Machine Learning-Based Identification of Craniosynostosis From Nonsynostotic Cranial Deformities.

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Bibliographic Details
Title: Machine Learning-Based Identification of Craniosynostosis From Nonsynostotic Cranial Deformities.
Authors: Kocabalkanli C; PediaMetrix Inc, Rockville, MD, USA., Wu B; PediaMetrix Inc, Rockville, MD, USA., Blondin M; Department of Plastic and Reconstructive Surgery, Wake Forest University, Winston-Salem, NC, USA., Mantilla-Rivas E; Department of Plastic and Reconstructive Surgery, Children's National Hospital, Washington, DC, USA., Zhang A; Department of Plastic and Reconstructive Surgery, Children's National Hospital, Washington, DC, USA., Keating RF; Department of Neurosurgery, Children's National Hospital, Washington, DC, USA., Rogers G; Department of Plastic and Reconstructive Surgery, Children's National Hospital, Washington, DC, USA., Runyan CM; Department of Plastic and Reconstructive Surgery, Wake Forest University, Winston-Salem, NC, USA., Linguraru MG; PediaMetrix Inc, Rockville, MD, USA., Aalamifar F; PediaMetrix Inc, Rockville, MD, USA., Seifabadi R; PediaMetrix Inc, Rockville, MD, USA.
Source: The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association [Cleft Palate Craniofac J] 2026 Jul 14, pp. 10556656261468119. Date of Electronic Publication: 2026 Jul 14.
Publication Type: Journal Article
Journal Info: Publisher: SAGE Publications in Association with American Cleft Palate-Craniofacial Association Country of Publication: United States NLM ID: 9102566 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1545-1569 (Electronic) Linking ISSN: 10556656 NLM ISO Abbreviation: Cleft Palate Craniofac J Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1545-1569
DOI:10.1177/10556656261468119