Exploring Huntington's Disease Diagnosis via Artificial Intelligence Models: A Comprehensive Review

Area of Science:

• Neuroscience
• Medical Informatics
• Computational Biology

Background:

• Huntington's Disease (HD) is a severe neurodegenerative disorder with motor, cognitive, and psychiatric symptoms.
• Early and accurate diagnosis is critical for managing HD and improving patient outcomes.
• Existing diagnostic methods can be invasive or time-consuming.

Purpose of the Study:

• To review the application of Artificial Intelligence (AI) algorithms in diagnosing Huntington's Disease.
• To identify trends, methodologies, and challenges in AI-driven HD diagnosis.
• To highlight the potential of machine learning (ML) and deep learning (DL) for automated HD detection.

Main Methods:

• Systematic review of existing literature on AI in HD diagnosis.
• Analysis of studies utilizing clinical, genetic, and neuroimaging data.
• Evaluation of ML and DL approaches for diagnostic accuracy.

Main Results:

• AI, particularly ML and DL, demonstrates significant potential for automating Huntington's Disease diagnosis.
• Analysis of multimodal data (clinical, genetic, neuroimaging) enhances diagnostic accuracy.
• Identified key trends and methodologies in the application of AI for HD.

Conclusions:

• AI-powered algorithms offer a promising avenue for the early and accurate diagnosis of Huntington's Disease.
• Further research is needed to address limitations and ethical considerations for widespread clinical adoption.
• This review serves as a resource for professionals at the intersection of AI and neurodegenerative disease research.