Advanced bladder analysis using ultrasonic and electrical impedance tomography with machine learning algorithms

Abstract

Purpose: The primary purpose of the study is to reconstruct the bladder based on the tomographic measurements obtained. Depending on the type of tomography used, two approaches are presented. Methods: In the first presented case, the measurements were collected using ultrasonic tomography, while in the second one, they were gathered with electrical impedance tomography. Deterministic methods and machine learning algorithms, such as Elastic Net, Least Angle Regression, and a Neural Network, were used to obtain the bladder reconstruction. Results: The bladder's position and size can be recognized based on the tomographic measurements of the UST and EIT. Both methods allow for its effective reconstruction. Discussion: The research results are satisfactory, but their effectiveness is debatable. Future studies will focus on comparing and optimizing both solutions regarding reconstruction time.