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Recent technological advances in esophageal manometry allowed the definition of new classification methods for the diagnosis of disorders of esophageal motility and the implementation of innovative computational tools for the autonomic, reliable and unbiased detection of different disorders. Computational models can be developed aiming to interpret the mechanical behavior and functionality of the gastrointestinal tract and to summarize the results from clinical measurements, as high-resolution manometry pressure plots, into model parameters. A physiological model was here developed to interpret data from esophageal high-resolution manometry. Such model accounts for parameters related to specific physiological properties of the biological structures involved in the peristaltic mechanism. The identification of model parameters was performed by minimizing the discrepancy between clinical data from high-resolution manometry and model results. Clinical data were collected from both healthy volunteers ( n = 35) and patients with different motor disorders, such as achalasia patterns 1 ( n = 13), 2 ( n = 20) and 3 ( n = 5), distal esophageal spasm ( n = 69), esophago-gastric junction outflow obstruction ( n = 25), nutcracker esophagus ( n = 11) and normal motility ( n = 42). The physiological model that was formulated in this work can properly explain high-resolution manometry data, as confirmed by the evaluation of the coefficient of determination R2 = 0.83 − 0.96. The study finally led to identify the statistical distributions of model parameters for each healthy or pathologic conditions considered, addressing the applicability of the achieved results for the implementation of autonomic diagnosis procedures to support the medical staff during the traditional diagnostic process.

A coupled experimental and computational approach is provided for the identification of the structural behaviour of gastrointestinal regions, accounting for both elastic and visco-elastic properties. The developed procedure is applied to characterize the mechanics of gastrointestinal samples from pig colons. Experimental data about the structural behaviour of colonic segments are provided by inflation tests. Different inflation processes are performed according to progressively increasing top pressure conditions. Each inflation test consists of an air in-flow, according to an almost constant increasing pressure rate, such as 3.5 mmHg/s, up to a prescribed top pressure, which is held constant for about 300 s to allow the development of creep phenomena. Different tests are interspersed by 600 s of rest to allow the recovery of the tissues’ mechanical condition. Data from structural tests are post-processed by a physio-mechanical model in order to identify the mechanical parameters that interpret both the non-linear elastic behaviour of the sample, as the instantaneous pressure–stretch trend, and the time-dependent response, as the stretch increase during the creep processes. The parameters are identified by minimizing the discrepancy between experimental and model results. Different sets of parameters are evaluated for different specimens from different pigs. A statistical analysis is performed to evaluate the distribution of the parameters and to assess the reliability of the experimental and computational activities.