{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T11:14:56Z","timestamp":1789211696199,"version":"build-2803163510"},"reference-count":57,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T00:00:00Z","timestamp":1750982400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"National Institute of Health\/National Institute of Dental Craniofacial Research","award":["NIH\/NIDCR R01-DE032366"],"award-info":[{"award-number":["NIH\/NIDCR R01-DE032366"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The Cleft Palate Craniofacial Journal"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and validate machine learning (ML) models for objective and comprehensive quantification of sagittal craniosynostosis (SCS) severity, enhancing clinical assessment, management, and research.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>A cross-sectional study that combined the analysis of computed tomography (CT) scans and expert ratings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>The study was conducted at a children's hospital and a major computer imaging institution. Our survey collected expert ratings from participating surgeons.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Participants<\/jats:title>\n                    <jats:p>The study included 195 patients with nonsyndromic SCS, 221 patients with nonsyndromic metopic craniosynostosis (CS), and 178 age-matched controls. Fifty-four craniofacial surgeons participated in rating 20 patients head CT scans.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interventions<\/jats:title>\n                    <jats:p>Computed tomography scans for cranial morphology assessment and a radiographic diagnosis of nonsyndromic SCS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Main Outcomes<\/jats:title>\n                    <jats:p>Accuracy of the proposed Sagittal Severity Score (SSS) in predicting expert ratings compared to cephalic index (CI). Secondary outcomes compared Likert ratings with SCS status, the predictive power of skull-based versus skin-based landmarks, and assessments of an unsupervised ML model, the Cranial Morphology Deviation (CMD), as an alternative without ratings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The SSS achieved significantly higher accuracy in predicting expert responses than CI (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      \u2009&lt;\u2009.05). Likert ratings outperformed SCS status in supervising ML models to quantify within-group variations. Skin-based landmarks demonstrated equivalent predictive power as skull landmarks (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      \u2009&lt;\u2009.05, threshold 0.02). The CMD demonstrated a strong correlation with the SSS (Pearson coefficient: 0.92, Spearman coefficient: 0.90,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      \u2009&lt;\u2009.01).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The SSS and CMD can provide accurate, consistent, and comprehensive quantification of SCS severity. Implementing these data-driven ML models can significantly advance CS care through standardized assessments, enhanced precision, and informed surgical planning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/10556656251347366","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T11:22:43Z","timestamp":1751023363000},"page":"1708-1717","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Quantifying Sagittal Craniosynostosis Severity: A Machine Learning Approach With CranioRate"],"prefix":"10.1177","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8745-5996","authenticated-orcid":false,"given":"Wenzheng","family":"Tao","sequence":"first","affiliation":[{"name":"Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6249-5577","authenticated-orcid":false,"given":"Tobi J.","family":"Somorin","sequence":"additional","affiliation":[{"name":"Division of Pediatric Plastic Surgery, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janina","family":"Kueper","sequence":"additional","affiliation":[{"name":"Division of Pediatric Plastic Surgery, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angel","family":"Dixon","sequence":"additional","affiliation":[{"name":"Division of Pediatric Plastic Surgery, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2968-1253","authenticated-orcid":false,"given":"Nicolas","family":"Kass","sequence":"additional","affiliation":[{"name":"Division of Pediatric Plastic Surgery, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nawazish","family":"Khan","sequence":"additional","affiliation":[{"name":"Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krithika","family":"Iyer","sequence":"additional","affiliation":[{"name":"Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jake","family":"Wagoner","sequence":"additional","affiliation":[{"name":"Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashley","family":"Rogers","sequence":"additional","affiliation":[{"name":"Division of Pediatric Plastic Surgery, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ross","family":"Whitaker","sequence":"additional","affiliation":[{"name":"Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shireen","family":"Elhabian","sequence":"additional","affiliation":[{"name":"Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5242-3844","authenticated-orcid":false,"given":"Jesse A.","family":"Goldstein","sequence":"additional","affiliation":[{"name":"Division of Pediatric Plastic Surgery, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,6,27]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"StatPearls","author":"Betances EM","year":"2024","unstructured":"Betances EM, Mendez MD, Das JM. 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