BACK SURFACE ASSESSMENT OF SCOLIOSIS SEVERITY BY NEURAL NETWORK.

Abstract

Recent studies have shown that scoliotic deformity can be estimated accurately from deformity of the full three hundred and sixty degrees torso shape. However, acquisition of these data requires an expensive multi-scanner system. If it was possible to estimate accurately scoliosis from the back surface shape alone, a single scanner and simplified analysis methods could be used. Here, we estimated the Cobb angle within ten degrees in 84% of forty-six patients from back surface data, compared to 99% within ten degrees for a previous, larger study using the entire torso shape. These results suggested that both back-surface and full-torso models for Cobb angle estimation should be pursued for their potential merits.

The surface deformity of scoliosis, often the primary patient complaint, progresses non-linearly with the underlying spinal deformity. If it was possible to estimate reliably the degree of scoliosis from the surface, adolescent patients with non-progressing scoliosis could be spared harmful X-ray radiation. Some of us have previously estimated the scoliotic Cobb angle from three hundred and sixty degrees torso surface deformity. Here, we tested how accurately the Cobb angle could be estimated from back surface data alone, which are easier and less expensive to obtain than full-torso data.

A genetic algorithm selected the clinical parameters to be used by a neural network to estimate scoliosis deformity from back surface deformity. We had forty-six consecutive patients with right-thoracic curves (Cobb angles eleven to ninety-seven degrees), in whom fifteen indices were available including age, height, bracing status, scoliometer reading, back surface rotation, and cosmetic score of landmark asymmetry. Those data were used by a neural network to estimate the Cobb angle within ten degrees in 84% of patients, a 30% improvement over regression-model accuracy, though less accurate than use of the three hundred and sixty degrees torso shape which estimated up to 99% of curves within ten degrees in a previous study.

Neural network predictive accuracy was better when using the full three hundred and sixty degrees torso shape, but the simpler and more economical acquisition of back surface data alone also gave promising results. This pilot comparison study suggested that both models (using back surface data alone vs. using three hundred and sixty degrees torso data) should continue to be developed in attempts to optimize surface estimation of scoliosis.