Shoupa: An AI System for Early Diagnosis of Parkinson's Disease

TL;DR

This paper presents Shoupa, an AI-powered mobile system designed for early Parkinson's Disease detection by assessing motor and non-motor symptoms outside clinical settings, aiming to improve accessibility and early diagnosis.

Contribution

It introduces a novel integrated AI framework on mobile devices for early PD detection, combining multiple symptom evaluations in non-clinical environments.

Findings

Effective detection of PD symptoms using mobile AI system

Potential for early intervention and personalized treatment planning

Framework adaptable for other neurological disorder detection

Abstract

Parkinson's Disease (PD) is a progressive nervous system disorder that has affected more than 5.8 million people, especially the elderly. Due to the complexity of its symptoms and its similarity to other neurological disorders, early detection requires neurologists or PD specialists to be involved, which is not accessible to most old people. Therefore, we integrate smart mobile devices with AI technologies. In this paper, we introduce the framework of our developed PD early detection system which combines different tasks evaluating both motor and non-motor symptoms. With the developed model, we help users detect PD punctually in non-clinical settings and figure out their most severe symptoms. The results are expected to be further used for PD rehabilitation guidance and detection of other neurological disorders.