Training Ultrasound Image Classification Deep-Learning Algorithms for Pneumothorax Detection Using a Synthetic Tissue Phantom Apparatus

Ultrasound (US) imaging is a critical tool in emergency and military medicine because of its portability and immediate nature. However, proper image interpretation requires skill, limiting its utility in remote applications for conditions such as pneumothorax (PTX) which requires rapid intervention....

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Vydáno v:Journal of imaging Ročník 8; číslo 9; s. 249
Hlavní autoři: Boice, Emily N., Hernandez Torres, Sofia I., Knowlton, Zechariah J., Berard, David, Gonzalez, Jose M., Avital, Guy, Snider, Eric J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 11.09.2022
MDPI
Témata:
Algorithms
Artificial intelligence
automation
Classification
Datasets
Deep learning
Emergency medical care
Emergency medical services
Gelatin
Image acquisition
Image analysis
Image classification
Lungs
Machine learning
model development
Pneumothorax
porcine
Respiration
Shrapnel
Swine
Three dimensional printing
tissue phantom
Training
Trouble shooting
Troubleshooting
Ultrasonic imaging
ultrasound
X-rays
United States > US
ISSN:2313-433X, 2313-433X
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Popis
Shrnutí:Ultrasound (US) imaging is a critical tool in emergency and military medicine because of its portability and immediate nature. However, proper image interpretation requires skill, limiting its utility in remote applications for conditions such as pneumothorax (PTX) which requires rapid intervention. Artificial intelligence has the potential to automate ultrasound image analysis for various pathophysiological conditions. Training models require large data sets and a means of troubleshooting in real-time for ultrasound integration deployment, and they also require large animal models or clinical testing. Here, we detail the development of a dynamic synthetic tissue phantom model for PTX and its use in training image classification algorithms. The model comprises a synthetic gelatin phantom cast in a custom 3D-printed rib mold and a lung mimicking phantom. When compared to PTX images acquired in swine, images from the phantom were similar in both PTX negative and positive mimicking scenarios. We then used a deep learning image classification algorithm, which we previously developed for shrapnel detection, to accurately predict the presence of PTX in swine images by only training on phantom image sets, highlighting the utility for a tissue phantom for AI applications.
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USDOE
ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging8090249