ArticleScientific reports2022
An image classification deep-learning algorithm for shrapnel detection from ultrasound images.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Pattern and structural detection in grayscale images through the application of quantile graphs in higher-dimensional spaces.Scientific reports · 2025Article
- Development of Deep Learning Models for Real-Time Thoracic Ultrasound Image Interpretation.Journal of imaging · 2025Article
- Automated elbow ultrasound image recognition: a two-stage deep learning system via Swin Transformer.Quantitative imaging in medicine and surgery · 2025Article
- Evaluation of Deep Learning Model Architectures for Point-of-Care Ultrasound Diagnostics.Bioengineering (Basel, Switzerland) · 2024Article
- Design and testing of ultrasound probe adapters for a robotic imaging platform.Scientific reports · 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
- Deep learning models for interpretation of point of care ultrasound in military working dogs.Frontiers in veterinary science · 2024Article
- Leveraging AI in Postgraduate Medical Education for Rapid Skill Acquisition in Ultrasound-Guided Procedural Techniques.Journal of imaging · 2023Article
- Lightweight image steganalysis with block-wise pruning.Scientific reports · 2023Article
- Performance Comparison of Object Detection Networks for Shrapnel Identification in Ultrasound Images.Bioengineering (Basel, Switzerland) · 2023Article
- Research on tire crack detection using image deep learning method.Scientific reports · 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
- Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes.Frontiers in medicine · 2023Article
- Using an Ultrasound Tissue Phantom Model for Hybrid Training of Deep Learning Models for Shrapnel Detection.Journal of imaging · 2022Article
- Evaluation of an Object Detection Algorithm for Shrapnel and Development of a Triage Tool to Determine Injury Severity.Journal of imaging · 2022Article
- Training Ultrasound Image Classification Deep-Learning Algorithms for Pneumothorax Detection Using a Synthetic Tissue Phantom Apparatus.Journal of imaging · 2022Article
- Development of a Modular Tissue Phantom for Evaluating Vascular Access Devices.Bioengineering (Basel, Switzerland) · 2022Article
- Comparison of Ultrasound Image Classifier Deep Learning Algorithms for Shrapnel Detection.Journal of imaging · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ultrasound imaging is essential for non-invasively diagnosing injuries where advanced diagnostics may not be possible. However, image interpretation remains a challenge as proper expertise may not be available. In response, artificial intelligence algorithms are being investigated to automate image analysis and diagnosis. Here, we highlight an image classification convolutional neural network for detecting shrapnel in ultrasound images. As an initial application, different shrapnel types and sizes were embedded first in a tissue mimicking phantom and then in swine thigh tissue. The algorithm architecture was optimized stepwise by minimizing validation loss and maximizing F1 score. The final algorithm design trained on tissue phantom image sets had an F1 score of 0.95 and an area under the ROC curve of 0.95. It maintained higher than a 90% accuracy for each of 8 shrapnel types. When trained only on swine image sets, the optimized algorithm format had even higher metrics: F1 and area under the ROC curve of 0.99. Overall, the algorithm developed resulted in strong classification accuracy for both the tissue phantom and animal tissue. This framework can be applied to other trauma relevant imaging applications such as internal bleeding to further simplify trauma medicine when resources and image interpretation are scarce.
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