Markerless spinal assessment: a purely geometric framework for automated landmark detection from 3D surface scans

Non-invasive assessment of spinal morphology traditionally relies on subjective manual palpation or data-driven deep learning models that often lack metrological interpretability. This study presents a fully automated, purely geometric framework for the localization and morphometric characterization of spinous processes directly from 3D dorsal surface scans. Leveraging continuous differential geometry and the scale-invariant properties of the shape index, the proposed algorithm operates independently of training data, physical markers, and ionizing radiation. Validation against expert clinical palpation demonstrated high spatial reliability, yielding a mean spatial deviation of 3.7 mm and a 98.6% Confidence Index in matching manually identified landmarks. Notably, the computational framework exhibited heightened morphological sensitivity, consistently extracting mathematically valid geometric signatures in spinal regions conservatively bypassed by the clinician. This resulted in a theoretical algorithmic False Negative Rate bounded between 13.5% and 14.6%, compared to a 33.1% manual rate. Furthermore, the system autonomously evaluated morphometric parameters, such as basal width, symmetry index, and local apical prominence, successfully classifying 327 apophyses into “prominent” (19%) and “masked” (81%) morphotypes across complex, soft-tissue-mediated topographies. While these findings highlight the method’s capability to standardize and objectify dorsal surface assessment, definitive anatomical fidelity requires future radiological correlation. Ultimately, this robust topographic profiling lays the foundation for autonomously quantifying structural asymmetries in clinical spinal deformities.