AI and Neurological Rare & Neuroimmune Diseases ── 2026-11-07
New papers on neurological rare and neuroimmune diseases and AI, collected from journal feeds, PubMed (searched across all journals) and OpenAlex (a public index used instead of Google Scholar), judged by Jev (an AI that makes language judgements): original research, AI central, which of 9 disease groups, treatment or trials, and then ranked by importance up to a daily limit. Journals are not filtered by impact factor; each paper shows its journal's IF-equivalent (OpenAlex two-year mean citedness) and the date of that value. Each entry shows the full original abstract.
Read by disease: Basic research（0） · ALS（0） · Parkinson's（2） · Hereditary neurodegeneration（0） · Neuromuscular（0） · Multiple sclerosis（1） · NMOSD/MOGAD（0） · Myasthenia gravis（0） · Autoimmune encephalitis/CIDP/GBS（0）
Neurodegeneration and neuroimmunology research and AI
No new qualifying paper for this issue.
ALS: treatment, trials and AI
No new qualifying paper for this issue.
Parkinson's disease and parkinsonism: treatment, trials and AI
- Abstract
This repository contains the complete reproducibility package for the systematic review titled "Validation, Reproducibility, and Generalization of Machine Learning Models with Wearable Sensors for Parkinson's Disease Movement Analysis: A Systematic Review". Following the PRISMA 2020 guidelines, this review screened 132 records from four databases (Scopus, IEEE Xplore, ACM Digital Library, and ScienceDirect) and included 11 primary studies in the qualitative synthesis. This package provides full transparency and enables the replication of our methodology. It includes:- Table S1: Complete search strings for each database.- Table S2: Detailed eligibility criteria.- Table S3: Quality assessment scores (Downs and Black adapted criteria) for all 54 full-text screened studies.- Table S4: Detailed data extraction table for the included studies.- Table S5: List of the 11 secondary studies removed from the main synthesis.- Table S6: Complete list of excluded studies with reasons.- PRISMA 2020 Checklist and PRISMA Flow Diagram.- Analysis scripts (Python/R) used for data visualization and descriptive statistics. This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Journal IF: unknown (could not be matched)Reference: [No authors listed]. Reproducibility Package for: Validation, Reproducibility, and Generalization of Machine Learning Models with Wearable Sensors for Parkinson's Disease Movement Analysis: A Systematic Review. Figshare. [Epub ahead of print]. doi:10.6084/m9.figshare.34918647.Checked: Abstract only - Abstract
Early detection of Parkinson's Disease (PD) remains challenging due to vocal signal complexity and severe data imbalance, which necessitates that machine learning (ML) models deliver higher classification accuracy. This study aims to break the systematic, inverse trade-off between sensitivity and specificity in voice-based PD classification. We present the Intelligent Fuzzy Inference and Machine Learning (IFI-ML) framework, an uncertainty-aware architecture utilizing entropy-based partitioning, hybrid classifiers, and a Sugeno Type-2 Fuzzy Inference System (FIS). While traditional frameworks apply global resampling techniques such as the Synthetic Minority Over-sampling Technique (SMOTE) or Random Over-Sampling (ROS) across entire datasets, the proposed IFI-ML framework utilizes information gain-driven clustering to segment samples into sensitive (uncertain boundary) and non-sensitive partitions. The framework optimizes conventional ML classifiers on the stable, non-sensitive clusters, while routing critical boundary samples to the Type-2 FIS to handle local class overlap without distorting the overall data distribution. We evaluated our framework on two public, imbalanced datasets from the University of California, Irvine (UCI) Machine Learning repository: Parkinson's voice Dataset 1 ( D 1 ) and Dataset 2 ( D 2 ). The proposed framework achieves an accuracy of 97% on D 1 and 92.17% on D 2 , while simultaneously raising specificity to 94-95% on D 1 and 75-88% on D 2 . The Matthews Correlation Coefficient (MCC) improves to 0.93 and 0.77, respectively. Furthermore, IFI-ML reduces false-positive and false-negative rates to 3-7% on D 1 across all classifiers, and limits false-positive rates to 11-12% for the K-Nearest Neighbors (KNN) model on D 2 . Under a rigorous 10-fold cross-validation (10 CV) scheme, the framework consistently outperforms 15 baseline machine learning models. These benchmark results show the clinical reliability and robustness of the proposed approach, establishing a state-of-the-art benchmark for non-invasive vocal biomarkers.
Journal IF-equivalent: 6.0 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: S. V, G. M. IFI-ML: a novel framework combining Fuzzy Inference and Machine Learning for Parkinson's disease classification. Front Artif Intell. 2026 Oct 21;9:1949960. doi:10.3389/frai.2026.1949960.Checked: Abstract only
See all 2 Parkinson's papers →
Hereditary neurodegenerative diseases: treatment, trials and AI
No new qualifying paper for this issue.
Neuromuscular diseases (muscular dystrophy, SMA): treatment, trials and AI
No new qualifying paper for this issue.
Multiple sclerosis: treatment, trials and AI
- Abstract
BACKGROUND: Accurate magnetic resonance imaging (MRI)-based segmentation of the optic chiasm is essential for the quantitative assessment of visual pathway damage in multiple sclerosis (MS). However, accurate segmentation remains challenging because of the structure's small size, complex anatomy, and marked foreground-background imbalance. Existing deep learning approaches often rely on single-view imaging and may not fully exploit complementary anatomical information across MRI planes. To address these limitations, we developed and evaluated a novel multi-view two-dimensional U-Net (2D U-Net) framework that jointly leverages axial and coronal MRI information within a unified segmentation architecture for automated optic chiasm segmentation. MATERIALS AND METHODS: In this secondary analysis, a novel multi-view 2D U-Net framework was developed for optic chiasm segmentation by integrating axial and coronal MRI images within a unified three-class segmentation architecture. Models were optimized using a Tversky loss function and evaluated using five-fold patient-wise cross-validation. Segmentation performance was assessed quantitatively using multiple metrics, with the Dice similarity coefficient (DSC), intersection over union (IoU), and balanced accuracy serving as the primary evaluation metrics, and qualitatively through visual comparison with expert annotations. RESULTS: A total of 58 patients with relapsing-remitting MS were included in the analysis. The proposed multi-view model demonstrated stable convergence and achieved the highest segmentation performance among all evaluated models, with a DSC of 91.28%, IoU of 84.05%, and balanced accuracy of 94.43%. Compared with the best-performing single-view model, the multi-view approach improved DSC and IoU by approximately 13 and 19 percentage points, respectively. Qualitative assessment showed closer agreement between multi-view segmentations and expert annotations. CONCLUSION: The proposed multi-view, multi-class 2D U-Net framework outperformed conventional single-view models for optic chiasm segmentation. These findings demonstrate that integrating complementary anatomical information from axial and coronal MRI planes can substantially improve segmentation performance. The results indicate the potential of multi-view learning for the automated segmentation of small and anatomically complex structures.
Journal IF-equivalent: 2.6 (OpenAlex 2-year mean citedness, value as of 2026-11-06, retrieved 2026-11-07; not the official Clarivate IF)Reference: Kolahdouzan F, Sajadieh SM, Kafieh R, Adibi I, Ramezani N, Farnia F. Automated optic chiasm segmentation in magnetic resonance imaging of multiple sclerosis patients using a multi-view, multi-class U-Net with limited annotated data. Journal of Research in Medical Sciences. 2026 Jul;31(1). doi:10.4103/jrms.jrms_151_26.Checked: Abstract only
See all 1 Multiple sclerosis papers →
NMOSD and MOGAD: treatment, trials and AI
No new qualifying paper for this issue.
Myasthenia gravis: treatment, trials and AI
No new qualifying paper for this issue.
Autoimmune encephalitis, CIDP and Guillain-Barre syndrome: treatment, trials and AI
No new qualifying paper for this issue.