Purpose: The purpose of this study was to detect any possible prognostic factors which may affect the spinal deformity progression and their relationships in idiopathic scoliosis.

Methods: The stereo-radiograph of whole spine at each visit was reconstructed with two spinal x-ray images in PA 0° and 20° using DLT techniques. Sequential data sets with 3, 4 or 5 successive values of prognostic factors were extracted from 111 consecutive patients (12.3±2.3 yrs, Cobb angle 30.2±12.4°) and separated into the stable and the progressed groups, based on a progression threshold of Cobb angle 5° and 10°. The prognostic factors included gender, curve pattern, age, curve magnitude, apex location, lateral deviation and spinal growth. Effects of those factors were conducted by comparing them between two groups (statistical significances p< 0.05) and the relationships were determined using Pearson’s correlation coefficient (r).

Results: The progressed subjects were predominantly females (50–79%) with double curves. Double curves progressed on both curves RT and LL at the same times and alternatively. There were no significant differences of initial ages and ages with maximum curve magnitudes between two groups. Initial and maximum curve magnitudes were significantly large in the progressed group, but no significantly different between maximum curve magnitudes in the stable group and initial curve magnitude in the progressed group. High curve apex locations were observed in the progressed group. Initial and maximum apex lateral deviations were clearly different in two groups and correlated with curve magnitudes from well to excellent (r = 0.43–0.98). The relationships between the spinal growth and the curve progressing were not consistent (r = −0.6 – +0.6). There were no evidences to show the significant differences of spinal growths between groups and genders.

Conclusions: Scoliosis progression is case dependent. Double curves dynamically progress between curve regions. Initial curve magnitudes have more significant effect on the progression than initial ages. A great progression can be expected from curves with high apex location. Apex lateral deviations are changing with curve magnitudes and spinal growths and, however, the curve magnitudes are not always increased with spinal growths. Funding: 2 Funding Parties: Alberta Provincial CIHR Training Program in Bone and Joint Health

The pathogenesis of scoliosis progression remains poorly understood. Seventy-two subject data sets, consisting of four successive values of Cobb-angle and lateral deviations at apices for six and twelve-months intervals in the coronal plane, were used to train and test an artificial neural network (ANN) to predict spinal deformity progression. The accuracies of the trained ANN (3-4-1) for training and testing data were within 3.64° (±2.58°) and 4.40° (±1.86°) of Cobb angles, and within 3.59 (±3.96) mm and 3.98 (±3.41) mm of lateral deviations, respectively. The adapted technique for predicting the scoliosis deformity progression has promising clinical applications.

Scoliosis is a common and poorly understood three-dimensional spinal deformity. The study purpose is to predict scoliosis progression at six and twelve months intervals in the future using successive spinal indices with an artificial neural network (ANN).

The adapted ANN technique enables earlier detection of scoliosis progression with high accuracy. Improved prediction of scoliosis progression will impact bracing or surgical treatment decisions, and may decrease hazardous X-ray exposure.

Seventy-two data sets from adolescent idiopathic scoliosis subjects recruited at the Alberta Children’s Hospital were used in this study. Data sets composed of four successive values of Cobb angles and lateral deviations at apices for six and twelvemonth intervals (coronal plane) were extracted to train and test a specific ANN for predicting scoliosis progression.

Progression patterns in Cobb angles (n = 10) and lateral deviations (n = 8) were successfully identified. The accuracies of the trained ANN (3-4-1) with the training and testing data sets were 3.64° (±2.58°) and 4.40° (±1.86°) of Cobb angles, 3.59 (±3.96) mm and 3.98 (±3.41) mm of lateral deviations, respectively. These results are in close agreement with those using cubic spline extrapolation techniques (3.49° ± 1.85° and 3.31 ± 4.22 mm) and adaptive neuro-fuzzy inference system (3.92° ±3.53° and 3.37 ±3.95 mm) for the same testing data.

ANN can be a promising technique for prediction of scoliosis progression with substantial improvements in accuracy over current techniques, leading to potentially important implications for scoliosis monitoring and treatment decisions.

Funding: AHFMR, CIHR, Fraternal Order of Eagles, NSERC, GEOIDE.

This research sought a mathematical model to relate the postero-anterior (PA) and lateral (LAT) views of the spinal curve in scoliosis in an attempt to justify the acquisition of only One X-ray, thereby reducing patient exposure to harmful X-radiation while preserving complete 3D characterization of the spine. Using powerful developments in functional statistics and machine learning, no such relation could be found. Thus, this research sustained the clinical decision to acquire two biplanar X-rays and supported current research in 3D spinal curvature analysis.

Scoliosis is monitored through full spinal X-rays, and this serial protocol causes an increased incidence of cancer development. This research sustains the clinical decision at Hôpital Sainte-Justine in Montréal and elsewhere to acquire postero-anterior (PA) and lateral (LAT) X-rays, despite the increased exposure to X-radiation. Indeed, geometrically, these two views are required to reconstruct the spine in 3D. However, under the assumption of strong physiological patterns between the PA and LAT views of the spinal curve, one of these X-rays may be redundant for some or all patients. The purpose of this study was to seek this a priori assumption.

To this end, a database consisting of three hundred and sixty-nine spinal reconstructions from distinct patients was used. Two powerful geometric modeling approaches were exploited: functional data analysis and minimum noise fractions. These resulted in five comprehensive, uncorrelated and noise-insensitive features in each plane. Simple linear regression yielded no relation that was statistically significant (p< 0.05) and genereralizable to a set of previously unseen samples. Therefore, nonlinear relational modeling was attempted using support vector regression, a recent advance in machine learning theory. This tool was incapable of identifying a robust regression, suggesting that the PA and LAT views are mathematically independent. Thus, this study highlights the necessity of two biplanar X-rays to evaluate scoliotic deformities and fully characterize spinal shape. Further, this study supports the practical insufficiency observed by clinical staff with respect to current 2D scoliosis classifications that has resulted in current efforts to propose 3D classification schemes.