ArticleBioengineering (Basel, Switzerland)2023
A Lightweight Deep Learning Network on a System-on-Chip for Wearable Ultrasound Bladder Volume Measurement Systems: Preliminary Study.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 16 citations in OpenAlex.
- A hybrid deep learning framework for real-time speckle reduction and image enhancement on portable ultrasound systems.Medical physics · 2026Article
- A new single-sagittal-plane deep learning approach for continuous bladder volume monitoring: a feasibility study.Ultrasonography (Seoul, Korea) · 2026Article
- Wearable Ultrasound Devices for Continuous Bladder Monitoring: Emerging Utility and Current Limitations in Neurourology.International neurourology journal · 2026Article
- Intravascular ultrasound imaging with directional synthetic aperture focusing and coherence factor weighting.Ultrasonography (Seoul, Korea) · 2025Article
- Decision support using machine learning for predicting adequate bladder filling in prostate radiotherapy: a feasibility study.Radiological physics and technology · 2025Article
- New Imaging Techniques on the Horizon to Study Overactive and Neurogenic Bladder.Current bladder dysfunction reports · 2025Review
- Artificial Intelligence (AI) Applications for Point of Care Ultrasound (POCUS) in Low-Resource Settings: A Scoping Review.Diagnostics (Basel, Switzerland) · 2024Article
- Wearable-Based Integrated System for In-Home Monitoring and Analysis of Nocturnal Enuresis.Sensors (Basel, Switzerland) · 2024Article
- Detection of spondylosis deformans in thoracolumbar and lumbar lateral X-ray images of dogs using a deep learning network.Frontiers in veterinary science · 2024Article
- Development of a deep learning model for automatic detection of narrowed intervertebral disc space sites in caudal thoracic and lumbar lateral X-ray images of dogs.Frontiers in veterinary science · 2024Article
- Comparative Effectiveness of Two Models of Point-of-Care Ultrasound for Detection of Post-Void Residual Urine during Acute Ischemic Stroke: Preliminary Findings of Real-World Clinical Application.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bladder volume assessments are crucial for managing urinary disorders. Ultrasound imaging (US) is a preferred noninvasive, cost-effective imaging modality for bladder observation and volume measurements. However, the high operator dependency of US is a major challenge due to the difficulty in evaluating ultrasound images without professional expertise. To address this issue, image-based automatic bladder volume estimation methods have been introduced, but most conventional methods require high-complexity computing resources that are not available in point-of-care (POC) settings. Therefore, in this study, a deep learning-based bladder volume measurement system was developed for POC settings using a lightweight convolutional neural network (CNN)-based segmentation model, which was optimized on a low-resource system-on-chip (SoC) to detect and segment the bladder region in ultrasound images in real time. The proposed model achieved high accuracy and robustness and can be executed on the low-resource SoC at 7.93 frames per second, which is 13.44 times faster than the frame rate of a conventional network with negligible accuracy drawbacks (0.004 of the Dice coefficient). The feasibility of the developed lightweight deep learning network was demonstrated using tissue-mimicking phantoms.
Indexed as
Identifiers
What OpenQuestion holds
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.