{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T17:56:54Z","timestamp":1781459814095,"version":"3.54.1"},"reference-count":19,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T00:00:00Z","timestamp":1781049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T00:00:00Z","timestamp":1781049600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The Cleft Palate Craniofacial Journal"],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop a predictive model for estimating cortical bone thickness at any maxillary location in patients with unilateral cleft lip and palate (UCLP).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>Retrospective cross-sectional cohort study with machine learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>University hospital, Department of Oral and Maxillofacial Radiology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Patients, Participants<\/jats:title>\n                    <jats:p>\n                      Fifty patients with non-syndromic UCLP and 50 age- and gender-matched controls (total\n                      <jats:italic toggle=\"yes\">N<\/jats:italic>\n                      \u2009=\u2009100). Mean age: UCLP 14.8\u2009\u00b1\u20095.1 years; controls 16.5\u2009\u00b1\u20094.6 years. Age range: 8 to 27 years.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interventions<\/jats:title>\n                    <jats:p>All participants underwent CBCT (0.2\u2005mm voxel, 14\u2009\u00d7\u200916\u2005cm FOV). Cortical bone thickness was measured at 4, 6, 8, 10, and 12\u2005mm from the alveolar crest across six interradicular regions, yielding 4500 data points.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Main Outcome Measure(s)<\/jats:title>\n                    <jats:p>\n                      Cortical bone thickness; model performance metrics (\n                      <jats:italic toggle=\"yes\">R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      , RMSE); feature importance; accuracy by region and depth.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Random Forest regression explained 75% of variance (patient-level cross-validation:\n                      <jats:italic toggle=\"yes\">R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.75, RMSE\u2009=\u20090.102\u2005mm). Anatomical region was the strongest predictor (0.38), followed by depth (0.27), age (0.18), cleft status (0.12), and side (0.05). Best accuracy in posterior regions (\n                      <jats:italic toggle=\"yes\">R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.84) and at 12\u2005mm depth (\n                      <jats:italic toggle=\"yes\">R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.81); lowest in anterior regions (\n                      <jats:italic toggle=\"yes\">R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.72) and at 4\u2005mm depth (\n                      <jats:italic toggle=\"yes\">R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.69). The model identified high-risk zones with 89% sensitivity (AUC\u2009=\u20090.94). External validation has not yet been performed; the author welcomes collaboration with other centers for independent testing.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This model provides accurate, patient-specific predictions of cortical bone thickness in UCLP patients, assisting in pretreatment risk assessment, surgical planning, and patient counseling.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/10556656261456214","type":"journal-article","created":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T09:01:48Z","timestamp":1781082108000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Predictive Model for Bone Availability in Patients With Unilateral Cleft Lip and Palate: A Machine Learning Approach"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9800-7195","authenticated-orcid":false,"given":"Murat Mert","family":"Atapek","sequence":"first","affiliation":[{"name":"Yeditepe University Faculty of Dentistry"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,6,10]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14222555"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00056-020-00276-w"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1055\/s-0042-1756137"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.2319\/061614-438.1"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1177\/1055665617726999"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41407-022-0926-7"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajodo.2006.02.021"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajodo.2007.01.017"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.5624\/isd.2013.43.2.85"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-019-0681-4"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1111\/ocr.12477"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1093\/ejo\/cjaa045"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1177\/00220345241235606"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajodo.2009.07.014"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajodo.2011.06.021"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.4041\/kjod.2013.43.2.83"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajodo.2008.02.020"}],"container-title":["The Cleft Palate Craniofacial Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/10556656261456214","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/10556656261456214","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/10556656261456214","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T17:08:50Z","timestamp":1781456930000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/10556656261456214"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,10]]},"references-count":19,"alternative-id":["10.1177\/10556656261456214"],"URL":"https:\/\/doi.org\/10.1177\/10556656261456214","relation":{},"ISSN":["1055-6656","1545-1569"],"issn-type":[{"value":"1055-6656","type":"print"},{"value":"1545-1569","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,10]]},"article-number":"10556656261456214"}}