STUDY DESIGN: Correlation of torso scan and three-dimensional radiographic data in 65 scans of 40 subjects. OBJECTIVES: To assess whether full-torso surface laser scan images can be effectively used to estimate spinal deformity with the aid of an artificial neural network. SUMMARY OF BACKGROUND DATA: Quantification of torso surface asymmetry may aid diagnosis and monitoring of scoliosis and thereby minimize the use of radiographs. Artificial neural networks are computing tools designed to relate input and output data when the form of the relation is unknown. METHODS: A three-dimensional torso scan taken concurrently with a pair of radiographs was used to generate an integrated three-dimensional model of the spine and torso surface. Sixty-five scan-radiograph pairs were generated during 18 months in 40 patients (Cobb angles 0-58 degrees ): 34 patients with adolescent idiopathic scoliosis and six with juvenile scoliosis. Sixteen (25%) were randomly selected for testing and the remainder (n = 49) used to train the artificial neural network. Contours were cut through the torso model at each vertebral level, and the line joining the centroids of area of the torso contours was generated. Lateral deviations and angles of curvature of this line, and the relative rotations of the principal axes of each contour were computed. Artificial neural network estimations of maximal computer Cobb angle were made. RESULTS: Torso-spine correlations were generally weak (r < 0.5), although the range of torso rotation related moderately well to the maximal Cobb angle (r = 0.64). Deformity of the torso centroid line was minimal despite significant spinal deformity in the patients studied. Despite these limitations and the small data set, the artificial neural network estimated the maximal Cobb angle within 6 degrees in 63% of the test data set and was able to distinguish a Cobb angle greater than 30 degrees with a sensitivity of 1.0 and specificity of 0.75. CONCLUSIONS: Neural-network analysis of full-torso scan imaging shows promise to accurately estimate scoliotic spinal deformity in a variety of patients.
STUDY DESIGN: Correlation of torso scan and three-dimensional radiographic data in 65 scans of 40 subjects. OBJECTIVES: To assess whether full-torso surface laser scan images can be effectively used to estimate spinal deformity with the aid of an artificial neural network. SUMMARY OF BACKGROUND DATA: Quantification of torso surface asymmetry may aid diagnosis and monitoring of scoliosis and thereby minimize the use of radiographs. Artificial neural networks are computing tools designed to relate input and output data when the form of the relation is unknown. METHODS: A three-dimensional torso scan taken concurrently with a pair of radiographs was used to generate an integrated three-dimensional model of the spine and torso surface. Sixty-five scan-radiograph pairs were generated during 18 months in 40 patients (Cobb angles 0-58 degrees ): 34 patients with adolescent idiopathic scoliosis and six with juvenile scoliosis. Sixteen (25%) were randomly selected for testing and the remainder (n = 49) used to train the artificial neural network. Contours were cut through the torso model at each vertebral level, and the line joining the centroids of area of the torso contours was generated. Lateral deviations and angles of curvature of this line, and the relative rotations of the principal axes of each contour were computed. Artificial neural network estimations of maximal computer Cobb angle were made. RESULTS: Torso-spine correlations were generally weak (r < 0.5), although the range of torso rotation related moderately well to the maximal Cobb angle (r = 0.64). Deformity of the torso centroid line was minimal despite significant spinal deformity in the patients studied. Despite these limitations and the small data set, the artificial neural network estimated the maximal Cobb angle within 6 degrees in 63% of the test data set and was able to distinguish a Cobb angle greater than 30 degrees with a sensitivity of 1.0 and specificity of 0.75. CONCLUSIONS: Neural-network analysis of full-torso scan imaging shows promise to accurately estimate scoliotic spinal deformity in a variety of patients.
Authors: Lisa R Gabor; Andrew P Chamberlin; Ellen Levy; Monique B Perry; Holly Cintas; Scott M Paul Journal: Am J Phys Med Rehabil Date: 2011-10 Impact factor: 2.159
Authors: Theodoros B Grivas; Marian H Wade; Stefano Negrini; Joseph P O'Brien; Toru Maruyama; Martha C Hawes; Manuel Rigo; Hans Rudolf Weiss; Tomasz Kotwicki; Elias S Vasiliadis; Lior Neuhaus Sulam; Tamar Neuhous Journal: Scoliosis Date: 2007-11-26
Authors: Nils Christian Lehnen; Robert Haase; Jennifer Faber; Theodor Rüber; Hartmut Vatter; Alexander Radbruch; Frederic Carsten Schmeel Journal: Diagnostics (Basel) Date: 2021-05-19