ISIS Scanning: A Useful Assessment Technique in the Management of Scoliosis

The aim of this work was to develop a low-cost automated system to measure the three-dimensional shape of the back in patients with scoliosis. The resulting system uses structured light to illuminate a patient's back from an angle while a digital photograph is taken. The height of the surface is calculated using Fourier transform profilometry with an accuracy of ±1 mm. The surface is related to body axes using bony landmarks on the back that have been palpated and marked with small coloured stickers prior to photographing. Clinical parameters are calculated automatically and presented to the user on a monitor and as a printed report. All data are stored in a database. The database can be interrogated and successive measurements plotted for monitoring the deformity changes. The system developed uses inexpensive hardware and open source software. Accurate surface topography can help the clinician to measure spinal deformity at baseline and monitor changes over time. It can help the patients and their families to assess deformity. Above all it reduces the dependence on serial radiography and reduces radiation exposure when monitoring spinal deformity.

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“…The printed report is based on the old ISIS report which has been shown to be clinically valuable [16,34]. The report (Fig.…”

Section: Presentation Of Clinical Parameters In Isis2mentioning

confidence: 99%

A new system for measuring three-dimensional back shape in scoliosis

et al. 2008

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“…Such systems are the Moiré-fringe mapping, the structured light techniques or Integrated Shape Imaging System (ISIS) (Turner-Smith 1984, 1988, Weisz 1988, Theologis 1997, Berryman 2008, 360° scanning devices (Poncet 2000) and stereo-photogrammetric systems (Frobin 1983). Laser techniques were also developed for corset manufacturing, but not for clinical use.…”

Section: Optical Techniquesmentioning

confidence: 99%

Three-Dimensional Assessment of the Scoliosis

Legaye¹

2012

Recent Advances in Scoliosis

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“…The first technique used for this purpose is Moire contour topography where a light-interference pattern projected from a fixed grid near the camera permit to draw contour lines on the back surface [4,5]. Other techniques such as rasterstereography [6,7], integrated shape imaging system (ISIS) [8,9], Quantec scanner [10], were proposed in order to quantify scoliosis deformity from back surface asymmetry. However, the results obtained from the various research were mitigated because of many problems related to the limitations of the avail-able technologies.…”

Section: Introductionmentioning

confidence: 99%

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Adankon

Dansereau

Cheriet

2012

Artificial Intelligence in Medicine

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Non invasive classification system of scoliosis curve types using least-squares support vector machines." Artificial Intelligence in Medicine, Vol. 56, no. 2 (2012): pp. 99-107.The final publication is available via the DOI: http://dx.doi.org/10.1016/j.artmed.2012.07.002The manuscript, in a draft version prior to being accepted by the publisher, is reproduced in the following pages. Mathias AbstractObjective: To determine scoliosis curve types using non invasive acquisition, without any prior knowledge on X-ray data.Methods: Classification of scoliosis deformities according to curve type is used to plan management of scoliosis patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of 3D back surface of the patients. The 3D image of back surface of the trunk is divided into patches and local geometric descriptors characterizing the surface of the back are computed from each patch and forming the features. We perform the reduction of the dimensionality by using principal component analysis and retain 53 components using overlap criterion combined with the total variance in the observed variables. In this work a multi-class classifier is built with leastsquares support vector machine (LS-SVM). The original LS-SVM formulation was modified by weighting differently the positive and negative samples and a new kernel was designed in order to achieve a robust classifier. The proposed system was validated using data of 165 patients with different scoliosis curve types. A comparison of the results of a non invasive classification was done with those obtained by an expert using X-ray images.Results: The average rate of successful classification was computed using * Corresponding author Email addresses: mathias-mahouzonsou.adankon@polymtl.ca (Mathias M. Adankon), jean.dansereau@polymtl.ca (Jean Dansereau), hubert.labelle@recherche-ste-justine.qc.ca (Hubert Labelle), farida.cheriet@polymtl.ca (Farida Cheriet) Preprint submitted to ElsevierJuly 16, 2012 leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. Considering the correct classification rate per class, we obtained 96%, 84% and 97% respectively for thoracic major curve, double major curve and lumbar/thoracolumbar major curve. Conclusion: This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods.The proposed system uses non invasive acquisition which is safety for the patient, no radiation. Also, a design of a specific kernel improved classification performance.

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ISIS Scanning: A Useful Assessment Technique in the Management of Scoliosis

Cited by 49 publications

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A new system for measuring three-dimensional back shape in scoliosis

A new system for measuring three-dimensional back shape in scoliosis

Three-Dimensional Assessment of the Scoliosis

Non invasive classification system of scoliosis curve types using least-squares support vector machines

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