Self-organising maps in the analysis of strains of human abdominal wall to identify areas of similar mechanical behaviour.

Abstract

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 behave in a similar mechanical way. The research is based on data acquired during in vivo tests using the digital image correlation technique (DIC). The mechanical behaviour of the human abdominal wall is analysed during changing intra-abdominal pressure. SOM allow to study simultaneously three variables in four time/load steps. The variables refer to the principal strains and their directions. SOM classifies all the abdominal surface data points into clusters that behave similarly in accordance with the 12 variables. The analysis of the clusters provides 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 when selecting proper implants as well as for the design 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 solutions.