ArticleJournal of imaging2022
Using an Ultrasound Tissue Phantom Model for Hybrid Training of Deep Learning Models for Shrapnel Detection.
Article in Journal of imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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.
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Who cites it
9 citing papers in PubMed.
- Smart, automated junctional tourniquets leveraging AI-driven ultrasound guidance.Scientific reports · 2026Article
- A New Wave of Ultrasound Phantoms Using Real Fixed Organs: Birth of the Danny Phantom.Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association · 2025Article
- Evaluation of a Semi-Automated Ultrasound Guidance System for Central Vascular Access.Bioengineering (Basel, Switzerland) · 2024Article
- A Dynamic Ultrasound Phantom with Tissue-Mimicking Mechanical and Acoustic Properties.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Using AI Segmentation Models to Improve Foreign Body Detection and Triage from Ultrasound Images.Bioengineering (Basel, Switzerland) · 2024Article
- Toward Smart, Automated Junctional Tourniquets-AI Models to Interpret Vessel Occlusion at Physiological Pressure Points.Bioengineering (Basel, Switzerland) · 2024Article
- Performance Comparison of Object Detection Networks for Shrapnel Identification in Ultrasound Images.Bioengineering (Basel, Switzerland) · 2023Article
- Using Ultrasound Image Augmentation and Ensemble Predictions to Prevent Machine-Learning Model Overfitting.Diagnostics (Basel, Switzerland) · 2023Article
- An extended focused assessment with sonography in trauma ultrasound tissue-mimicking phantom for developing automated diagnostic technologies.Frontiers in bioengineering and biotechnology · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Tissue phantoms are important for medical research to reduce the use of animal or human tissue when testing or troubleshooting new devices or technology. Development of machine-learning detection tools that rely on large ultrasound imaging data sets can potentially be streamlined with high quality phantoms that closely mimic important features of biological tissue. Here, we demonstrate how an ultrasound-compliant tissue phantom comprised of multiple layers of gelatin to mimic bone, fat, and muscle tissue types can be used for machine-learning training. This tissue phantom has a heterogeneous composition to introduce tissue level complexity and subject variability in the tissue phantom. Various shrapnel types were inserted into the phantom for ultrasound imaging to supplement swine shrapnel image sets captured for applications such as deep learning algorithms. With a previously developed shrapnel detection algorithm, blind swine test image accuracy reached more than 95% accuracy when training was comprised of 75% tissue phantom images, with the rest being swine images. For comparison, a conventional MobileNetv2 deep learning model was trained with the same training image set and achieved over 90% accuracy in swine predictions. Overall, the tissue phantom demonstrated high performance for developing deep learning models for ultrasound image classification.
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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.