AI-Driven Biomarker Discovery in Motor-Related Neurodegenerative Diseases.
This review surveys AI/ML approaches for discovering and validating molecular, imaging, and digital biomarkers related to motor dysfunction across PD, HD, ALS, and SCAs—highlighting candidates such as alpha-synuclein, tau, and neurofilament light chain and the promise of multimodal models while…
What the AI sees
This review surveys AI/ML approaches for discovering and validating molecular, imaging, and digital biomarkers related to motor dysfunction across PD, HD, ALS, and SCAs—highlighting candidates such as alpha-synuclein, tau, and neurofilament light chain and the promise of multimodal models while…
Research significance
By consolidating actionable biomarker candidates and AI methods, the paper supports improved patient stratification and outcome measures for PD therapeutic development, though translational impact will require stronger cross-center validation and clearer connections from biomarkers to targetable…
Source abstract
Parkinson's disease (PD), Huntington's disease (HD), amyotrophic lateral sclerosis (ALS), and spinocerebellar ataxias (SCAs) are examples of neurodegenerative disorders (NDDs) that share overlapping neuropathological processes and largely affect motor coordination. For early diagnosis, illness monitoring, and treatment targeting, it is essential to find trustworthy biomarkers that represent motor circuit dysfunction. The purpose of this study is to summarize the state of the art regarding molecular, neurochemical, and imaging biomarkers that are pertinent to motor impairment and to investigate the function of artificial intelligence (AI) in their identification and verification Methods: With an emphasis on biomarker discovery, validation, and AI/ML applications in PD, HD, ALS, and SCAs, a thorough literature search was carried out in the PubMed, Scopus, and Google Scholar databases for research published between 2015 and 2025. The motor-specific correlations of key molecular (α-synuclein, tau, neurofilament light chain, TDP-43, mutant huntingtin), neuroimaging, and digital biomarkers were carefully examined Results: AI-driven methods, such as deep learning and machine learning, have shown great promise in combining multimodal data from digital, fluid, and imaging sources. These techniques enhanced the detection of disease-specific biomarker signatures, especially those associated with deficiencies in motor coordination Discussion: Data heterogeneity, biomarker standardization, model interpretability, and limited cross-disease validation are still issues despite encouraging developments. Improving the clinical reliability of AI-based biomarker models requires filling in these gaps Conclusion: An effective foundation for deciphering intricate motor neurological pathways is provided by AI-assisted biomarker discovery. Transparent algorithms, multicenter data integration, and ethical frameworks should be given top priority in future research to guarantee clinical translation and better patient stratification.