Craniometrics in Metopic Craniosynostosis: A Review of Craniometric Parameters and the Emergence of Machine Learning Models
CT-based metrics remain standard, but ML models using advanced imaging offer radiation-free, objective, and reproducible assessments. Future efforts should emphasize multicenter data sharing, standardized variables, longitudinal imaging, and integration of genotypic and neuropsychological data.
Authors
- Brooke French (ORCID: https://orcid.org/0000-0002-8810-8790)
- Gavin R. Hoffman (ORCID: https://orcid.org/0000-0002-5142-6955)
- David Y. Khechoyan (ORCID: https://orcid.org/0000-0003-2362-9664)
- Connor Elkhill (ORCID: https://orcid.org/0000-0001-8753-9575)
- David Woodbridge Mathes (ORCID: https://orcid.org/0000-0003-4388-1373)
- David Le (ORCID: https://orcid.org/0000-0002-3472-9815)
- Antonio Porras
- Jason W. Yu
- Thalia Le
- Phuong D. Nguyen
Institutions
- Colorado School of Public Health (US)
- Children's Hospital Colorado (US)
- University of Colorado Anschutz Medical Campus (US)
- University of Colorado Denver (US)
Publication Details
- Journal
- Journal of Craniofacial Surgery
- Published
- 2026-01-13
- DOI
- https://doi.org/10.1097/scs.0000000000012342
- Primary Topic
- Craniofacial Disorders and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00