{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T05:27:38Z","timestamp":1788326858987,"version":"build-2803163510"},"reference-count":24,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T00:00:00Z","timestamp":1774569600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T00:00:00Z","timestamp":1774569600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Medical Radiation Sci"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) is increasingly adopted in digital radiography to enhance workflow efficiency, standardisation and support for radiation dose optimisation. Auto\u2010thorax collimation (ATC) has been introduced to automate field size selection in chest X\u2010ray (CXR) imaging; however, its clinical performance and impact on collimation consistency in routine chest X\u2010ray practice require further evaluation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A single\u2010centre retrospective study analysed 400 posteroanterior (PA) erect CXRs sourced from the Picture Archiving and Communication System (PACS) and local image archives. Collimation size was measured in both the superior\u2013inferior and lateral dimensions. An additional, 200 erect CXRs were collected to assess the repeat rate due to collimation errors. Statistical analysis was performed using two\u2010sample\n                      <jats:italic>t<\/jats:italic>\n                      \u2010tests for collimation measurements and a two\u2010proportion\n                      <jats:italic>Z<\/jats:italic>\n                      \u2010test for repeat rates. The coefficient of variation (CV) was calculated to quantify the variability in collimation field size for each operator across both ATC and manual collimation methods.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      ATC demonstrated tighter inferior and left lateral collimation than manual collimation (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05), while manual collimation showed tighter superior collimation. Collimation consistency was greater with ATC, as evidenced by lower CV values across operators. Repeat rates were comparable between ATC (7%) and manual collimation (8%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI\u2010based collimation provides collimation results similar to manual performance, with improved standardisation and reproducibility. Its consistent output suggests potential benefits in high\u2010turnover environments, enhancing workflow efficiency whilst optimising radiation safety.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jmrs.70088","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T10:35:49Z","timestamp":1774607749000},"page":"300-308","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Radiation Dose Optimisation Through Artificial Intelligence (\n                    <scp>AI<\/scp>\n                    )\u2010based Auto\u2010Thorax Collimation"],"prefix":"10.1002","volume":"73","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2645-6965","authenticated-orcid":false,"given":"Derek (Yoon\u2010Sang)","family":"Lee","sequence":"first","affiliation":[{"name":"Monash Imaging Monash Health, Casey Hospital  Berwick Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6131-188X","authenticated-orcid":false,"given":"Nicole","family":"Chan","sequence":"additional","affiliation":[{"name":"Monash Imaging Monash Health, Casey Hospital  Berwick Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0943-2320","authenticated-orcid":false,"given":"Richard","family":"Knight","sequence":"additional","affiliation":[{"name":"Monash Imaging Monash Health, Casey Hospital  Berwick Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Hayre","sequence":"additional","affiliation":[{"name":"Monash Imaging Monash Health, Casey Hospital  Berwick Victoria Australia"},{"name":"Monash University  Clayton Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,27]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13030460"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278\u2010025\u201001628\u2010x"},{"key":"e_1_2_10_4_1","volume-title":"Siemens Healthineers Uses Artificial Intelligence to Take X\u2010Ray Radiography to a New Level [Press Release]","author":"Siemens Healthineers","year":"2020"},{"key":"e_1_2_10_5_1","doi-asserted-by":"crossref","unstructured":"D.Mairh\u00f6fer M.Laufer L.Berkel et\u00a0al. \u201cAI\u2010Based Collimation Optimisation for X\u2010Ray Imaging Using Time\u2010of\u2010Flight Cameras \u201dIn: Proceedings of ESANN 2024 (2024) https:\/\/www.esann.org\/sites\/default\/files\/proceedings\/2024\/ES2024\u2010147.pdf.","DOI":"10.14428\/esann\/2024.ES2024-147"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmrs.630"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/rpd\/ncab155"},{"key":"e_1_2_10_8_1","volume-title":"Ethical Considerations in Quality Assurance and Evaluation Activities [Internet]","author":"National Health and Medical Research Council","year":"2014"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330\u2010002\u20101350\u20101"},{"key":"e_1_2_10_10_1","volume-title":"YSIO X.pree: How Intelligent Imaging Ensures an Efficient and Patient\u2010Centered Radiography Workflow [White Paper]","author":"Rasche A.","year":"2023"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-58868-1_9"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmir.2018.11.001"},{"key":"e_1_2_10_13_1","volume-title":"The Two\u2010Sample t\u2010Test. Introduction to Statistics [Internet]","author":"JMP"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.13.11657"},{"key":"e_1_2_10_15_1","unstructured":"Salimetrics \u201cCalculating Inter\u2010 and Intra\u2010Assay Coefficients of Variability [Internet]\u201d(2016) https:\/\/salimetrics.com\/calculating\u2010inter\u2010and\u2010intra\u2010assay\u2010coefficients\u2010of\u2010variability\/."},{"issue":"4","key":"e_1_2_10_16_1","first-page":"819","article-title":"Influence of Feature Calculating Parameters on the Reproducibility of Radiomic Features","volume":"10","author":"Li Y.","year":"2020","journal-title":"Quantitative Imaging in Medicine and Surgery"},{"key":"e_1_2_10_17_1","volume-title":"9.1 \u2013 Two Independent Proportions: Hypothesis Testing. STAT 200 Elementary Statistics","author":"Penn State STAT Online","year":"2026"},{"key":"e_1_2_10_18_1","first-page":"2705","article-title":"Sample Size Calculation 2. Comparison of Two Independent Proportions","volume":"22","author":"Kim H. Y.","year":"2016","journal-title":"Medical Science Monitor"},{"key":"e_1_2_10_19_1","volume-title":"Sample Size Determination: A Practical Guide for Health Researchers","author":"Lwanga S. K.","year":"1991"},{"issue":"2","key":"e_1_2_10_20_1","first-page":"1","article-title":"The 2007 Recommendations of the International Commission on Radiological Protection. ICRP Publication 103","volume":"37","author":"International Commission on Radiological Protection","year":"2007","journal-title":"Annals of the ICRP"},{"key":"e_1_2_10_21_1","unstructured":"Federal Register \u201cLinear\u2010No\u2010Threshold Model and Standards for Protection Against Radiation\u201d(2021)."},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.4103\/1115-3474.198108"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmrs.648"},{"key":"e_1_2_10_24_1","first-page":"561","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018. Lecture Notes in Computer Science 11071","author":"S\u00e9n\u00e9gas J.","year":"2018"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radi.2024.05.002"}],"container-title":["Journal of Medical Radiation Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jmrs.70088","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/jmrs.70088","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jmrs.70088","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T05:09:04Z","timestamp":1788325744000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/jmrs.70088"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,27]]},"references-count":24,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["10.1002\/jmrs.70088"],"URL":"https:\/\/doi.org\/10.1002\/jmrs.70088","archive":["Portico"],"relation":{},"ISSN":["2051-3895","2051-3909"],"issn-type":[{"value":"2051-3895","type":"print"},{"value":"2051-3909","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,27]]},"assertion":[{"value":"2025-12-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-17","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}