AI and Neurological Rare & Neuroimmune Diseases ── Basic research ── 2026-10-25

Journals covered and how papers are chosen: see the index

Neurodegeneration and neuroimmunology research and AI

• A deep learning framework for automated cervical spinal cord vertebral labeling and morphometric extraction from brain MRI with large-scale multicenter validation
  npj Digital Medicine · 2026-10-23 · Evidence: Model development and external validation on existing multicenter MRI data (observational)
  Abstract

  This study aimed to develop and validate a fully automated pipeline for vertebral level identification (C1–C3) of the upper cervical spinal cord (SC) in T1-weighted brain MRI with a quality control module to assess the generated masks. MRI data from 998 controls and 879 patients with hereditary ataxias (32 sites; age 5–85 years; 46% male) were used to train deep learning models (nnU-Net, U-Net with deep supervision, SwinUNETR) for automatic vertebral labeling of C1–C3 levels. A secondary model based on VGG, ResNet, and EfficientNet architectures was trained to identify vertebral labeling errors. The pipeline also generates image quality metrics including signal-to-noise ratio, contrast-to-noise ratio, coefficient of joint variation, and entropy focus criterion. External validation was performed using 2737 MRIs from four public datasets, and clinical utility was demonstrated in a longitudinal cohort of 51 patients with multiple sclerosis. The nnU-Net achieved the highest performance (Dice: C1 = 0.85, C2 = 0.89, C3 = 0.89), labeling 95.8% of mislabeled cases from the Spinal Cord Toolbox. The VGG-based classifier achieved 97.9% accuracy and 98.7% sensitivity. External validation showed labeling accuracies exceeding 92% in four of five datasets and sensitivity to longitudinal SC atrophy in multiple sclerosis. This pipeline enables scalable, reliable vertebral labeling and morphometric extraction of the upper cervical SC for multicenter studies and supports biomarker development in neurodegenerative diseases.

  Journal IF-equivalent: 13.9 (OpenAlex 2-year mean citedness, value as of 2026-10-18, retrieved 2026-10-19; not the official Clarivate IF)
  Reference: Jarola GM, Shiraishi DH, Adanyeguh IM, Ashizawa T, Barsottini OGP, Bender B, et al. A deep learning framework for automated cervical spinal cord vertebral labeling and morphometric extraction from brain MRI with large-scale multicenter validation. npj Digit Med. 2026 Oct 23 [Epub ahead of print]. doi:10.1038/s41746-026-03403-w.
  Checked: Abstract only

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