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Updated: Jun 27, 2026
High Content Screening in Neurodegenerative Diseases
Published on: January 6, 2012
Exploring Huntington's Disease Diagnosis via Artificial Intelligence Models: A Comprehensive Review
Sowmiyalakshmi Ganesh1, Thillai Chithambaram1, Nadesh Ramu Krishnan2
1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India.
Insights
Artificial Intelligence (AI) shows promise for diagnosing Huntington's Disease (HD). Machine learning and deep learning algorithms can analyze diverse data to improve early detection and patient management.
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.
Abstract:
Huntington's Disease (HD) is a devastating neurodegenerative disorder characterized by progressive motor dysfunction, cognitive impairment, and psychiatric symptoms. The early and accurate diagnosis of HD is crucial for effective intervention and patient care. This comprehensive review provides a comprehensive overview of the utilization of Artificial Intelligence (AI) powered algorithms in the diagnosis of HD. This review systematically analyses the existing literature to identify key trends, methodologies, and challenges in this emerging field. It also highlights the potential of ML and DL approaches in automating HD diagnosis through the analysis of clinical, genetic, and neuroimaging data. This review also discusses the limitations and ethical considerations associated with these models and suggests future research directions aimed at improving the early detection and management of Huntington's disease. It also serves as a valuable resource for researchers, clinicians, and healthcare professionals interested in the intersection of machine learning and neurodegenerative disease diagnosis.
