AI Article Synopsis
- Huntington's Disease (HD) is a serious neurodegenerative disorder that affects movement, cognition, and mental health, making early diagnosis essential for effective treatment.
- This review examines how AI algorithms, particularly machine learning (ML) and deep learning (DL), can improve the diagnosis of HD by analyzing clinical, genetic, and neuroimaging data.
- It also addresses challenges, limitations, and ethical concerns surrounding these technologies while offering guidance for future research to enhance early detection and care for patients with Huntington's Disease.
Article 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.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10706174 | PMC |
| http://dx.doi.org/10.3390/diagnostics13233592 | DOI Listing |
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