  The study refers to the application of a type of artificial neural network
called the Self-Organising Map (SOM) for the identification of areas of the
human abdominal wall that perform in a similar mechanical way. The research was
based on data acquired during in vivo tests using the digital image correlation
technique (DIC). The mechanical behaviour of the human abdominal wall was
analysed during changing intra-abdominal pressure. SOM allowed us to study
simultaneously three variables in four time steps. The variables referred to
the principal strains and their directions. SOM classified into clusters all
the abdominal surface data points that behaved similarly in accordance with the
12 variables. The analysis of the clusters provided a better insight into
abdominal wall deformation and its evolution under pressure than when observing
a single mechanical variable. The presented results may provide a better
understanding of the mechanics of the living human abdominal wall. It might be
particularly useful in the designing of surgical meshes for the treatment of
abdominal hernias, which would be mechanically compatible with identified
regions of the human anterior abdominal wall, and possibly open the way for
patient-specific implants.
