Validation of a Mathematical Procedure for the Cobb Angle Assessment Based on Photogrammetry

Abstract

Objective: This prospective study aimed to assess the concurrent validity and diagnostic accuracy of a mathematical procedure for measurement of the spinal inclination angle, analogous to the Cobb angle, by means of photogrammetry. Methods: Sixty-one subjects (aged 7 to 18 years), male and female, underwent radiographic (Cobb angle) and photogrammetric (DIPA [Digital Image-based Postural Assessment] angle) evaluations. The measurement of spinal inclination angle obtained through photogrammetry followed the Digital Image-Based Postural Assessment software protocol. Concurrent validity was appraised using Spearman rank correlation, the coefficient of determination, the root-mean-square error, Bland-Altman plot analysis, and receiver operating characteristic analysis, adopting P .05. Results: The analyses were divided according to the topography of the scoliotic curve (thoracic, lumbar, or thoracolumbar). The correlations were excellent (from 0.72 to 0.81) and significant for all the regions of the spine, and the coefficients of determination ranged between 0.75 and 0.88. The root-mean-square error was between 5°and 11°, and the mean difference was very close to 0. The area under the curve was excellent and significant, ranging between 95% and 99%. Conclusion:The mathematical procedure presented is valid to evaluate the spinal inclination angle in photogrammetry, analogous to the Cobb angle in radiography. (

Spine Deformity Assessment for Scoliosis Diagnostics Utilizing Image Processing Techniques: A Systematic Review

“…The instrumentation or imaging modality is crucial in capturing the structure of the spine to obtain good image quality because it affects the accuracy of scoliosis diagnosis. Six out of eighteen studies used common and conventional imaging modalities, such as X-rays, computed tomography (CT), and ultrasounds [18][19][20]28,33,34], while other researchers [21][22][23][24][25][26][27][29][30][31]35] used uncommon instrumentation, like rasterstereography, cameras, EOS imaging, scanners, and 3D laser profilemeters. Special mention goes to Sikidar et al [32], as the study did not collect data as images that used electromyogram (EMG) and ground reaction force (GRF) data.…”

“…Study [18] provided no evaluated parameter, as the study used image processing for the assessment, whereas the authors constructed new parameters or improvised from the current parameter to evaluate scoliosis. The new parameters used for scoliosis evaluation are the vertebrae as a landmark [22,24], rasterstereographic measurements [25], the center of laminae (COL) [28], the Scolioscan angle [29], a digital image-based postural assessment (DIPA) [31], the bending asymmetry index (BAI) [33], EMG and GRF [32] data, and a 3D scoliosis angle [34].…”

“…Three possible approaches have been employed for the assessment method of scoliosis diagnosis where five scholarly articles devoted to the study of artificial intelligence [18,19,25,27,32], nine articles studied image processing [20,22,24,26,28,31,33,34], and four articles studied building a system to diagnose scoliosis [21,23,29,30]. Digital image processing has become the most common form in the medical field, as it is the most efficient and cheapest method.…”

“…The number of subjects or data that participated in the studies varied with the highest being 3240 images and the lowest being 10 patients. Three studies involved a wide age range of individuals (aged between 11 and 86) [20,22,23], and ten studies focused on adolescents and middle-aged individuals (aged between 10 and 30 years old) [18,[24][25][26][27][28][29][30][31][32], while four studies did not provide the age information of the dataset. Fourteen studies provided the required details of the data included in the investigations, such as the inclusion and exclusion criteria.…”

Photogrammetry Applied to Neurosurgery: A Literature Review

“…Photogrammetry is also a reliable method to evaluate different parameters of the human posture, such as the trunk length and inclination, as well as the kyphotic and lordotic angles [ 68 , 71 , 72 ]. The use of machine learning algorithms could hypothetically facilitate even further the assessment and bring it to the level of comfort of a mobile application [ 69 ].…”