{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T06:34:36Z","timestamp":1790836476890,"version":"4.1.0"},"reference-count":60,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T00:00:00Z","timestamp":1655683200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012151","name":"Sanming Project of Medicine in Shenzhen","doi-asserted-by":"publisher","award":["SZSM201612027"],"award-info":[{"award-number":["SZSM201612027"]}],"id":[{"id":"10.13039\/501100012151","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>The detection of abdominal free fluid or hemoperitoneum can provide critical information for clinical diagnosis and treatment, particularly in emergencies. This study investigates the use of deep learning (DL) for identifying peritoneal free fluid in ultrasonography (US) images of the abdominal cavity, which can help inexperienced physicians or non\u2010professional people in diagnosis. It focuses specifically on first\u2010response scenarios involving focused assessment with sonography for trauma (FAST) technique.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A total of 2985 US images were collected from ascites patients treated from 1 January 2016 to 31 December 2017 at the Shenzhen Second People's Hospital. The data were categorized as Ascites\u20101, Ascites\u20102, or Ascites\u20103, based on the surrounding anatomy. A uniform standard for regions of interest (ROIs) and the lack of obstruction from acoustic shadow was used to classify positive samples. These images were then divided into training (90%) and test (10%) datasets to evaluate the performance of a U\u2010net model, utilizing an encoder\u2013decoder architecture and contracting and expansive paths, developed as part of the study.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Test results produced sensitivity and specificity values of 94.38% and 68.13%, respectively, in the diagnosis of Ascites\u20101 US images, with an average Dice coefficient of 0.65 (standard deviation [SD] = 0.21). Similarly, the sensitivity and specificity for Ascites\u20102 were 97.12% and 86.33%, respectively, with an average Dice coefficient of 0.79 (SD = 0.14). The accuracy and area under the curve (AUC) were 81.25% and 0.76 for Ascites\u20101 and 91.73% and 0.91 for Ascites\u20102.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The results produced by the U\u2010net demonstrate the viability of DL for automated ascites diagnosis. 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