Differential diagnosis of neurodegenerative diseases using an innovative deep learning approach

Abstract

Background: Neurodegenerative diseases lead to memory disorders and cognitive impairments with major impacts on day-to-day function. Early detection of patients is essential for an efficient care approach, and differential diagnosis is essential for determining prognosis and tailoring management. Most research studies compare patients by pairwise comparison methods, but this does not correspond to daily clinical practice, in which clinicians need to distinguish many different possible types of dementia concomitantly. It has been shown that magnetic resonance imaging (MRI) can mark with good accuracy the different types of dementia, such as: Alzheimer's disease (AD), Frontotemporal dementia (FTD), Parkinson's disease (PD), dementia with Lewy bodies (DLB). Recently, Deep Learning approaches have started being used although differential diagnosis is still almost ignored. The aim of this study was to develop a Convolution Neural Network (CNN) to classify probable AD, FTD, DLB, PD and healthy elderly control (CN) subjects starting from Diffusion Tensor Imaging (DTI).

A Comprehensive Study on Personalized Mindfulness Interventions for Improved Quality of Life in Parkinson’s Disease: A Case Study

“…The paper aims to establish a robust methodology capable of distinguishing Parkinson's disease from other related conditions, such as Alzheimer's disease and Frontotemporal dementia, through meticulous analysis of brain scans. [12] M. M. Salman,et.al in a study titled "Advances in applying Computer-Aided Drug Design for Neurodegenerative Diseases" discussed Neurodegenerative diseases present a profound global health challenge marked by widespread impact and the absence of definitive cures. Tackling this exigent need for effective treatments necessitates innovative methodologies to expedite drug discovery, among which computer-aided drug design (CADD) stands out.…”