Automatic analysis should provide more repeatable results, when the structures actually do manifest themselves on the surface of the back. It is questionable in case of scapulae and vertebra prominens, where surface shape varies with changes in the positions of arms and posture in general. If this condition is not fulfilled, the result will be very coarsely approximated and will raise even bigger errors. However, the authors state that the recognition process should be guided mainly by the shape of the back surface, putting as little emphasis on the first approximation as possible. This may be regarded as a more radical approach to the analysis process, because recognition will fail if anomalies are present. On the other hand, if more influence will be given to the mean model, which represents an average patient (i.e., healthy in a normal population), cases with an actual deformation present will be regarded as closer to the average and the magnitude of the deformation will be diminished. It is therefore a choice between a false positive and a false negative, which should be considered by specialists utilizing the system [Fig. 12(b)]. In this study a balanced compromise was presented, which may provide guidelines for future uses of the developed algorithms. It has to be noted that the algorithm was only tested on a small dataset and should be validated with a larger amount of measurement data, with diverse distributions of patients.