Urinary bladder volume reconstruction based on bioimpedance measurements: ex vivo and in vivo validation through implanted patch and needle electrodes

• Published in: IEEE sensors journal Vol. 23; no. 24; p. 1
• Main Authors: Semproni, Federica, Iacovacci, Veronica, Musco, Stefania, Menciassi, Arianna
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bioimpedance; Bladder; Bladder volume monitoring; Electrodes; Errors; Impedance; Implantable biorobotic organs; Implantable sensors; Ion concentration; Monitoring; Polynomials; Reconstruction; Sensors; Volume measurement
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2023.3330978

Restoring bladder sensation in patients with bladder dysfunctions by performing urinary volume monitoring is an ambitious goal. The bioimpedance technique has shown promising results in wearable solutions but long-term validation and implantable systems are not available, yet. In this work, we propose to implant commercial bioimpedance sensors on bladder walls to perform bladder volume estimation. Two commercial sensor types (Ag/AgCl patch and needle electrodes) were selected to this purpose. Injected current frequency of 1.337 MHz and electrodes pair on the same face of the bladder allowed to correlate the changes in impedance with increasing volumes. Two volume reconstruction algorithms have been proposed, based on the direct correlation between bioimpedance readings and bladder volume (Algorithm A) or bioimpedance readings and inter-electrode distance (Algorithm B, bladder shape approximated to a sphere). For both algorithms, a better fit with a second-degree fitting polynomial was obtained. Algorithm A obtained lower estimation errors with an average of 20.35% and 21.98% (volumes greater than 150 ml) for patch and needle electrodes, respectively. The variations in ions concentration led to a slight deterioration of volume estimation, however the presence of tissues surrounding the bladder did not influence the performance. Although Algorithm B was less affected by the experimental conditions and inter-subject biological variability, it featured higher estimation errors. In vivo validation on suine model showed average errors of 29.36% (volumes greater than 100 ml), demonstrating the potential of the proposed solution and paving the way towards a novel implantable volume monitoring system.