Enamundram Naga Karthik; Sandrine B茅dard; Jan Valo拧ek; Christoph S. Aigner; Elise Bannier; Josef Bedna艡铆k; Virginie Callot; Anna Combes; Armin Curt; Gergely David; Falk Eippert; Lynn Farner; Michael G. Fehlings; Patrick Freund; Tobias Granberg; Cristina Granziera; Ulrike Horn; Tom谩拧 Hor谩k; Suzanne Humphreys; Markus Hupp; Anne Kerbrat; Nawal Kinany; Shannon Kolind; Petr Kudli膷ka; Anna Lebret; Lisa Eunyoung Lee; Caterina Mainero; Allan R. Martin; Megan McGrath; Govind Nair; Kristin P. O鈥橤rady; Jiwon Oh; Russell Ouellette; Nikolai Pfender; Dario Pfyffer; Pierre-Fran莽ois Pradat; Alexandre Prat; Emanuele Pravat脿; Daniel S. Reich; Ilaria Ricchi; Naama Rotem-Kohavi; Simon Schading-Sassenhausen; Maryam Seif; Andrew Smith; Seth A. Smith; Grace Sweeney; Roger Tam; Anthony Traboulsee; Constantina Andrada Treaba; Charidimos Tsagkas; Zachary Vavasour; Dimitri Van De Ville; Kenneth Arnold Weber II; Sarath Chandar; Julien Cohen-Adad (2026).听.听Imaging Neuroscience, 4, Article a.1105.听
This study looks at how measurements of the spinal cord鈥攕uch as its聽cross-sectional area聽(the size of the cord when viewed in a slice)鈥攃an be used as important indicators (biomarkers) for diagnosing and tracking neurological diseases like multiple sclerosis or spinal cord compression. Modern artificial intelligence methods can automatically identify and outline (segment) the spinal cord in MRI scans, but it is unclear whether these measurements stay consistent as models are updated with new data over time. This consistency is especially important when building 鈥渘ormal鈥� reference values from healthy individuals.
To address this, the researchers developed a spinal cord segmentation model trained on a large and diverse dataset collected from 75 sites and over 1,600 participants, covering different MRI types and various spinal cord conditions. They also created a 鈥渓ifelong learning鈥� system that continuously monitors changes in measurements (called聽morphometric drift) whenever the model is updated. This system automatically runs through a workflow (via GitHub Actions, an automated coding tool) to track how measurements evolve over time.
The results showed that the new model performs very well, accurately identifying the spinal cord even in challenging cases such as severe compression or tissue damage, with a high Dice score (a measure of how closely the model鈥檚 segmentation matches the true anatomy) of 0.95. The monitoring system also proved useful for quickly detecting any changes in measurements between model versions. Importantly, the study found that updates to the model caused only minimal shifts in spinal cord measurements, meaning the results remain stable and reliable. This allowed the researchers to safely update an existing database of normal spinal cord measurements. Overall, this work provides a reliable and transparent way to maintain consistency in AI-based medical measurements as models evolve.
Fig 1
Overview of the dataset and image characteristics. Representative axial slices of nine contrasts and the total of images used for each contrast in brackets, the orientation (axial/sagittal) along with the median resolution of images. The respective doughnut chart illustrates the proportion of clinical status among the scanned participants, including healthy controls (HC), patients with radiologically isolated syndrome (RIS), patients with multiple sclerosis (MS), and their different phenotypes, including primary progressive (PPMS) and relapsing-remitting (RRMS), patients with amyotrophic lateral sclerosis (ALS), patients with neuromyelitis optica spectrum disorder (NMOSD), pre-decompression acute traumatic SCI (AcuteSCI), post-decompression traumatic spinal cord injury (SCI), degenerative cervical myelopathy (DCM), and syringomyelia (SYR; not shown). Labels indicate the phenotype associated with the patient, with their respective colors shared across contrast sets.