Detection of Neurological diseases Using Machine Learning

Abstract

The human pupil serves not only as a conduit for visual information but also as a delicate physiological indicator that mirrors the internal condition of the brain and the autonomic nervous system. The dynamics of the pupil particularly variations in size, the speed of constriction and dilation, and the latency of response are governed by intricate neural circuits that encompass both sympathetic and parasympathetic pathways. These reactions can be initiated by external stimuli (like light) as well as internal cognitive activities (including attention, memory, and emotional arousal). Recent developments in neuroscience have shown that irregularities in these dynamic pupil behaviors can act as early indicators of neurological and psychiatric conditions, such as Alzheimer’s disease, Parkinson’s disease, Autism Spectrum Disorder (ASD), Multiple Sclerosis, and Traumatic Brain Injury (TBI). A slow pupil light reflex or less pupil dilation during mental tasks has been linked to neurodegeneration and cognitive decline. As a result, non-invasive pupillometry may assist in the early diagnosis, tracking of disease progression, and evaluation of treatment effectiveness. At the same time, the domain of machine learning (ML) has transformed biomedical research by facilitating the detection of complex patterns within high-dimensional time-series data. Machine learning algorithms excel in modeling the intricate, nonlinear dynamics that define pupil behavior. By integrating machine learning techniques with pupillometry, we can develop automated, scalable systems that are able to detect early signs of neurological impairment through subtle changes in eye physiology. This study conducted to investigate the application of pupil dynamics as digital biomarkers through the recording and analysis of pupil reactions to light and cognitive stimuli. The research will concentrate on deriving significant features from pupil time-series data and developing machine learning models to differentiate between healthy individuals and those with neurological impairments. Additionally, it will assess the reliability and interpretability of these models in both clinical and real-world environments. By integrating knowledge from neuroscience, computer vision, and machine learning, this study aims to provide an innovative, affordable solution for early neurological screening ultimately facilitating prompt diagnosis and tailored treatment strategies.