ArticleJournal of imaging2022
Comparison of Ultrasound Image Classifier Deep Learning Algorithms 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 10 papers.
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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
10 citing papers in PubMed.
- Detection of Valve Vegetations in Native and Prosthetic Valves using Echocardiographic Radiomics and Deep Learning on Transesophageal Echocardiography Images.Journal of biomedical physics & engineering · 2026Article
- Evaluation of Deep Learning Model Architectures for Point-of-Care Ultrasound Diagnostics.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
- 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
- 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
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Ultrasound imaging is essential in emergency medicine and combat casualty care, oftentimes used as a critical triage tool. However, identifying injuries, such as shrapnel embedded in tissue or a pneumothorax, can be challenging without extensive ultrasonography training, which may not be available in prolonged field care or emergency medicine scenarios. Artificial intelligence can simplify this by automating image interpretation but only if it can be deployed for use in real time. We previously developed a deep learning neural network model specifically designed to identify shrapnel in ultrasound images, termed ShrapML. Here, we expand on that work to further optimize the model and compare its performance to that of conventional models trained on the ImageNet database, such as ResNet50. Through Bayesian optimization, the model's parameters were further refined, resulting in an F1 score of 0.98. We compared the proposed model to four conventional models: DarkNet-19, GoogleNet, MobileNetv2, and SqueezeNet which were down-selected based on speed and testing accuracy. Although MobileNetv2 achieved a higher accuracy than ShrapML, there was a tradeoff between accuracy and speed, with ShrapML being 10× faster than MobileNetv2. In conclusion, real-time deployment of algorithms such as ShrapML can reduce the cognitive load for medical providers in high-stress emergency or miliary medicine scenarios.
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Registered trials
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