AI and Neurological Rare & Neuroimmune Diseases ── Parkinson's ── 2026-11-08
Parkinson's disease and parkinsonism: treatment, trials and AI
- Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disorder worldwide, affecting especially elderly. Early diagnosis of PD remains challenging due to the subtle onset of symptoms. Traditional motor assessment tools, such as the MDS-UPDRS, often lack objectivity and sensitivity. This study evaluates Machine Learning (ML) and Deep Learning (DL) techniques combined with wearable inertial measurement unit IMU sensors to detect gait abnormalities associated with Parkinson. Several shallow ML models and DL architectures were trained on preprocessed IMU gait data partitioned at the subject level. Data augmentation techniques were applied exclusively to the training dataset to address class imbalance without information leakage. The LSTM model trained on augmented temporal signals achieved the highest performance, reaching an accuracy of 93.79%, a recall of 94.84%, a balanced accuracy of 93.78%, and a Matthews correlation coefficient (MCC) of 0.876. The MLP and Random Forest with DWT features achieved an accuracy of 92.48%, a recall of 93.55%, and an MCC of 0.850. Model robustness was further confirmed on an independent cross-site cohort achieving, LSTM: 93.10% accuracy, 94.16% recall; MLP: 92.13% accuracy, 93.07% recall. Furthermore, Explainable AI revealed that model decisions were predominantly driven by wrist rotational dynamics, physiologically corroborating asymmetric arm-swing reduction.
Journal IF-equivalent: 13.5 (OpenAlex 2-year mean citedness, value as of 2026-11-07, retrieved 2026-11-08; not the official Clarivate IF)Reference: Molina-Coronel R, Moscoso-Esquivel A, Loayza F, Tapia-Rosero A, Pelaez E, Navarro-Cadavid A, et al. IMU-Based Gait Analysis for Detection of Parkinson Disease Using Machine and Deep Learning Models. Digit Gov: Res Pract. 2026 Nov 6 [Epub ahead of print]. doi:10.1145/3859276.Checked: Abstract only - Abstract
To address the challenges in mass spectrometry data processing, including poor detection of low-abundance metabolites, severe noise interference, and the reliance on labor-intensive parameter tuning in conventional peak detection methods, a deep learning-based peak detection algorithm termed MSPeakNet was developed. The model used a multi-scale convolutional module and a U-net encoder-decoder architecture integrated with an attention mechanism, enabling high-precision peak identification and noise reduction in complex biological matrices. MSPeakNet achieved precision and F1 scores comparable to CWT, with superior noise robustness, while maintaining competitive recall. The developed method was subsequently applied to the in vivo metabolite profiling of total triterpenoids from Eleutherococcus senticosus leaves (ESLT) in an MPTP-induced mouse model of Parkinson's disease (PD) using UPLC-Q-TOF/MS analysis. A total of 143 ESLT-related compounds, comprising 88 prototypes and 55 metabolites, were identified in plasma, urine, feces, and brain tissue, of which 16 prototypes were detected in brain tissue. Pharmacodynamic evaluation further confirmed that ESLT significantly ameliorated MPTP-induced motor dysfunction and dopaminergic neuronal loss. Comparative analysis further revealed distinct profiles of ESLT-related differential compounds between PD model and normal mice across multiple biological matrices; 14, 9, 6, and 4 differential metabolites were identified in feces, urine, plasma, and brain, respectively, suggesting that disease status may affect the biotransformation, distribution, and elimination of ESLT constituents. A robust and efficient deep learning-based approach for mass spectrometry peak detection was thereby established, and critical experimental evidence was provided for the discovery of anti-Parkinsonian active ingredients and quality markers from E. senticosus leaves.
Journal IF-equivalent: 7.3 (OpenAlex 2-year mean citedness, value as of 2026-11-07, retrieved 2026-11-08; not the official Clarivate IF)Reference: Wen T, Guan W, Xu S, Yang X, Li Y, Wang H, et al. A novel deep learning-based peak detection technique for in vivo metabolite analysis of triterpenoids from Eleutherococcus senticosus leaves in Parkinson's disease mice. Talanta. 2027 Apr;314:130904. doi:10.1016/j.talanta.2026.130904.Checked: Abstract only