DocumentCode :
1757223
Title :
Scoliosis Follow-Up Using Noninvasive Trunk Surface Acquisition
Author :
Adankon, Mathias M. ; Chihab, Najat ; Dansereau, J. ; Labelle, H. ; Cheriet, Farida
Author_Institution :
Ecole Polytech. de Montreal, Montreal, QC, Canada
Volume :
60
Issue :
8
fYear :
2013
fDate :
Aug. 2013
Firstpage :
2262
Lastpage :
2270
Abstract :
Adolescent idiopathic scoliosis (AIS) is a musculoskeletal pathology. It is a complex spinal curvature in a 3-D space that also affects the appearance of the trunk. The clinical follow-up of AIS is decisive for its management. Currently, the Cobb angle, which is measured from full spine radiography, is the most common indicator of the scoliosis progression. However, cumulative exposure to X-rays radiation increases the risk for certain cancers. Thus, a noninvasive method for the identification of the scoliosis progression from trunk shape analysis would be helpful. In this study, a statistical model is built from a set of healthy subjects using independent component analysis and genetic algorithm. Based on this model, a representation of each scoliotic trunk from a set of AIS patients is computed and the difference between two successive acquisitions is used to determine if the scoliosis has progressed or not. This study was conducted on 58 subjects comprising 28 healthy subjects and 30 AIS patients who had trunk surface acquisitions in upright standing posture. The model detects 93% of the progressive cases and 80% of the nonprogressive cases. Thus, the rate of false negatives, representing the proportion of undetected progressions, is very low, only 7%. This study shows that it is possible to perform a scoliotic patient´s follow-up using 3-D trunk image analysis, which is based on a noninvasive acquisition technique.
Keywords :
cancer; data acquisition; diagnostic radiography; feature extraction; genetic algorithms; independent component analysis; medical image processing; muscle; neurophysiology; orthopaedics; patient care; physiological models; 3-D trunk image analysis; AIS patients; Cobb angle; adolescent idiopathic scoliosis; cancers; clinical follow-up; complex spinal curvature; false negatives; full spine radiography; genetic algorithm; independent component analysis; musculoskeletal pathology; noninvasive trunk surface acquisition; statistical model; Genetic algorithms; Principal component analysis; Surface reconstruction; Surface topography; Three-dimensional displays; Vectors; 3-D trunk modeling; genetic algorithm; independent component analysis; pattern recognition; scoliosis; surface topography; Algorithms; Artificial Intelligence; Data Interpretation, Statistical; Female; Humans; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Male; Pattern Recognition, Automated; Principal Component Analysis; Reproducibility of Results; Scoliosis; Sensitivity and Specificity; Torso;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/TBME.2013.2251466
Filename :
6479275
Link To Document :
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