ArticleThe Journal of craniofacial surgery
Machine Learning in Metopic Craniosynostosis: Does Phenotypic Severity Predict Long-Term Esthetic Outcome?
Article in The Journal of craniofacial surgery. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Machine learning applications in craniosynostosis diagnosis and treatment prediction: a systematic review.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2024Pooled it
- Quantifying Sagittal Craniosynostosis Severity: A Machine Learning Approach With CranioRate.The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association · 2026Article
- Artificial Intelligence Applications in Pediatric Craniofacial Surgery.Diagnostics (Basel, Switzerland) · 2025Review
- Technical evolution of pediatric neurosurgery: craniosynostosis from 1972 to 2023 and beyond.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2023Review
- Automated surgical planning in spring-assisted sagittal craniosynostosis correction using finite element analysis and machine learning.PloS one · 2023Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundThere have been few longitudinal studies assessing the effect of preoperative phenotypic severity on long-term esthetic outcomes in metopic craniosynostosis. This study evaluates the relationship between metopic severity and long-term esthetic outcomes using interfrontal angle (IFA) and CranioRate, a novel metopic synostosis severity measure.
methodsPatients with metopic craniosynostosis who underwent bifrontal orbital advancement and remodeling between 2012 and 2017 were reviewed. Preoperative computed tomography head scans were analyzed for IFA and CranioRate, a machine learning algorithm which generates quantitative severity ratings including metopic severity score (MSS) and cranial morphology deviation (CMD). Long-term esthetic outcomes were assessed by craniofacial surgeons using blinded 3-rater esthetic grading of clinical photos. Raters assessed Whitaker score and the presence of temporal hollowing, lateral orbital retrusion, frontal bone irregularities and/or "any visible irregularities."
resultsPreoperative scans were performed at a mean age of 7.7±3.4 months, with average MSS of 6/10, CMD of 200/300, and IFA of 116.8±13.8 degrees. Patients underwent bifrontal orbital advancement and remodeling at mean 9.9±3.1 months. The average time from operation to esthetic assessment was 5.4±1.0 years. Pearson correlation revealed a significant negative correlation between MSS and age at computed tomography ( r =-0.451, P =0.004) and IFA ( r =-0.371, P =0.034) and between IFA and age at surgery ( r =-0.383, P =0.018). In multinomial logistic regression, preoperative MSS was the only independent predictor of visible irregularities (odds ratio=2.18, B =0.780, P =0.024) and preoperative IFA alone significantly predicted Whitaker score, with more acute IFA predicting worse Whitaker score (odds ratio=0.928, B =-0.074, P =0.928).
conclusionsMore severe preoperative phenotypes of metopic craniosynostosis were associated with worse esthetic dysmorphology. Objective measures of preoperative metopic severity predicted long-term esthetic outcomes.
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