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

Ganesh, Sowmiyalakshmi; Chithambaram, Thillai; Krishnan, Nadesh Ramu; Vincent, Durai Raj; Kaliappan, Jayakumar; Srinivasan, Kathiravan

doi:10.3390/diagnostics13233592

Cited by 7 publications

(1 citation statement)

References 121 publications

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“…In order to construct a complete research background, we referred to recently published reviews of computational methods in neurodegeneration by Tăuţan et al (2021), Bhachawat et al (2023) and Ganesh et al (2023).…”

Section: Research Backgroundmentioning

confidence: 99%

The performance of domain-based feature extraction on EEG, ECG, and fNIRS for Huntington’s disease diagnosis via shallow machine learning

Maddury

2024

Front. Signal Process.

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Introduction: The early detection of Huntington’s disease (HD) can substantially improve patient quality of life. Current HD diagnosis methods include complex biomarkers such as clinical and imaging factors; however, these methods have high time and resource demands.Methods: Quantitative biomedical signaling has the potential for exposing abnormalities in HD patients. In this project, we attempted to explore biomedical signaling for HD diagnosis in high detail. We used a dataset collected at a clinic with 27 HD-positive patients, 36 controls, and 6 unknowns with EEG, ECG, and fNIRS. We first preprocessed the data and then presented a comprehensive feature extraction procedure for statistical, Hijorth, slope, wavelet, and power spectral features. We then applied several shallow machine learning techniques to classify HD-positives from controls.Results: We found the highest accuracy was achieved by the extremely randomized trees algorithm, with an ROC AUC of 0.963 and accuracy of 91.353%.Discussion: The results provide improved performance over competing methodologies and also show promise for biomedical signals for early prognosis of HD.

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Section: Research Backgroundmentioning

confidence: 99%

The performance of domain-based feature extraction on EEG, ECG, and fNIRS for Huntington’s disease diagnosis via shallow machine learning

Maddury

2024

Front. Signal Process.

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Huntington’s Disorder Prediction Using Hybrid Grid Search-SVM Model

Pandey,

Srivastava

2024

Studies in Autonomic, Data-Driven and Industrial Computing

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Therapeutic approaches targeting aging and cellular senescence in Huntington's disease

Bhat,

Moglad,

Afzal

et al. 2024

CNS Neurosci Ther

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Huntington's disease (HD) is a devastating neurodegenerative disease that is manifested by a gradual loss of physical, cognitive, and mental abilities. As the disease advances, age has a major impact on the pathogenic signature of mutant huntingtin (mHTT) protein aggregation. This review aims to explore the intricate relationship between aging, mHTT toxicity, and cellular senescence in HD. Scientific data on the interplay between aging, mHTT, and cellular senescence in HD were collected from several academic databases, including PubMed, Google Scholar, Google, and ScienceDirect. The search terms employed were “AGING,” “HUNTINGTON'S DISEASE,” “MUTANT HUNTINGTIN,” and “CELLULAR SENESCENCE.” Additionally, to gather information on the molecular mechanisms and potential therapeutic targets, the search was extended to include relevant terms such as “DNA DAMAGE,” “OXIDATIVE STRESS,” and “AUTOPHAGY.” According to research, aging leads to worsening HD pathophysiology through some processes. As a result of the mHTT accumulation, cellular senescence is promoted, which causes DNA damage, oxidative stress, decreased autophagy, and increased inflammatory responses. Pro‐inflammatory cytokines and other substances are released by senescent cells, which may worsen the neuronal damage and the course of the disease. It has been shown that treatments directed at these pathways reduce some of the HD symptoms and enhance longevity in experimental animals, pointing to a new possibility of treating the condition. Through their amplification of the harmful effects of mHTT, aging and cellular senescence play crucial roles in the development of HD. Comprehending these interplays creates novel opportunities for therapeutic measures targeted at alleviating cellular aging and enhancing HD patients’ quality of life.

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

Cited by 7 publications

References 121 publications

The performance of domain-based feature extraction on EEG, ECG, and fNIRS for Huntington’s disease diagnosis via shallow machine learning

The performance of domain-based feature extraction on EEG, ECG, and fNIRS for Huntington’s disease diagnosis via shallow machine learning

Huntington’s Disorder Prediction Using Hybrid Grid Search-SVM Model

Therapeutic approaches targeting aging and cellular senescence in Huntington's disease

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