AI and Neurological Rare & Neuroimmune Diseases ── Basic research ── 2026-10-10

Vertical ground reaction force gait signals are converted into recurrence plots and spectrograms. A lightweight dual-branch CNN fuses them to classify Parkinson's disease, ALS, Huntington's disease and healthy controls. On a dataset of 64 subjects, accuracy was 96.15%, above the single-branch models at 94.19% and 95.75%. The study is retrospective and only the abstract was checked. — Image abstract — the whole article on one page (click to enlarge)

1 Basic research papers. Lead: Gait-based neurodegenerative disease classification using multimodal temporal-spectral representations and a dual-branch CNN.

Journals covered and how papers are chosen: see the index

Neurodegeneration and neuroimmunology research and AI

Gait-based neurodegenerative disease classification using multimodal temporal-spectral representations and a dual-branch CNN.
Computer methods in biomechanics and biomedical engineering · 2026-10-08 · Evidence: Retrospective observational (model development and evaluation on an existing dataset)
Abstract
Neurodegenerative diseases (NDDs) often cause gait impairments with overlapping motor symptoms, complicating multi-class discrimination. This study proposes a lightweight dual-branch convolutional neural network integrating recurrence plots and spectrogram representations of vertical ground reaction force signals to classify Parkinson's disease, amyotrophic lateral sclerosis, Huntington's disease, and healthy controls. Evaluated on the Gait in NDDs Dataset comprising 64 subjects, the proposed framework achieved 96.15% accuracy, outperforming recurrence-based (94.19%) and spectrogram-based (95.75%) single-branch models. The findings demonstrate that combining complementary temporal recurrence and spectral information improves discrimination of neurodegenerative gait patterns while maintaining low computational complexity.
Journal IF-equivalent: 1.7 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)
Reference: Ozeloglu İG, Akman Aydin E. Gait-based neurodegenerative disease classification using multimodal temporal–spectral representations and a dual-branch CNN. Computer Methods in Biomechanics and Biomedical Engineering. 2026 Oct 8 [Epub ahead of print]. doi:10.1080/10255842.2026.2736243.
Checked: Abstract only

← Back to this issue