{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T12:57:10Z","timestamp":1787921830022,"version":"build-2784847793"},"reference-count":25,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"3","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:sec>\n                    <jats:title>Background and Purpose:<\/jats:title>\n                    <jats:p>Size-specific dose estimates (SSDE) have been introduced into computed tomography (CT) dosimetry to tailor patients\u2019 unique sizes to facilitate accurate CT radiation dose quantification and optimization. The purpose of this study was to develop and validate an automated algorithm for the determination of patient size (effective diameter) and SSDE.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods:<\/jats:title>\n                    <jats:p>A MATLAB platform was used to develop software of algorithms based on image segmentation techniques to automate the calculation of patient size and SSDE. The algorithm was used to automatically estimate the individual size and SSDE of four CT dose index phantoms and 80 CT images of pediatric patients comprising head, thorax, and abdomen scans. For validation, the American Association of Physicists in Medicine (AAPM) manual methods were used to determine the patient\u2019s size and SSDE for the same subjects. The accuracy of the proposed algorithm in size and SSDE calculation was evaluated for agreement with the AAPM\u2019s estimations (manual) using Bland\u2013Altman\u2019s agreement and Pearson\u2019s correlation coefficient. The normalized error, system bias, and limits of agreement (LOA) between methods were derived.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      The results demonstrated good agreement and accuracy between the automated and AAPM\u2019s patient size estimations with an error rate of 1.9% and 0.27% on the patient and phantoms study, respectively. A 1% percentage difference was found between the automated and manual (AAPM) SSDE estimates. A strong degree of correlation was seen with a narrow LOA between methods for clinical study (\n                      <jats:italic toggle=\"yes\">r<\/jats:italic>\n                      &gt; 0.9771) and phantom study (\n                      <jats:italic toggle=\"yes\">r<\/jats:italic>\n                      &gt; 0.9999).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>The proposed automated algorithm provides an accurate estimation of patient size and SSDE with negligible error after validation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/jmp.jmp_26_24","type":"journal-article","created":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T04:00:56Z","timestamp":1727064056000},"page":"456-463","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":1,"title":["A Segmentation-based Automated Calculation of Patient Size and Size-specific Dose Estimates in Pediatric Computed Tomography Scans"],"prefix":"10.4103","volume":"49","author":[{"given":"Muhammad Kabir","family":"Abdulkadir","sequence":"first","affiliation":[{"name":"Department of Biomedical Imaging, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Malaysia"},{"name":"Department of Radiography, Faculty of Basic Clinical Sciences, University of Ilorin, Ilorin, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noor Diyana","family":"Osman","sequence":"additional","affiliation":[{"name":"Department of Biomedical Imaging, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anusha","family":"Achuthan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Computer Science, Universiti Sains Malaysia, Penang, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Radin A.","family":"Nasirudin","sequence":"additional","affiliation":[{"name":"Department of Biomedical Imaging, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Zabidi","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Department of Biomedical Imaging, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noor Hasyima Mat","family":"Zain","sequence":"additional","affiliation":[{"name":"Department of Biomedical Imaging, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ibrahim Lutfi","family":"Shuaib","sequence":"additional","affiliation":[{"name":"Department of Biomedical Imaging, Advanced Medical and Dental Institute, 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