AI and Neurological Rare & Neuroimmune Diseases ── 2026-10-16
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（2） · NMOSD/MOGAD（0） · Myasthenia gravis（1） · 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
Background: Multiple system atrophy (MSA) is frequently misdiagnosed as Parkinson's disease (PD) or progressive supranuclear palsy (PSP), posing significant diagnostic challenges.
Objective: The aim of the study was to systematically evaluate the efficacy of machine learning (ML) in differentiating MSA from other neurodegenerative disorders.
Methods: We conducted a systematic search in PubMed, Embase, Cochrane Library, and Web of Science (up to July 5, 2025), including ML‐based MSA diagnostic studies. QUADAS‐2 assessed bias risk, and a bivariate mixed‐effects model synthesized diagnostic metrics.
Results: A total of 51 studies (comprising 3595 MSA patients and 18,189 total cases) were included in the systematic review. Of these, 38 studies contributed to the quantitative synthesis, whereas the remaining 13 studies were summarized qualitatively. In the primary analysis comparing MSA with PD, the pooled sensitivity was 0.86 (95% confidence interval [CI]: 0.81–0.90), specificity 0.87 (95% CI: 0.83–0.90), and the area under the summary receiver operating characteristic (SROC) curve (AUC) 0.93 (95% CI: 0.90–0.95). Overall, pooled sensitivity was 0.85 (95% CI: 0.82–0.88), specificity 0.92 (95% CI: 0.89–0.94), and AUC 0.94 (95% CI: 0.92–0.96). Positron emission tomography (PET)‐based models achieved optimal performance (sensitivity: 0.90, specificity: 0.96, AUC: 0.97), whereas deep learning models outperformed others (sensitivity: 0.93, specificity: 0.98, AUC: 0.99).
Conclusion: ML models integrating PET/magnetic resonance imaging (MRI) data demonstrate high diagnostic accuracy for MSA. Future work should prioritize prospective multicenter validation and standardized external testing to facilitate clinical implementation.
Journal IF-equivalent: 2.0 (OpenAlex 2-year mean citedness, value as of 2026-10-15, retrieved 2026-10-16; not the official Clarivate IF)Reference: Wang G, Nie C, Liu M. Accuracy of Machine Learning in Differential Diagnosis of Multiple System Atrophy: A Systematic Review and Meta‐Analysis. Movement Disord Clin Pract. 2026 Oct 14 [Epub ahead of print]. doi:10.1002/mdc3.70857.Checked: Abstract only - Abstract
Background: Parkinson’s disease (PD) and Progressive Supranuclear Palsy (PSP) are neurodegenerative disorder which have Parkinsonism as core clinical feature. PD and PSP are often misdiagnosed in early phases by conventional magnetic resonance imaging (MRI). Hence the objective of the study is to evaluate accuracy of MRI based machine learning (ML) models for differentiating PD and PSP.
Methods: This is a retrospective study including total of 96 subjects (32 PD, 32 PSP and 32 healthy controls). The basal nuclei region such as substantia nigra (SN), caudate nucleus (CN), putamen, globus pallidus (GP), red nucleus (RN), and subthalamic nucleus (STN) were manually segmented in T2 weighted fluid attenuation inversion recovery (T2W-FLAIR) images and radiomics features (RF) were extracted. The top 20 RF were used to train and test ML models. Performance measures of ML models for differentiating PD, PSP and HC were evaluated.
Results: For differentiating PD from PSP, SN consistently showed highest performance achieving accuracy, F1score and AUC values of 0.95–1.00 across all the tested ML models. RN and STN also demonstrated near perfect classification with several ML models with accuracy and AUC values of 1.00. CN, putamen and GP showed moderate performance (accuracy 38% - 85%). Among classifiers, AdaBoost provided robust performance achieving AUC up to 0.89 and MCC up to 0.72 in test data sets.
Conclusion: MRI based radiomics models from SN, STN and RN provide the most accurate classification of PD and PSP. It can also help in diagnosis of PD and PSP which are undifferentiable in early stages of disease.
Journal IF-equivalent: 1.2 (OpenAlex 2-year mean citedness, value as of 2026-10-15, retrieved 2026-10-16; not the official Clarivate IF)Reference: Khruomo VV, - P, Kadavigere R, Nayak K, Sampathila N, Pendem S, et al. MRI-based machine learning models for differentiating Parkinson’s disease and Progressive Supranuclear Palsy. F1000Res. 2026 Oct 13;15:1709. doi:10.12688/f1000research.190609.1.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: The use of generative artificial intelligence to create synthetic data is an emerging strategy to enhance real-world evidence.
Objective: To assess the validity of artificial intelligence-generated synthetic data in replicating data collected in the Italian Multiple Sclerosis and Related Disorders Register for the evaluation of progression independent of relapse activity risk under early intensive treatment and escalation treatment strategies.
Methods: Artificial intelligence generative models were trained on a sub-cohort of 1666 Italian Multiple Sclerosis and Related Disorders Register patients to generate a synthetic dataset of 4878 patients. Synthetic vAlidation FramEwork, Clinical Synthetic Fidelity, and Nearest Neighbor Distance Ratio were applied. Comparisons between treatment strategies were conducted using both real and synthetic data, focusing on progression independent of relapse activity risk.
Results: The synthetic data demonstrated fidelity and privacy preservation. Treatment-effect estimates for escalation treatment versus early intensive treatment were similar across the real and synthetic datasets. Cox proportional hazards models showed that synthetic data preserved the robustness of the estimates for the risk of a first progression independent of relapse activity event. Associations with radiological risk factors were broadly similar across the real and synthetic datasets.
Conclusion: Artificial intelligence-generated synthetic data reliably replicated treatment-effect outcomes of Italian Multiple Sclerosis and Related Disorders Register data, supporting their potential use in clinically defined research settings.
Journal IF-equivalent: 3.5 (OpenAlex 2-year mean citedness, value as of 2026-10-15, retrieved 2026-10-16; not the official Clarivate IF)Reference: Iaffaldano P, D’Amico S, Lucisano G, Copetti M, Guerra T, Rocca MA, et al. Validation of generative artificial intelligence techniques for synthetic data generation in multiple sclerosis research: A comparison with real-world evidence from the Italian multiple sclerosis registry. Mult Scler. 2026 Oct 14 [Epub ahead of print]. doi:10.1177/13524585261492525.Checked: Abstract only - Abstract
Automated lesion segmentation can support rapid and reproducible quantitative assessment and long-term monitoring of multiple sclerosis. Ultra-high-field 7T MRI provides increased spatial resolution and lesion visibility but introduces challenges for automated analysis, including field inhomogeneity, susceptibility-related artifacts, and spatially varying image contrast. Although deep learning methods perform well at conventional field strengths, their application to 7T MRI remains underexplored. We evaluated nnUNet using FLAIR alone, MP2RAGE alone, and combined MP2RAGE+FLAIR. The dataset included 115 longitudinal 7T MRI scans from 57 patients with MS, with 20 scans from 11 independent patients reserved for testing. DeepMedic was included as a comparative architecture. On the test set, nnUNet achieved mean DSC values of 0.72, 0.71, and 0.66 for combined, MP2RAGE-only, and FLAIR-only inputs, respectively. No significant DSC differences were observed between nnUNet and DeepMedic. For both architectures, adding FLAIR to MP2RAGE did not show a statistically significant additional benefit in the present test cohort, whereas MP2RAGE-containing inputs significantly outperformed FLAIR-only input. The findings of the present study provide preliminary evidence suggesting that the observed voxel-level segmentation performance was primarily associated with the inclusion of MP2RAGE. However, lesion-level performance depended on lesion size, indicating further investigation of contrast-specific effects on small-lesion detection.
Journal IF-equivalent: 4.3 (OpenAlex 2-year mean citedness, value as of 2026-10-15, retrieved 2026-10-16; not the official Clarivate IF)Reference: Petrova A, Petrovic I, Berger M, Kravutske Y, Leinkauf J, Rumbak R, et al. Deep Learning-Based Multiple Sclerosis Lesion Segmentation at 7 Tesla: Comparison of Single- and Multi-Contrast MRI Inputs. J Imaging. 2026 Oct 14;12(10):510. doi:10.3390/jimaging12100510.Checked: Abstract only
See all 2 Multiple sclerosis papers →
NMOSD and MOGAD: treatment, trials and AI
No new qualifying paper for this issue.
Myasthenia gravis: treatment, trials and AI
- Abstract
Background: Perioperative risk stratification in thymoma-associated myasthenia gravis remains challenging, and the optimal spatial extent for preoperative computed tomography (CT) radiomics is uncertain. We compared five three-dimensional tumor and peritumoral radiomic representations for discrimination of an in-hospital perioperative composite outcome.
Methods: This retrospective single-center study included 145 patients (37 events). Intratumoral, physical-distance peritumoral (1, 3, and 5 mm), and integrated tumor-plus-3-mm (VOIFusion3mm) representations were compared. Radiomic models used radial-basis-function support vector machines with five-fold nested cross-validation repeated 10 times. Prespecified paired comparisons used 2,000 patient-cluster bootstrap resamples with false-discovery-rate correction. Feature-exclusion, no-LASSO, boundary-perturbation, and clinical-increment analyses were also performed.
Results: The integrated tumor-plus-3-mm representation had the highest mean repeat-level out-of-fold area under the receiver operating characteristic curve (AUC) (0.628; 95% confidence interval (CI), 0.544–0.714), versus 0.536 for the intratumoral and 0.533 for the 3-mm peritumoral representation. AUC differences were 0.092 versus intratumoral (95% CI, 0.029–0.155; q = 0.009) and 0.095 versus 3-mm peritumoral (95% CI, 0.024–0.173; q = 0.015). VOIFusion3mm remained highest-ranked after feature exclusion and in the no-LASSO analysis. Mean AUC changed by − 0.093 after 1-mm inward erosion (95% CI, − 0.169 to − 0.015) and by − 0.004 after outward dilation (95% CI, − 0.028 to 0.019). Radiomics did not demonstrate incremental discrimination beyond the prespecified clinical model, and calibration was limited.
Conclusions: VOIFusion3mm showed higher internally evaluated rank discrimination than the intratumoral and isolated 3-mm peritumoral representations, but absolute discrimination was modest. Radiomics did not demonstrate incremental discrimination beyond the evaluated clinical models, and calibration did not support individualized risk estimation. VOIFusion3mm remains a candidate spatial representation requiring external validation and should not guide individual perioperative decisions. Interpretation is limited by the small number of outcome events, single-center design, and lack of independent multi-reader assessment of segmentation reproducibility.
Journal IF-equivalent: 3.8 (OpenAlex 2-year mean citedness, value as of 2026-10-15, retrieved 2026-10-16; not the official Clarivate IF)Reference: Chai Y, Lu C, Wang Z, Li Z, Li Z, Wang Y, et al. Integrated tumor–peritumoral CT radiomics for perioperative event discrimination in thymoma-associated myasthenia gravis: a retrospective cohort study. BMC Med Imaging. 2026 Oct 14 [Epub ahead of print]. doi:10.1186/s12880-026-02924-w.Checked: Abstract only
See all 1 Myasthenia gravis papers →
Autoimmune encephalitis, CIDP and Guillain-Barre syndrome: treatment, trials and AI
No new qualifying paper for this issue.