Proposal of a Lab Bench for the Unobtrusive Monitoring of the Bladder Fullness with Bioimpedance Measurements

Weakly supervised learning-based 3D bladder reconstruction from 2D ultrasound images for bladder volume measurement

BACKGROUND

Accurate measurement of bladder volume is necessary to maintain the consistency of the patient's anatomy in radiation therapy for pelvic tumors. As the diversity of the bladder shape, traditional methods for bladder volume measurement from 2D ultrasound have been found to produce inaccurate results.

PURPOSE

To improve the accuracy of bladder volume measurement from 2D ultrasound images for patients with pelvic tumors.

METHODS

The bladder ultrasound images from 130 patients with pelvic cancer were collected retrospectively. All data were split into a training set (80 patients), a validation set (20 patients), and a test set (30 patients). A total of 12 transabdominal ultrasound images for one patient were captured by automatically rotating the ultrasonic probe with an angle step of 15°. An incomplete 3D ultrasound volume was synthesized by arranging these 2D ultrasound images in 3D space according to the acquisition angles. With this as input, a weakly supervised learning-based 3D bladder reconstruction neural network model was built to predict the complete 3D bladder. The key point is that we designed a novel loss function, including the supervised loss of bladder segmentation in the ultrasound images at known angles and the compactness loss of the 3D bladder. Bladder volume was calculated by counting the number of voxels belonging to the 3D bladder. The dice similarity coefficient (DSC) was used to evaluate the accuracy of bladder segmentation, and the relative standard deviation (RSD) was used to evaluate the calculation accuracy of bladder volume with that of computed tomography (CT) images as the gold standard.

RESULTS

The results showed that the mean DSC was up to 0.94 and the mean absolute RSD can be reduced to 6.3% when using 12 ultrasound images of one patient. Further, the mean DSC also was up to 0.90 and the mean absolute RSD can be reduced to 9.0% even if only two ultrasound images were used (i.e., the angle step is 90°). Compared with the commercial algorithm in bladder scanners, which has a mean absolute RSD of 13.6%, our proposed method showed a considerably huge improvement.

CONCLUSIONS

The proposed weakly supervised learning-based 3D bladder reconstruction method can greatly improve the accuracy of bladder volume measurement. It has great potential to be used in bladder volume measurement devices in the future.

Catheter-Free Urodynamics Testing: Current Insights and Clinical Potential

Lower urinary tract dysfunction not only interferes with the health-related quality of life of patients but may also lead to acute kidney injury and infections. To assess the bladder, urodynamic studies (UDS) have been implemented but the use of catheters leads to discomfort for the patient. Catheter-free long-term UDS would be useful and a potential solution could be ambulatory wireless devices that communicate via telemetry. Such sensors can detect pressure or volume. Numerous types of potential catheter-free sensors have been proposed for bladder monitoring. Despite substantial innovation in the manufacturing of implantable biomedical electronic systems, such sensors have remained at the laboratory stage due to a number of critical challenges. These challenges primarily concern hermeticity and biocompatibility, sensitivity and artifacts, drift, telemetry, and energy management. Having overcome these challenges, catheter-free ambulatory urodynamic monitoring could combine a synchronized intravesical pressure sensor with a volume analyzer but only the steps of cystometry and volume measurement are currently sufficiently reproducible to simulate UDS results. The measurement of volume by infrared optical sensors, in the form of abdominal patches, appears to be promising and studies are underway to market a telemetric ambulatory urodynamic monitoring system that includes an intravesical pressure sensor. There has been considerable progress in wearable and conformable electronics on many fronts, and continued collaboration between engineers and urologists could quickly overcome current challenges. In addition, to the diagnosis of UDS, such sensors could be useful in the development of a long-term closed-loop neuromodulation system. In this review, we explore the various types of catheter-free bladder sensors, inherent challenges and solutions to overcome these challenges, and the clinical potential of such long-term implantable sensors.

PANI-Based Wearable Electrochemical Sensor for pH Sweat Monitoring

Nowadays, we are assisting in the exceptional growth in research relating to the development of wearable devices for sweat analysis. Sweat is a biofluid that contains useful health information and allows a non-invasive, continuous and comfortable collection. For this reason, it is an excellent biofluid for the detection of different analytes. In this work, electrochemical sensors based on polyaniline thin films deposited on the flexible substrate polyethylene terephthalate coated with indium tin oxide were studied. Polyaniline thin films were abstained by the potentiostatic deposition technique, applying a potential of +2 V vs. SCE for 90 s. To improve the sensor performance, the electronic substrate was modified with reduced graphene oxide, obtained at a constant potential of −0.8 V vs. SCE for 200 s, and then polyaniline thin films were electrodeposited on top of the as-deposited substrate. All samples were characterized by XRD, SEM, EDS, static contact angle and FT-IR/ATR analysis to correlate the physical-chemical features with the performance of the sensors. The obtained electrodes were tested as pH sensors in the range from 2 to 8, showing good behavior, with a sensitivity of 62.3 mV/pH, very close to a Nernstian response, and a reproducibility of 3.8%. Interference tests, in the presence of competing ions, aimed to verify the selectivity, were also performed. Finally, a real sweat sample was collected, and the sweat pH was quantified with both the proposed sensor and a commercial pH meter, showing an excellent concordance.