Modular eFAST tissue phantom for AI-based ultrasound triage

Ultrasound (US) imaging is the primary choice for diagnosing and triaging patients in the battlefield as well as emergency medicine due to ease of portability and low-power requirements. Interpretation and acquisition of ultrasound images can be challenging and requires personnel with specialized training. Incorporating artificial intelligence (AI) can enhance the imaging process while improving diagnostic accuracy. To accomplish this goal, we have developed a full torso tissue-mimicking phantom for simulating US image capture at each site of the extended-focused assessment with sonography for trauma (eFAST) exam and is suitable for developing AI guidance and classification models. The US images taken from the phantom were used to train AI models for detection of specific anatomical features and injury state diagnosis. The tissue-mimicking phantom successfully simulated full thoracic motion as well as modular injuries at each scan site. AI models trained from the tissue phantom were able to achieve IOU's greater than 0.80 and accuracy of 71.5% on blind inferences. In summary, the tissue mimicking phantom is a reliable tool for acquiring eFAST images for training AI models. Furthermore, the tissue phantom could be implemented for training personnel on ultrasound examination techniques as well as developing image acquisition automation techniques.

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Publication Details

Journal
BioMedical Engineering OnLine
Published
2025-10-09
DOI
https://doi.org/10.1186/s12938-025-01448-8
Primary Topic
Ultrasound in Clinical Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Modular eFAST tissue phantom for AI-based ultrasound triage

Sofía I. Hernández Torres, Thomas C. Winter, Carlos Amador‐Bedolla, Eric J. Snider et al.
BioMedical Engineering OnLine
Ultrasound in Clinical Applications
article

Modular eFAST tissue phantom for AI-based ultrasound triage

Sofía I. Hernández Torres, Thomas C. Winter, Carlos Amador‐Bedolla, Eric J. Snider, Krysta-Lynn Amezcua, Rachel Gathright, Isiah Mejia
article en

Abstract

Ultrasound (US) imaging is the primary choice for diagnosing and triaging patients in the battlefield as well as emergency medicine due to ease of portability and low-power requirements. Interpretation and acquisition of ultrasound images can be challenging and requires personnel with specialized training. Incorporating artificial intelligence (AI) can enhance the imaging process while improving diagnostic accuracy. To accomplish this goal, we have developed a full torso tissue-mimicking phantom for simulating US image capture at each site of the extended-focused assessment with sonography for trauma (eFAST) exam and is suitable for developing AI guidance and classification models. The US images taken from the phantom were used to train AI models for detection of specific anatomical features and injury state diagnosis. The tissue-mimicking phantom successfully simulated full thoracic motion as well as modular injuries at each scan site. AI models trained from the tissue phantom were able to achieve IOU's greater than 0.80 and accuracy of 71.5% on blind inferences. In summary, the tissue mimicking phantom is a reliable tool for acquiring eFAST images for training AI models. Furthermore, the tissue phantom could be implemented for training personnel on ultrasound examination techniques as well as developing image acquisition automation techniques.

BioMedical Engineering OnLineVol. 24(1)
United States Army Institute of Surgical Research (US)
U.S. Department of Energy, Oak Ridge Associated Universities, National Institutes of Health, Oak Ridge Institute for Science and Education, U.S. Army Medical Research and Development Command
Openalex Percentile: Top 68%
Ultrasound in Clinical Applications
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