Comput. Biol. Med. - Statistical model based 3D shape prediction of postoperative trunks for non-invasive scoliosis surgery planning.

Tópicos

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Resumo

One of the major concerns of scoliosis patients undergoing surgical treatment is the aesthetic aspect of the surgery outcome. It would be useful to predict the postoperative appearance of the patient trunk in the course of a surgery planning process in order to take into account the expectations of the patient. In this paper, we propose to use least squares support vector regression for the prediction of the postoperative trunk 3D shape after spine surgery for adolescent idiopathic scoliosis. Five dimensionality reduction techniques used in conjunction with the support vector machine are compared. The methods are evaluated in terms of their accuracy, based on the leave-one-out cross-validation performed on a database of 141 cases. The results indicate that the 3D shape predictions using a dimensionality reduction obtained by simultaneous decomposition of the predictors and response variables have the best accuracy.

Resumo Limpo

one major concern scoliosi patient undergo surgic treatment aesthet aspect surgeri outcom use predict postop appear patient trunk cours surgeri plan process order take account expect patient paper propos use least squar support vector regress predict postop trunk d shape spine surgeri adolesc idiopath scoliosi five dimension reduct techniqu use conjunct support vector machin compar method evalu term accuraci base leaveoneout crossvalid perform databas case result indic d shape predict use dimension reduct obtain simultan decomposit predictor respons variabl best accuraci

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