{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T12:31:40Z","timestamp":1779366700429,"version":"3.53.0"},"reference-count":43,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,9,11]],"date-time":"2022-09-11T00:00:00Z","timestamp":1662854400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"U.S. Army Medical Research and Development Command"},{"name":"National Institutes of Health (NIH)"},{"name":"U.S. Department of Energy, Oak Ridge Institute for Science and Education"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/jimaging8090249","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T01:44:03Z","timestamp":1663033443000},"page":"249","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Training Ultrasound Image Classification Deep-Learning Algorithms for Pneumothorax Detection Using a Synthetic Tissue Phantom Apparatus"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7180-2842","authenticated-orcid":false,"given":"Emily N.","family":"Boice","sequence":"first","affiliation":[{"name":"U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0764-519X","authenticated-orcid":false,"given":"Sofia I.","family":"Hernandez Torres","sequence":"additional","affiliation":[{"name":"U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zechariah J.","family":"Knowlton","sequence":"additional","affiliation":[{"name":"U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2286-3846","authenticated-orcid":false,"given":"David","family":"Berard","sequence":"additional","affiliation":[{"name":"U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4325-409X","authenticated-orcid":false,"given":"Jose M.","family":"Gonzalez","sequence":"additional","affiliation":[{"name":"U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9337-185X","authenticated-orcid":false,"given":"Guy","family":"Avital","sequence":"additional","affiliation":[{"name":"U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA"},{"name":"Trauma & Combat Medicine Branch, Surgeon General\u2019s Headquarters, Israel Defense Forces, Ramat-Gan 52620, Israel"},{"name":"Division of Anesthesia, Intensive Care & Pain Management, Tel-Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 64239, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0293-4937","authenticated-orcid":false,"given":"Eric J.","family":"Snider","sequence":"additional","affiliation":[{"name":"U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,11]]},"reference":[{"key":"ref_1","unstructured":"American College of Emergency Physicians (1990). Council Resolution on Ultrasound. ACEP News, 11."},{"key":"ref_2","unstructured":"Society for Academic Emergency Medicine (1991). Ultrasound Position Statement. SAEM Newsletter, summer."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1186\/s13054-016-1399-x","article-title":"Ultrasonography in the Emergency Department","volume":"20","author":"Whitson","year":"2016","journal-title":"Crit. Care"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"American College of Emergency Physicians (2001). ACEP Emergency Ultrasound Guidelines\u20132001. Ann. Emerg. Med., 38, 470\u2013481.","DOI":"10.1016\/S0196-0644(01)70030-3"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"American College of Emergency Physicians (2009). Emergency Ultrasound Guidelines. Ann. Emerg. Med., 53, 550\u2013570.","DOI":"10.1016\/j.annemergmed.2008.12.013"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1378\/chest.11-0131","article-title":"Test Characteristics of Ultrasonography for the Detection of Pneumothorax: A Systematic Review and Meta-Analysis","volume":"141","author":"Alrajhi","year":"2012","journal-title":"Chest"},{"key":"ref_7","first-page":"29","article-title":"Diagnostic Accuracy of Chest Ultrasonography versus Chest Radiography for Identification of Pneumothorax: A Systematic Review and Meta-Analysis","volume":"13","author":"Ebrahimi","year":"2014","journal-title":"Tanaffos"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2681","DOI":"10.1002\/jum.14629","article-title":"Does the Addition of M-Mode to B-Mode Ultrasound Increase the Accuracy of Identification of Lung Sliding in Traumatic Pneumothoraces?","volume":"37","author":"Avila","year":"2018","journal-title":"J. Ultrasound Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"R112","DOI":"10.1186\/cc5004","article-title":"Rapid Detection of Pneumothorax by Ultrasonography in Patients with Multiple Trauma","volume":"10","author":"Zhang","year":"2006","journal-title":"Crit. Care"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/978-3-030-33128-3_3","article-title":"Deep Learning for Pulmonary Image Analysis: Classification, Detection, and Segmentation","volume":"Volume 1213","author":"Lee","year":"2020","journal-title":"Deep Learning in Medical Image Analysis"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5341","DOI":"10.1007\/s00330-019-06130-x","article-title":"Application of Deep Learning\u2013Based Computer-Aided Detection System: Detecting Pneumothorax on Chest Radiograph after Biopsy","volume":"29","author":"Park","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_12","first-page":"409","article-title":"Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)","volume":"8","author":"Alakwaa","year":"2017","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7714","DOI":"10.1088\/1361-6560\/aa82ec","article-title":"A Deep Learning Framework for Supporting the Classification of Breast Lesions in Ultrasound Images","volume":"62","author":"Han","year":"2017","journal-title":"Phys. Med. Biol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2493","DOI":"10.1109\/TUFFC.2020.2993779","article-title":"Deep Learning to Obtain Simultaneous Image and Segmentation Outputs From a Single Input of Raw Ultrasound Channel Data","volume":"67","author":"Nair","year":"2020","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Diaz-Escobar, J., Ord\u00f3\u00f1ez-Guill\u00e9n, N.E., Villarreal-Reyes, S., Galaviz-Mosqueda, A., Kober, V., Rivera-Rodriguez, R., and Rizk, J.E.L. (2021). Deep-Learning Based Detection of COVID-19 Using Lung Ultrasound Imagery. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0255886"},{"key":"ref_16","unstructured":"Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., and Wells, W. (2016). Real-Time Standard Scan Plane Detection and Localisation in Fetal Ultrasound Using Fully Convolutional Neural Networks. Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2016, Springer International Publishing."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.eng.2018.11.020","article-title":"Deep Learning in Medical Ultrasound Analysis: A Review","volume":"5","author":"Liu","year":"2019","journal-title":"Engineering"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1016\/j.rapm.2005.08.007","article-title":"Ultrasound Phantom for Hands-on Practice","volume":"30","author":"Xu","year":"2005","journal-title":"Reg. Anesth. Pain Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1136\/emermed-2011-200264","article-title":"Homemade Ultrasound Phantom for Teaching Identification of Superficial Soft Tissue Abscess","volume":"29","author":"Lo","year":"2012","journal-title":"Emerg. Med. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1002\/jcu.1870170617","article-title":"Preparation of a Homemade Ultrasound Biopsy Phantom","volume":"17","author":"Mp","year":"1989","journal-title":"J. Clin. Ultrasound: JCU"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1177\/1129729820961941","article-title":"A Comparison of Homemade Vascular Access Ultrasound Phantom Models for Peripheral Intravenous Catheter Insertion","volume":"22","author":"Selame","year":"2021","journal-title":"J. Vasc. Access"},{"key":"ref_22","unstructured":"Costa-Felix, R., Machado, J.C., and Alvarenga, A.V. (2019). Lung Ultrasonography Phantom for Lung-Pulse Sign Simulation. XXVI Brazilian Congress on Biomedical Engineering, Springer Singapore."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/s11739-010-0347-z","article-title":"Detection of Spontaneous Pneumothorax with Chest Ultrasound in the Emergency Department","volume":"5","author":"Barillari","year":"2010","journal-title":"Intern. Emerg. Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1111\/j.1553-2712.2012.01349.x","article-title":"A Porcine Pneumothorax Model for Teaching Ultrasound Diagnostics","volume":"19","author":"Oveland","year":"2012","journal-title":"Acad. Emerg. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1002\/mrd.22489","article-title":"The ImageJ Ecosystem: An Open Platform for Biomedical Image Analysis","volume":"82","author":"Schindelin","year":"2015","journal-title":"Mol. Reprod. Dev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1038\/nmeth.2019","article-title":"Fiji: An Open-Source Platform for Biological-Image Analysis","volume":"9","author":"Schindelin","year":"2012","journal-title":"Nat. Methods"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"8427","DOI":"10.1038\/s41598-022-12367-2","article-title":"An Image Classification Deep-Learning Algorithm for Shrapnel Detection from Ultrasound Images","volume":"12","author":"Snider","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Boice, E.N., Hernandez-Torres, S.I., and Snider, E.J. (2022). Comparison of Ultrasound Image Classifier Deep Learning Algorithms for Shrapnel Detection. J. Imaging, 8.","DOI":"10.3390\/jimaging8050140"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"76","DOI":"10.4103\/0974-2700.93116","article-title":"Sonographic Diagnosis of Pneumothorax","volume":"5","author":"Husain","year":"2012","journal-title":"J. Emerg. Trauma Shock"},{"key":"ref_30","first-page":"114","article-title":"Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-Rays","volume":"43","author":"Kumar","year":"2020","journal-title":"IRBM"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Born, J., Wiedemann, N., Cossio, M., Buhre, C., Br\u00e4ndle, G., Leidermann, K., Aujayeb, A., Moor, M., Rieck, B., and Borgwardt, K. (2021). Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis. Appl. Sci., 11.","DOI":"10.3390\/app11020672"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2312","DOI":"10.1109\/TUFFC.2020.3002249","article-title":"Automated Lung Ultrasound B-Line Assessment Using a Deep Learning Algorithm","volume":"67","author":"Baloescu","year":"2020","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"104742","DOI":"10.1016\/j.compbiomed.2021.104742","article-title":"Deep Learning and Lung Ultrasound for COVID-19 Pneumonia Detection and Severity Classification","volume":"136","author":"Secco","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e13695","DOI":"10.1002\/acm2.13695","article-title":"Deep Learning for Emergency Ascites Diagnosis Using Ultrasonography Images","volume":"23","author":"Lin","year":"2022","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/j.chest.2017.10.019","article-title":"The Use of M-Mode Ultrasonography to Differentiate the Causes of B Lines","volume":"153","author":"Singh","year":"2018","journal-title":"Chest"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1053\/j.jvca.2019.11.051","article-title":"Tracheal, Lung, and Diaphragmatic Applications of M-Mode Ultrasonography in Anesthesiology and Critical Care","volume":"35","author":"Prada","year":"2021","journal-title":"J. Cardiothorac. Vasc. Anesth."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1038\/sj.sc.3101889","article-title":"Diaphragmatic Paralysis: The Use of M Mode Ultrasound for Diagnosis in Adults","volume":"44","author":"Lloyd","year":"2006","journal-title":"Spinal Cord"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1213\/ANE.0b013e3181d5e4d8","article-title":"An Evaluation of Diaphragmatic Movement by M-Mode Sonography as a Predictor of Pulmonary Dysfunction after Upper Abdominal Surgery","volume":"110","author":"Kim","year":"2010","journal-title":"Anesth. Analg."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_40","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201322). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1109\/JPROC.2015.2494218","article-title":"Taking the Human Out of the Loop: A Review of Bayesian Optimization","volume":"104","author":"Shahriari","year":"2016","journal-title":"Proc. IEEE"},{"key":"ref_43","unstructured":"Pereira, F., Burges, C.J., Bottou, L., and Weinberger, K.Q. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems, Curran Associates, Inc."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/8\/9\/249\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:29:37Z","timestamp":1760142577000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/8\/9\/249"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,11]]},"references-count":43,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["jimaging8090249"],"URL":"https:\/\/doi.org\/10.3390\/jimaging8090249","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,11]]}}}