Artif Intell Med - Non invasive classification system of scoliosis curve types using least-squares support vector machines.

Tópicos

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Resumo

JECTIVE: To determine scoliosis curve types using non invasive surface acquisition, without prior knowledge from X-ray data.METHODS: Classification of scoliosis deformities according to curve type is used in the clinical management of scoliotic patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of the 3D back surface of the patients. The 3D image of the surface of the trunk is divided into patches and local geometric descriptors characterizing the back surface are computed from each patch and constitute the features. We reduce the dimensionality by using principal component analysis and retain 53 components using an overlap criterion combined with the total variance in the observed variables. In this work, a multi-class classifier is built with least-squares support vector machines (LS-SVM). The original LS-SVM formulation was modified by weighting the positive and negative samples differently and a new kernel was designed in order to achieve a robust classifier. The proposed system is validated using data from 165 patients with different scoliosis curve types. The results of our non invasive classification were compared with those obtained by an expert using X-ray images.RESULTS: The average rate of successful classification was computed using a leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. As for the correct classification rates per class, we obtained 96%, 84% and 97% for the thoracic, double major and lumbar/thoracolumbar curve types, respectively.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 surface acquisition, which is safe for the patient as it involves no radiation. Also, the design of a specific kernel improved classification performance.

Resumo Limpo

jectiv determin scoliosi curv type use non invas surfac acquisit without prior knowledg xray datamethod classif scoliosi deform accord curv type use clinic manag scoliot patient work propos robust system can determin scoliosi curv type non invas acquisit d back surfac patient d imag surfac trunk divid patch local geometr descriptor character back surfac comput patch constitut featur reduc dimension use princip compon analysi retain compon use overlap criterion combin total varianc observ variabl work multiclass classifi built leastsquar support vector machin lssvm origin lssvm formul modifi weight posit negat sampl differ new kernel design order achiev robust classifi propos system valid use data patient differ scoliosi curv type result non invas classif compar obtain expert use xray imagesresult averag rate success classif comput use leaveoneout crossvalid procedur overal accuraci system correct classif rate per class obtain thorac doubl major lumbarthoracolumbar curv type respectivelyconclus studi show possibl find relationship intern deform back surfac deform scoliosi machin learn method propos system use non invas surfac acquisit safe patient involv radiat also design specif kernel improv classif perform

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