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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Abstract	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.
AbstractList	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. 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.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.
Author	Snider, Eric J. Knowlton, Zechariah J. Avital, Guy Boice, Emily N. Hernandez Torres, Sofia I. Gonzalez, Jose M. Berard, David
AuthorAffiliation	1 U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA 3 Division of Anesthesia, Intensive Care & Pain Management, Tel-Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 64239, Israel 2 Trauma & Combat Medicine Branch, Surgeon General’s Headquarters, Israel Defense Forces, Ramat-Gan 52620, Israel
AuthorAffiliation_xml	– name: 3 Division of Anesthesia, Intensive Care & Pain Management, Tel-Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 64239, Israel – name: 1 U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA – name: 2 Trauma & Combat Medicine Branch, Surgeon General’s Headquarters, Israel Defense Forces, Ramat-Gan 52620, Israel
Author_xml	– sequence: 1 givenname: Emily N. orcidid: 0000-0001-7180-2842 surname: Boice fullname: Boice, Emily N. – sequence: 2 givenname: Sofia I. orcidid: 0000-0002-0764-519X surname: Hernandez Torres fullname: Hernandez Torres, Sofia I. – sequence: 3 givenname: Zechariah J. surname: Knowlton fullname: Knowlton, Zechariah J. – sequence: 4 givenname: David orcidid: 0000-0003-2286-3846 surname: Berard fullname: Berard, David – sequence: 5 givenname: Jose M. orcidid: 0000-0002-4325-409X surname: Gonzalez fullname: Gonzalez, Jose M. – sequence: 6 givenname: Guy orcidid: 0000-0002-9337-185X surname: Avital fullname: Avital, Guy – sequence: 7 givenname: Eric J. orcidid: 0000-0002-0293-4937 surname: Snider fullname: Snider, Eric J.
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StartPage	249
SubjectTerms	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
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Title	Training Ultrasound Image Classification Deep-Learning Algorithms for Pneumothorax Detection Using a Synthetic Tissue Phantom Apparatus
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