such as traumatic brain injury and dementia. Most clinicians are forced to make a PLR assessment subjectively using a penlight and the naked eye - a technique known as manual pupillometry. While studies have shown that this is unreliable, it is the only method available to the majority of first responders and clinicians in the USA and throughout the world. Quantitative pupillometry was developed in response to the inaccuracy of manual pupillometry and is a highly accurate method of assessing the PLR. Unfortunately, existing quantitative pupillometry devices are fragile, cumbersome, and expensive, making them impractical for most hospitals in the USA, let alone the rest of the world. To address this need for a more affordable and accessible method of quantitative pupillometry, we have developed PupilScreen – a standalone smartphone application for reliable detection and quantification of the PLR using machine learning.