Advancements in Diagnostic Strategy of Neurological and Neuropsychiatric Disorders: From Conventional Methods to Point-of-Care Approaches

Abstract

Neurological and neuropsychiatric disorders such as Alzheimer’s, Parkinson’s, Epilepsy, Stroke, etc. are commonly being diagnosed in the human population nowadays due to changing lifestyle and other factors.  Every year, for nearly 10 million deaths and 10 million DALYs have been reported of Neurological disorders. This study overviews the advancements in conventional methods using machine learning or deep learning techniques and the development of point-of-care detection techniques to diagnose these diseases. Some of the non-invasive techniques such as EEG, MRI, Magnetic encephalography are expensive, time consuming and require sophisticated instrumentation facilities. Thus, to overcome these challenges point-of-care detection is an alternative for diagnosis which employs lateral Flow Assays, biosensors that are simple to use, cost effective, less time consuming, can be performed at low resource settings, and are sensitive, specific and accurate. Point-of-care detection involves detection of specific biomarkers in the body fluids of patients for early detection of the disease.