ArticleThe Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association2026
Quantifying Sagittal Craniosynostosis Severity: A Machine Learning Approach With CranioRate.
Article in The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- The Use of Machine Learning to Enable Objective Assessment of Surgical Outcomes in Sagittal Craniosynostosis.Plastic and reconstructive surgery. Global open · 2026Article
- Imaging Modalities in Craniosynostosis: A Systematic Review and Proposal of the ARCANA Protocol for Multimodal Radiation-Free Assessment.Diagnostics (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
ObjectiveTo develop and validate machine learning (ML) models for objective and comprehensive quantification of sagittal craniosynostosis (SCS) severity, enhancing clinical assessment, management, and research.DesignA cross-sectional study that combined the analysis of computed tomography (CT) scans and expert ratings.SettingThe study was conducted at a children's hospital and a major computer imaging institution. Our survey collected expert ratings from participating surgeons.ParticipantsThe 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.InterventionsComputed tomography scans for cranial morphology assessment and a radiographic diagnosis of nonsyndromic SCS.Main OutcomesAccuracy 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.ResultsThe SSS achieved significantly higher accuracy in predicting expert responses than CI (
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