title: Scoliosis follow-up using noninvasive trunk surface acquisition.
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.
authors.affiliation: École Polytechnique de Montreal, Montreal, Quebec H3C 3A7, Canada. mathias-mahouzonsou.adankon@polymtl.ca
authors.forename: Mathias M
authors.initials: MM
authors.lastname: Adankon
authors.forename: Najat
authors.initials: N
authors.lastname: Chihab
authors.forename: Jean
authors.initials: J
authors.lastname: Dansereau
authors.forename: Hubert
authors.initials: H
authors.lastname: Labelle
authors.forename: Farida
authors.initials: F
authors.lastname: Cheriet
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012600:Scoliosis / Q000175:diagnosis*; D012680:Sensitivity and Specificity; D060726:Torso / Q000473:pathology*