Pectus excavatum is the most common congenital deformity of the anterior chest wall, in which several ribs and the
sternum grow abnormally. Nowadays, the surgical correction is carried out in children and adults through Nuss technic.
This technic has been shown to be safe with major drivers as cosmesis and the prevention of psychological problems and
social stress. Nowadays, no application is known to predict the cosmetic outcome of the pectus excavatum surgical
correction. Such tool could be used to help the surgeon and the patient in the moment of deciding the need for surgery
correction. This work is a first step to predict postsurgical outcome in pectus excavatum surgery correction. Facing this
goal, it was firstly determined a point cloud of the skin surface along the thoracic wall using Computed Tomography
(before surgical correction) and the Polhemus FastSCAN (after the surgical correction). Then, a surface mesh was
reconstructed from the two point clouds using a Radial Basis Function algorithm for further affine registration between
the meshes. After registration, one studied the surgical correction influence area (SCIA) of the thoracic wall. This SCIA
was used to train, test and validate artificial neural networks in order to predict the surgical outcome of pectus excavatum
correction and to determine the degree of convergence of SCIA in different patients. Often, ANN did not converge to a
satisfactory solution (each patient had its own deformity characteristics), thus invalidating the creation of a mathematical
model capable of estimating, with satisfactory results, the postsurgical outcome.