title: Identification of spinal deformity classification with total curvature analysis and artificial neural network.
abstract: In this paper, a multilayer feed-forward, back-propagation (MLFF/BP) artificial neural network (ANN) was implemented to identify the classification patterns of the scoliosis spinal deformity. At the first step, the simplified 3-D spine model was constructed based on the coronal and sagittal X-ray images. The features of the central axis curve of the spinal deformity patterns in 3-D space were extracted by the total curvature analysis. The discrete form of the total curvature, including the curvature and the torsion of the central axis of the simplified 3-D spine model was derived from the difference quotients. The total curvature values of 17 vertebrae from the first thoracic to the fifth lumbar spine formed a Euclidean space of 17 dimensions. The King classification model was tested on this MLFF/BP ANN identification system. The 17 total curvature values were presented to the input layer of MLFF/BP ANN. In the output layer there were five neurons representing five King classification types. A total of 37 spinal deformity patterns from scoliosis patients were selected. These 37 patterns were divided into two groups. The training group had 25 patterns and testing group had 12 patterns. The 25-pattern training group was further divided into five subsets. Based on the definition of King classification system, each subset contained all five King types. The network training was conducted on these five subsets by the hold-out method, one of cross-validation variants, and the early stop method. In each one of the five cross-validation sessions, four subsets were alternatively used for estimation learning and one subset left was used for validation learning. Final network testing was conducted with remaining 12 patterns in testing group after the MLFF/BP ANN was trained by all five subsets in training group. The performance of the neural network was evaluated by comparing between two network topologies, one with one hidden layer and another with two hidden layers. The results are shown in three tables. The first table shows network errors in estimation learning and the second table shows identification rates in validation learning. The network errors and identification rates in the last round of network training and testing are shown in the third table. Each table has a comparison for both one hidden layer and two hidden layer networks.
authors.affiliation: Seay Center for Musculoskeletal Research, Texas Scottish Rite Hospital for Children, 2222 Welborn Street, Dallas, TX 75252, USA. honglin999@ieee.org
authors.forename: Hong
authors.initials: H
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012600:Scoliosis / Q000000981:diagnostic imaging*; D012680:Sensitivity and Specificity