AI Turns Routine Neck MRI Scans Into Numbers to Track Disc Degeneration

Neck pain is one of the most common complaints in modern medicine, and the workhorse test for investigating it is the magnetic resonance imaging scan of the cervical spine. Yet for all the sophistication of the scanners involved, the way radiologists judge the health of the discs between the vertebrae remains stubbornly subjective. A study published in BMC Medical Imaging by a team based at Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine now describes a semi-automated framework that converts the familiar midsagittal T2-weighted neck MRI into hard numbers, using an artificial intelligence segmentation model to measure both the brightness and the geometry of each cervical disc. The results offer a glimpse of how quantitative imaging could eventually replace eyeball-and-grade assessment, while also illustrating, with unusual candor, the limits of what an internal evaluation can honestly claim.

The clinical standard for describing intervertebral disc degeneration is the Pfirrmann grading system, a five-level scale originally devised for lumbar discs and later adapted for the cervical spine. A reader looks at the disc on a T2-weighted image and assigns a grade from I to V based on the loss of the bright nuclear signal, the blurring of the boundary between nucleus and annulus, and the collapse of disc height. The system is widely used and reasonably reproducible when performed by experienced readers, but it is ordinal rather than continuous, which means it cannot capture subtle progression, and it compresses a rich spectrum of tissue change into five coarse bins. Two discs with visibly different appearances can receive the same grade, and the same disc can receive different grades from different readers at different times.

The Suzhou team’s answer was to build a pipeline that begins with a single human decision: an experienced radiologist selects the midsagittal slice, the midline image through the spine where the cervical discs are best profiled. From that point onward, everything is automated. A two-dimensional Swin-UNETR, a transformer-based deep learning architecture, segments four structures on the chosen slice: the vertebral bodies, the intervertebral discs, the cerebrospinal fluid, and the spinal cord. Once these masks exist, the software extracts quantitative indices from each disc, including the relative signal intensity of the disc compared with a reference, a disc height index derived from a Euclidean distance transform, and a height-to-diameter ratio that captures disc shape independently of absolute scale.

The choice of a Swin-UNETR reflects broader trends in medical image analysis. Transformer architectures, which process images through attention mechanisms rather than purely local convolutional filters, have proven adept at capturing long-range context, which matters when the task is to distinguish a disc from the adjacent bone marrow and cerebrospinal fluid on a single grayscale slice. The model was trained and tested entirely within a single institution, and its performance was evaluated on an internal test cohort of 54 patients. The Dice coefficients, a standard overlap metric where 1.0 indicates perfect agreement between automated and reference masks, came in at 0.912 for vertebral bodies, 0.900 for intervertebral discs, and 0.821 for cerebrospinal fluid. For the discs, the structure that matters most for the downstream measurements, that level of agreement is respectable, though the lower score for cerebrospinal fluid hints at the difficulty of segmenting thin, low-contrast fluid spaces.

Reliability of the measurements themselves was assessed in a spectrum-enriched nested subset of 34 patients comprising 170 cervical discs, where automated outputs were compared against reference measurements. When the same distance-transform definition was applied to both automated and reference masks on the physical scale, the disc height index showed a correlation of r = 0.894 and an intraclass correlation coefficient of 0.863, indicating strong agreement. The picture was more complicated for measurements taken from the stored historical image series: the intraclass correlation coefficients were 0.770 for relative signal intensity, 0.635 for the disc height index, and just 0.381 for the height-to-diameter ratio. That last figure is a sobering reminder that signal intensity and geometry measured from routine clinical archives carry variability that can erode reproducibility, a problem that any clinical deployment would need to confront through standardized acquisition protocols.

To establish that the quantitative indices actually track the degenerative process, the researchers turned to a separate spectrum-enriched subset of 30 patients in which two readers independently annotated the discs and assigned Pfirrmann grades. Inter-reader agreement was strong, with a Dice of 0.873 for disc segmentation and a linearly weighted kappa of 0.901 for the Pfirrmann grades, a level of concordance that lends credibility to the grades used as the reference construct. The team then examined how the three quantitative indices related to those grades using patient-clustered statistical analyses adjusted for age, sex, and cervical level, an important step because discs at different cervical levels and in patients of different ages have systematically different baseline appearances.

The associations were consistent in direction. All three indices declined as Pfirrmann grade increased: relative signal intensity showed a Spearman correlation of −0.445 with grade, the stored disc height index −0.384, and the stored height-to-diameter ratio −0.635. After adjustment for age, sex, and level, the overall effect of grade remained statistically significant for all three indices, with P values of 0.00164 for relative signal intensity, 0.0185 for the disc height index, and less than 0.001 for the height-to-diameter ratio. But when the researchers applied the Holm correction for multiple comparisons to the pairwise differences between adjacent grades, only the height-to-diameter ratio retained significant discrimination, and only between grades III and IV and between grades IV and V. In other words, the shape-based index was the most robust at telling neighboring grades apart, while the others captured the overall trend without cleanly separating each step.

The team also tested whether the quantitative features could classify discs into Pfirrmann grades automatically, an exploratory exercise with instructive results. Among the classifiers evaluated, a Random Forest performed best numerically, achieving an accuracy of 0.488 on the five-class task and 0.665 on a simplified three-class version. A Decision Tree model was retained for its interpretability and reached 0.435 and 0.635 respectively. These numbers make clear that five-way grading from a handful of image-derived features remains a hard problem; a continuous degeneration score, rather than a categorical grade, may ultimately be the more natural output for quantitative imaging. The authors are careful to frame the entire classification analysis as exploratory and internally evaluated only.

Indeed, the study’s most admirable quality may be its restraint. The authors state explicitly that the observed associations represent correspondence with an imaging construct rather than independent biological or clinical validation, that all analyses were internal with no external scanner or institutional cohort evaluated, and that further multicenter and multi-scanner validation is required before the framework could be considered for clinical use. This matters because the gap between a promising internal evaluation and a deployable clinical tool is wide. Scanners from different manufacturers, coils, field strengths, and sequence parameters all alter signal intensities and apparent geometry, and a model trained on one center’s archive may not transfer gracefully to another’s. The modest reproducibility of the height-to-diameter ratio on stored historical series is a concrete example of exactly the kind of vulnerability that external validation would expose.

Still, the direction of travel is clear and, for patients with neck pain, potentially consequential. Degenerative cervical myelopathy, the most common cause of spinal cord dysfunction in adults worldwide, often develops silently as discs degenerate, narrow, and impinge on the cord, and earlier, more sensitive detection of disc deterioration could shift diagnosis toward a stage where intervention is simpler and more effective. A framework that turns each disc into a set of continuous, trackable numbers could allow clinicians to follow progression over time with far more sensitivity than a five-point grade allows, and could give researchers the measurement precision needed to test whether new therapies actually slow degeneration. The Suzhou study does not prove that vision, and it is honest enough to say so. What it does provide is a carefully documented demonstration that AI-assisted segmentation can extract physically meaningful, grade-correlated measurements from the images that hospitals already acquire every day, which is precisely the foundation on which the necessary multicenter trials can now be built.

Subject of Research: AI-based quantitative MRI assessment of cervical intervertebral disc degeneration

Article Title: Segmentation-based quantitative characterization of cervical disc degeneration on midsagittal T2-weighted MRI: a retrospective internal evaluation

Article References: Li, X., Gong, C., Tang, Z., Li, Z., Xu, K., Chang, W., Ma, Z., Liu, J., Dai, Y., Li, Y., & Yu, P. (2026). Segmentation-based quantitative characterization of cervical disc degeneration on midsagittal T2-weighted MRI: a retrospective internal evaluation. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02871-6

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02871-6

Keywords: cervical spine, disc degeneration, MRI, deep learning, Swin-UNETR, Pfirrmann grading, image segmentation, quantitative imaging, radiology, spine, machine learning, medical imaging