VERIDAH AI: Preventing Wrong-Level Spine Surgery Errors

The Challenge of Spinal Enumeration Anomalies

Accurate automated vertebra labeling remains a critical challenge in clinical neurosurgery and diagnostic radiology. Normal spinal anatomy presents seven cervical, twelve thoracic, and five lumbar vertebrae. However, anatomical variations occur frequently in the general population. Up to thirty percent of individuals harbor enumeration anomalies, such as lumbosacral transitional vertebrae. These anatomical variations include eleven or thirteen thoracic vertebrae and four or six lumbar segments. Consequently, standard radiological assessments often miss or miscount these transitional segments. Such misidentifications can lead to devastating surgical consequences, including wrong-level spine interventions. Furthermore, conventional automated tools regularly fail when encountering atypical anatomical variations.

How the VERIDAH Algorithm Overcomes Labeling Hurdles

To solve this diagnostic dilemma, researchers developed Vertebra Identification with Anomaly Handling, known as VERIDAH. Specifically, the framework utilizes multiple classification heads combined with a weighted vertebra sequence prediction algorithm. Existing deep learning architectures typically assume standardized vertebral counts. In contrast, VERIDAH explicitly accounts for anatomical variations across both MRI and CT datasets. The model analyzes sagittal magnetic resonance images and arbitrary field-of-view computed tomography scans. Moreover, the algorithm independently predicts sequence transitions rather than enforcing rigid anatomical templates. This unique design enables robust performance even when vertebral morphology deviates from classic patterns.

Exceptional Accuracy in MRI and CT Validation

The investigators validated VERIDAH on 1,000 sagittal T2-weighted MRI scans and 365 CT images. On T2-weighted MRI scans, the model correctly labeled all vertebrae in 98.30 percent of patients. In comparison, existing deep-learning architectures achieved only 94.24 percent accuracy on identical datasets. Furthermore, on CT imaging, VERIDAH demonstrated an impressive 99.18 percent complete labeling accuracy versus 77.26 percent for baseline models. The model also excelled at detecting subtle transitional anatomy. Specifically, VERIDAH identified thoracic enumeration anomalies in 87.80 percent of MRI scans and 96.30 percent of CT scans. In the lumbar region, detection rates reached 94.48 percent for MRI and 97.22 percent for CT scans. Therefore, the architecture establishes a new benchmark for diagnostic reliability through advanced muscular & skeletal radiology evaluations.

Clinical Impact of Automated Vertebra Labeling

For spine surgeons and radiologists in high-volume settings, automated vertebra labeling offers immense safety benefits. Ambiguous vertebral numbering represents one of the primary drivers of wrong-level lumbar surgery. When patients exhibit sacralized L5 or lumbarized S1 segments, manual counting often generates conflicting radiological reports. Consequently, operating surgeons face substantial medico-legal risks and patient morbidity. By integrating anomaly-aware algorithms into hospital picture archiving systems, clinical teams can standardize preoperative localization. Additionally, early recognition of transitional segments helps clinicians evaluate persistent back pain and facet arthropathy. Ultimately, automated labeling bridges the gap between diagnostic imaging and flawless surgical execution.

Frequently Asked Questions

Q1: What are spinal enumeration anomalies?

Spinal enumeration anomalies represent numerical variations in vertebral segments, such as having eleven or thirteen thoracic vertebrae or transitional lumbosacral vertebrae.

Q2: Why do conventional deep-learning models struggle with vertebra labeling?

Most conventional models enforce rigid assumptions of seven cervical, twelve thoracic, and five lumbar vertebrae, causing sequence misclassification during anatomical variations.

Q3: How does VERIDAH improve surgical safety?

VERIDAH automatically recognizes atypical segment counts, preventing spinal level misidentification and significantly reducing the risk of wrong-level spine surgeries.

References

1. Möller H et al. VERIDAH: solving enumeration anomaly-aware vertebra labeling across imaging sequences. Eur Radiol. 2026 Oct 16. doi: 10.1007/s00330-026-12966-x. PMID: 42886249.
2. Albano S, et al. Risks Associated with Surgical Management of Lumbosacral Transitional Vertebrae: Systematic Review of Surgical Considerations and Illustrative Case. World Neurosurg. 2024;186:e643-e653.
3. Konin GP, Walz DM. Lumbosacral transitional vertebrae: classification, imaging findings, and clinical relevance. AJNR Am J Neuroradiol. 2010;31(10):1778-1786.