Use of Neuroimaging and Artificial Intelligence to Optimize the Diagnosis and Treatment of Hereditary Cerebellar Ataxia

• Grant number: 25/03793-9
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: August 01, 2025
• End date: July 31, 2026
• Field of knowledge: Health Sciences - Medicine - Medical Radiology
• Principal Investigator: Thiago Junqueira Ribeiro de Rezende
• Grantee: Fernanda Schmitz de Miranda Gomes
• Host Institution: Faculdade de Ciências Médicas (FCM). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

Hereditary cerebellar ataxias (HCA) are a group of rare diseases characterized by cerebellar ataxia and nervous system degeneration. Diagnosis is challenging due to overlapping clinical symptoms, resulting in diagnostic delays and difficulties in clinical trials, which rely on low-sensitivity clinical scales. In this context, neuroimaging techniques have been successfully applied to characterize and clinically distinguish neurodegenerative disease. Furthermore, with advances in computing, artificial intelligence (AI) techniques have also been successfully used for data classification and clustering. Therefore, this project aims to integrate neuroimaging and AI as tools to optimize HCA diagnosis by developing an algorithm to assist in indicating genetic tests for the main HCAs (SCA1, SCA2, SCA3, SCA6, and FRDA), reducing costs and speeding up diagnosis. This study seeks to make a significant contribution to the clinical management of HCAs, promoting early interventions and improving patients' quality of life.