AI and Neurological Rare & Neuroimmune Diseases ── Parkinson's ── 2026-10-26
Parkinson's disease and parkinsonism: treatment, trials and AI
- Abstract
BACKGROUND: Parkinson's disease (PD) is characterized by progressive motor dysfunction, in which gait disturbances often emerge early yet remain underutilized in diagnostic frameworks. Advances in wearable sensor technology enable high-resolution quantification of gait dynamics, but the clinical translation of such high-dimensional data is limited by redundancy and model overfitting. OBJECTIVE: To determine the exploratory diagnostic efficacy of synthesizing wearable sensor-derived gait parameters, cognitive scores, and blood biomarkers utilizing a dual-stage dimensionality reduction framework coupled with logistic regression machine learning for the precise identification of PD. METHODS: In this cross-sectional study, demographic characteristics, blood biomarkers, cognitive assessments, and wearable sensor-derived gait parameters were collected from patients with PD and matched controls. Following initial group comparisons, least absolute shrinkage and selection operator (LASSO) regression was applied to identify candidate predictors from multimodal variables. Principal component analysis (PCA) was subsequently performed to reduce dimensionality and extract latent structures. Logistic regression models were constructed using both the original LASSO-selected variables and PCA-derived components. Model performance was assessed using 10-fold cross-validation, with discrimination quantified by the area under the receiver operating characteristic curve (AUC) and accuracy. RESULTS: Baseline characteristics were comparable between groups except for age. LASSO identified 22 potential predictors spanning demographic, biochemical, cognitive, and gait domains. PCA further condensed these variables into six principal components, explaining 62.983% of the total variance. Using a rigorous nested cross-validation framework on the primary cohort, the PCA-based model achieved an internal mean AUC of 0.949 with an accuracy of 0.827. Furthermore, when evaluated on a completely independent external cohort ( N = 36) to ensure generalizability, the finalized PCA-based model demonstrated robust diagnostic performance, yielding an AUC of 0.891 and an accuracy of 0.750. Both approaches showed robust discriminative capacity, with dimensionality reduction enhancing overall efficiency and stability. CONCLUSION: Integrating wearable gait signatures with LASSO-PCA dimensionality reduction and logistic regression yields reliable, interpretable PD classification, with PCA-condensed features demonstrating enhanced diagnostic efficiency. These findings warrant external validation in multicentre, longitudinal cohorts to advance non-invasive, sensor-based PD screening.
Journal IF-equivalent: 2.6 (OpenAlex 2-year mean citedness, value as of 2026-10-08, retrieved 2026-10-09; not the official Clarivate IF)Reference: Hong G, Wen X, Gao J, Lin J, Zumulaiti S, Yang Y, et al. Wearable sensor–derived gait signatures for Parkinson's disease: an exploratory diagnostic study integrating dimensionality reduction and machine learning. Front Med (Lausanne). 2026 Oct 8;13:1939598. doi:10.3389/fmed.2026.1939598; PMCID: PMC13686899.Checked: Full text checked - Abstract
BACKGROUND: Vocal impairment constitutes a prominent prodromal biomarker for Parkinson's disease (PD). However, existing deep learning solutions often rely on computationally intensive architectures or biased validation protocols. This study proposes the lightweight spectrotemporal hybrid network (LST-Net), a resource-efficient framework for robust PD screening. METHODS: The model employs a dual-stream architecture that concurrently processes raw one-dimensional waveforms and two-dimensional Mel-spectrograms to capture complementary cues, specifically transient temporal jitters and spectral energy patterns. The proposed architecture was evaluated on the Italian Parkinson's Voice and Speech dataset using a strict subject-independent 5-fold cross-validation protocol to prevent data leakage. RESULTS: The proposed LST-Net achieved a robust segment-level accuracy of 96.69%. By implementing a subject-level majority voting strategy, mimicking clinical diagnosis where patients are assessed over time, the model successfully classified all subjects in the held-out test sets (100% subject-level accuracy). Quantitative Gradient-weighted Class Activation Mapping analysis confirmed the model's focus on clinically relevant low-frequency tremors (mean energy: 61.56% ±27.90%). CONCLUSIONS: This framework demonstrates that explicit spectrotemporal fusion yields superior diagnostic performance without heavy computational burdens, offering a verifiable and scalable solution for real-time deployment on edge devices.
Journal IF-equivalent: 3.0 (OpenAlex 2-year mean citedness, value as of 2026-10-24, retrieved 2026-10-26; not the official Clarivate IF)Reference: [No authors listed]. Subject-independent Parkinson's Disease Detection through A Lightweight Spectrotemporal Hybrid Network. J Med Signals Sens. [Epub ahead of print]. doi:10.4103/jmss.jmss_137_25. PMID: 42924316.Checked: Abstract only