Design of a Wireless Medical Capsule for Measuring the Contact Pressure Between a Capsule and the Small Intestine

A wireless medical capsule for measuring the contact pressure between a mobile capsule and the small intestine lumen was developed. Two pressure sensors were used to measure and differentiate the contact pressure and the small intestine intraluminal pressure. After in vitro tests of the capsule, it was surgically placed and tested in the proximal small intestine of a pig model. The capsule successfully gathered and transmitted the pressure data to a receiver outside the body. The measured pressure signals in the animal test were analyzed in the time and frequency domains, and a mathematic model was presented to describe the different factors influencing the contact pressure. A novel signal processing method was applied to isolate the contraction information from the contact pressure. The result shows that the measured contact pressure was 1.08 ± 0.08 kPa, and the small intestine contraction pressure's amplitude and rate were 0.29 ± 0.046 kPa and 12 min−1. Moreover, the amplitudes and rates of pressure from respiration and heartbeat were also estimated. The successful preliminary evaluation of this capsule implies that it could be used in further systematic investigation of small intestine contact pressure on a mobile capsule-shaped bolus.

Quantitative measurements in capsule endoscopy

This review summarizes several approaches for quantitative measurement in capsule endoscopy. Video capsule endoscopy (VCE) typically provides wireless imaging of small bowel. Currently, a variety of quantitative measurements are implemented in commercially available hardware/software. The majority is proprietary and hence undisclosed algorithms. Measurement of amount of luminal contamination allows calculating scores from whole VCE studies. Other scores express the severity of small bowel lesions in Crohn׳s disease or the degree of villous atrophy in celiac disease. Image processing with numerous algorithms of textural and color feature extraction is further in the research focuses for automated image analysis. These tools aim to select single images with relevant lesions as blood, ulcers, polyps and tumors or to omit images showing only luminal contamination. Analysis of motility pattern, size measurement and determination of capsule localization are additional topics. Non-visual wireless capsules transmitting data acquired with specific sensors from the gastrointestinal (GI) tract are available for clinical routine. This includes pH measurement in the esophagus for the diagnosis of acid gastro-esophageal reflux. A wireless motility capsule provides GI motility analysis on the basis of pH, pressure, and temperature measurement. Electromagnetically tracking of another motility capsule allows visualization of motility. However, measurement of substances by GI capsules is of great interest but still at an early stage of development.

Intestinal biomechanics simulator for robotic capsule endoscope validation

This work describes the development and validation of a novel device which simulates important forces experienced by Robotic Capsule Endoscopes (RCE) in vivo in the small intestine. The purpose of the device is to expedite and lower the cost of RCE development. Currently, there is no accurate in vitro test method nor apparatus to validate new RCE designs; therefore, RCEs are tested in vivo at a cost of ∼$1400 per swine test. The authors have developed an in vitro RCE testing device which generates two peristaltic waves to accurately simulate the two biomechanical actions of the human small intestine that are most relevant to RCE locomotion: traction force and contact force. The device was successfully calibrated to match human physiological ranges for traction force (4-40 gf), contact force (80-500 gf) and peristaltic wave propagation speed (0.08-2 cm s(-1)) for a common RCE capsule geometry of 3.5 cm length and 1.5 cm diameter.

Soft material adhesion characterization for in vivo locomotion of robotic capsule endoscopes: Experimental and modeling results

The objective of this work is to validate an experimental method and nondimensional model for characterizing the normal adhesive response between a polyvinyl chloride based synthetic biological tissue substrate and a flat, cylindrical probe with a smooth polydimethylsiloxane (PDMS) surface. The adhesion response is a critical mobility design parameter of a Robotic Capsule Endoscope (RCE) using PDMS treads to provide mobility to travel through the gastrointestinal tract for diagnostic purposes. Three RCE design characteristics were chosen as input parameters for the normal adhesion testing: pre-load, dwell time and separation rate. These parameters relate to the RCE׳s cross sectional dimension, tread length, and tread speed, respectively. An inscribed central composite design (CCD) prescribed 34 different parameter configurations to be tested. The experimental adhesion response curves were nondimensionalized by the maximum stress and total displacement values for each test configuration and a mean nondimensional curve was defined with a maximum relative error of 5.6%. A mathematical model describing the adhesion behavior as a function of the maximum stress and total displacement was developed and verified. A nonlinear regression analysis was done on the maximum stress and total displacement parameters and equations were defined as a function of the RCE design parameters. The nondimensional adhesion model is able to predict the adhesion curve response of any test configuration with a mean R(2) value of 0.995. Eight additional CCD studies were performed to obtain a qualitative understanding of the impact of tread contact area and synthetic material substrate stiffness on the adhesion response. These results suggest that the nondimensionalization technique for analyzing the adhesion data is sufficient for all values of probe radius and substrate stiffness within the bounds tested. This method can now be used for RCE tread design optimization given a set of environmental conditions for device operation.