Analysis of biomechanical parameters extracted from anorectal manometry of fecally-continent and incontinent patients

ANORECTAL MANOMETRY STANDARD OF A BRAZILIAN POPULATION AT PRODUCTIVE AGE WITHOUT PELVIC FLOOR DISORDERS: A PROSPECTIVE VOLUNTEERED STUDY

Background:

Due to the lack of normal standards of anorectal manometry in Brazil, data used are subject to normality patterns described at different nationalities.

Aim:

To determine the values and range of the parameters evaluated at anorectal manometry in people, at productive age, without pelvic floor disorders comparing the parameters obtained between male and female.

Methods:

Prospective analysis of clinical data, such as gender, age, race, body mass index (BMI) and anorectal manometry, of volunteers from a Brazilian university reference in pelvic floor disorders.

Results:

Forty patients were included, with a mean age of 45.5 years in males and 37.2 females (p=0.43). According to male and female, respectively in mmHg, resting pressures were similar (78.28 vs. 63.51, p=0.40); squeeze pressures (153.89 vs. 79.78, p=0.007) and total squeeze pressures (231.27 vs. 145.63, p=0.002). Men presented significantly higher values of anorectal squeeze pressures, as well as the average length of the functional anal canal (2.85 cm in male vs. 2.45 cm in female, p=0.003).

Conclusions:

Normal sphincter pressure levels in Brazilians differ from those used until now as normal literature standards. Male gender has higher external anal sphincter tonus as compared to female, in addition a greater extension of the functional anal canal

A Novel Nonlinear Parameter Estimation Method of Soft Tissues

The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values. To provide highly precise data for estimating nonlinear parameters, the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine (WCFM_SVM). Secondly, a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues, using the substitution parameters of Young's modulus and Poisson's ratio to avoid solving complicated nonlinear problems. To improve the robustness of our model and avoid poor local minima, the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally, a self-adapting Levenberg-Marquardt (LM) algorithm was presented, which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM_SVM model was less than 0.03 Newton, resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5 mm, demonstrating that our nonlinear parameters are precise.