ArticleSensors (Basel, Switzerland)2024
A Novel Three-Point Localization Method for Bladder Volume Estimation.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- A cross-shaped combined ultrasound array patch for continuous bladder volume monitoring.Photoacoustics · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
The measurement of bladder volume is crucial for the diagnosis and treatment of urinary system diseases. Ultrasound imaging, with its non-invasive, radiation-free, and repeatable scanning capabilities, has become the preferred method for measuring residual urine volume. Nevertheless, it still faces some challenges, including complex imaging methods leading to longer measurement times and lower spatial resolution. Here, we propose a novel three-point localization method that does not require ultrasound imaging to calculate bladder volume. A corresponding triple-element ultrasound probe has been designed based on this method, enabling the ultrasound probe to transmit and receive ultrasound waves in three directions. Furthermore, we utilize the Hilbert Transform algorithm to extract the envelope of the ultrasound signal to enhance the efficiency of bladder volume measurements. The experiment indicates that bladder volume estimation can be completed within 5 s, with a relative error rate of less than 15%. These results demonstrate that this novel three-point localization method offers an effective approach for bladder volume measurement in patients with urological conditions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.