{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T03:50:41Z","timestamp":1762660241386,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T00:00:00Z","timestamp":1653004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"United States Department of Defense (United States Army Medical Research and Development Command)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>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\u2019s 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\u00d7 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.<\/jats:p>","DOI":"10.3390\/jimaging8050140","type":"journal-article","created":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T04:27:22Z","timestamp":1653020842000},"page":"140","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Comparison of Ultrasound Image Classifier Deep Learning Algorithms for Shrapnel Detection"],"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":"Engineering, Technology, and Automation Combat Casualty Care Research Team, United States Army Institute of Surgical Research, Ft. Sam Houston, TX 78234, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0764-519X","authenticated-orcid":false,"given":"Sofia I.","family":"Hernandez-Torres","sequence":"additional","affiliation":[{"name":"Engineering, Technology, and Automation Combat Casualty Care Research Team, United States Army Institute of Surgical Research, Ft. Sam Houston, TX 78234, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0293-4937","authenticated-orcid":false,"given":"Eric J.","family":"Snider","sequence":"additional","affiliation":[{"name":"Engineering, Technology, and Automation Combat Casualty Care Research Team, United States Army Institute of Surgical Research, Ft. Sam Houston, TX 78234, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,20]]},"reference":[{"key":"ref_1","first-page":"187","article-title":"Focussed Assessment Sonograph Trauma (FAST) and CT scan in blunt abdominal trauma: Surgeon\u2019s perspective","volume":"6","author":"Radwan","year":"2006","journal-title":"Afr. Health Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1097\/00005373-199903000-00022","article-title":"Focused Assessment with Sonography for Trauma (FAST): Results from an international consensus conference","volume":"46","author":"Scalea","year":"1999","journal-title":"J. Trauma"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/S0196-0644(97)70347-0","article-title":"Ultrasound for the Detection of Foreign Bodies in Human Tissue","volume":"29","author":"Hill","year":"1997","journal-title":"Ann. Emerg. 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