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These limitations make population-wide screening for prostate cancer challenging. Machine learning techniques applied to abdominal ultrasound scanning may help alleviate some of these disadvantages. Abdominal ultrasound scans are comparatively low cost and exhibit minimal patient discomfort, and machine learning can be applied to mitigate against the high operator-dependent variability of ultrasound scanning. In this study, a state-of-the-art machine learning model was compared to an expert radiologist and trainee radiologist registrars of varying experience when estimating prostate volume from abdominal ultrasound images, a crucial step in detecting prostate cancer using prostate-specific antigen density. The machine learning model calculated prostatic volume by marking out dimensions of the prolate ellipsoid formula from two orthogonal images of the prostate acquired with abdominal ultrasound scans (which could be conducted by operators with minimal experience in a primary care setting). While both the algorithm and the registrars showed high correlation with the expert (\n                    <jats:inline-formula>\n                      <jats:tex-math>$$r\\ge 0.8$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ) it was found that the model outperformed the trainees in both accuracy (lowest average volume error of\n                    <jats:inline-formula>\n                      <jats:tex-math>$$15\\%$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ) and consistency (lowest IQR of\n                    <jats:inline-formula>\n                      <jats:tex-math>$$19\\%$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    and lowest average volume standard deviation of\n                    <jats:inline-formula>\n                      <jats:tex-math>$$13\\%$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ). The results are promising for the future development of an automated prostate cancer screening workflow using machine learning and abdominal ultrasound scans.\n                  <\/jats:p>","DOI":"10.1038\/s41598-025-10420-4","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T13:51:41Z","timestamp":1759240301000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automating prostate volume acquisition using abdominal ultrasound scans for prostate-specific antigen density calculations"],"prefix":"10.1038","volume":"15","author":[{"given":"Rory Douglas","family":"Bennett","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tristan","family":"Barrett","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikita","family":"Sushentsev","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nimalan","family":"Sanmugalingam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang-Lung","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincent J.","family":"Gnanapragasam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zion Tsz Ho","family":"Tse","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"issue":"1","key":"10420_CR1","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1102\/1470-7330.2010.0004","volume":"10","author":"M Mitterbergera","year":"2010","unstructured":"Mitterbergera, M. et al. 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All methods were performed in accordance with the relevant guidelines and regulations.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}}],"article-number":"33937"}}