AI and Neurological Rare & Neuroimmune Diseases ── 2026-10-29
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（1） · ALS（0） · Parkinson's（1） · 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
- Abstract
BackgroundNeurodegenerative diseases like Alzheimer's and Parkinson's pose significant diagnostic challenges because they are incurable. Their symptoms often overlap with those of normal ageing and involve progressive neuronal loss. This makes early prediction, proper diagnosis, and ongoing monitoring crucial at every stage of the disease. Deep learning has become a useful tool for handling different types of data. However, the performance of these models relies heavily on the availability and variety of data, making it important to characterize datasets.ObjectiveThis PRISMA-based systematic review looks at 111 studies from 2019 to April 2026, organized into three clinical stages of neurodegenerative diseases: early prediction, diagnosis, and progression.MethodsEach study is assessed across four dimensions: data type (neuroimaging or non-neuroimaging), datasets (open-access, self-created, or hybrid), deep learning model architecture, and the use of explainable AI.ResultsMRI is the only imaging modality that spans all three stages. Speech is the most common non-neuroimaging method for diagnosis, while gait is used only for diagnosis and progression. The ADNI and PPMI datasets are the most commonly used. Self-created datasets introduce new modalities such as ECG and iPSCs, which are not found in open-access datasets. CNNs are the leading models for neuroimaging tasks, while LSTMs and RNNs capture temporal patterns in longitudinal datasets. Survival models are exclusively used for ALS and LATE. Fewer than 6% of the studies used confirmed XAI. Only 2 of the 111 studies validated their results on an independent external cohort.ConclusionThis review is the only one that covers all three disease stages across multiple neurodegenerative diseases.
Journal IF-equivalent: 2.5 (OpenAlex 2-year mean citedness, value as of 2026-10-28, retrieved 2026-10-29; not the official Clarivate IF)Reference: Poddar K, Wahi C. Deep learning models to detect neurodegenerative diseases across different modalities, datasets, and stages: A systematic review. Technology and Health Care. 2026 Oct 28 [Epub ahead of print]. doi:10.1177/09287329261502060.Checked: Abstract only
See all 1 Basic research papers →
ALS: treatment, trials and AI
No new qualifying paper for this issue.
Parkinson's disease and parkinsonism: treatment, trials and AI
- Abstract
OBJECTIVES: Advanced Parkinson disease (APD) is often under-recognized, which may delay treatment optimization and referral for device-aided therapy assessment. Machine learning-enabled remote monitoring systems (ML-RMS) may support earlier recognition of progression, but their value depends on whether alerts lead to clinical action and patient benefit. METHODS: We developed a Markov model comparing standard care with ML-RMS-supported care from the Finnish health care perspective. The model simulated persons with Parkinson disease at risk of APD over 16 years. The base case assumed ML-RMS sensitivity and specificity of 88 percent and 62 percent. Costs, quality-adjusted life years (QALYs), incremental cost-effectiveness ratios, and incremental net monetary benefit were estimated. Scenario and probabilistic sensitivity analyses explored diagnostic accuracy, monitoring costs, direct quality-of-life effects, deep brain stimulation (DBS) costs, pathway timing, and delayed nursing home admission. RESULTS: In the base case, ML-RMS generated 0.0044 additional QALYs at an additional cost of €761 per patient, resulting in an incremental cost-effectiveness ratio of €172,023 per QALY gained. At a willingness-to-pay threshold of €35,000 per QALY, the incremental net monetary benefit was negative. Scenario analyses indicated that ML-RMS could become cost-effective if active monitoring produced direct quality-of-life gains or delayed nursing home admission. CONCLUSIONS: ML-RMS is unlikely to be economically favorable if limited to earlier APD recognition and timely DBS treatment in a small eligible subgroup. Its value depends on whether monitoring changes care pathways, improves patient outcomes, or reduces long-term care needs. Future studies should prioritize clinical utility, quality-of-life effects, treatment decisions, and institutional care.
Journal IF-equivalent: 0.7 (OpenAlex 2-year mean citedness, value as of 2026-10-28, retrieved 2026-10-29; not the official Clarivate IF)Reference: Godoy Junior CA, van Deen WK, Mäkitie L, Koivu M, Bakker LJ, Uyl-de Groot CA, et al. Early health technology assessment of machine learning-enabled remote monitoring for advanced Parkinson disease. Int J Technol Assess Health Care. 2026 Oct 28;42(1):e104. doi:10.1017/s0266462326104310. PMID: 42947537.Checked: Abstract only
See all 1 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/Objectives: The central vein sign can help characterize multiple sclerosis (MS) lesions on MRI, but reading it takes specialist time and experience. We developed a compact convolutional neural network (CNN) and a deep feature engineering (DFE) pipeline for three-class classification of clinically labeled MR images.
Methods: The MS Vein dataset contains 3181 axial FLAIR images from 154 participants in vein-positive, vein-negative, and control classes. MACDIBNeXt (about 6.5 million parameters) combines pooling-based attention downsampling, grouped convolution, and bottleneck and inverted bottleneck blocks. It was pretrained on Stable ImageNet1k and fine-tuned on a participant-level split (98 training, 25 validation, and 31 test participants). The DFE pipeline extracted 768 global average pooling features, selected 47 with CWINCA in participant-grouped folds of the training data, and classified them with tkNN.
Results: On 635 held-out test images from 31 participants, the standalone CNN classified 527 correctly (82.99%; participant-cluster bootstrap 95% CI, 77.93–87.66%). DFE classified 571 correctly (89.92%; 85.47–93.82%), with 89.13% macro F1 and Cohen’s κ of 0.83. With participants resampled as clusters, the 6.93-point gain had a 95% CI of 3.33–10.63 points (p = 0.0002), and a cluster-adjusted McNemar test gave p = 0.0021. Grad-CAM activation was intracranial in the displayed examples.
Conclusions: MACDIBNeXt with DFE gives promising internal, image-level results. Because the data are retrospective, single-center, and labeled on FLAIR images, external participant-level validation is required before clinical use.
Journal IF-equivalent: 4.3 (OpenAlex 2-year mean citedness, value as of 2026-10-26, retrieved 2026-10-28; not the official Clarivate IF)Reference: Tasci I, Elidar G, Albayrak S, Goktas OF, Tuncer T, Baygin M, et al. Image-Level Classification of Central Vein Categories on FLAIR MRI in Multiple Sclerosis Using a Lightweight Explainable Deep Learning Framework. Brain Sci. 2026 Oct 26;16(11):1160. doi:10.3390/brainsci16111160.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.