Machine Learning in Metopic Craniosynostosis: Does Phenotypic Severity Predict Long-Term Esthetic Outcome?

Area of Science:

• Craniofacial surgery
• Pediatric plastic surgery
• Medical imaging analysis

Background:

• Few longitudinal studies assess preoperative metopic craniosynostosis severity's impact on long-term esthetic outcomes.
• Metopic craniosynostosis requires surgical correction, impacting facial development.
• Understanding severity-outcome relationships is crucial for patient management.

Purpose of the Study:

• To evaluate the relationship between preoperative metopic severity and long-term esthetic outcomes.
• To assess the predictive value of objective measures like interfrontal angle (IFA) and CranioRate.
• To correlate quantitative severity ratings with clinical esthetic assessments.

Main Methods:

• Retrospective review of metopic craniosynostosis patients undergoing bifrontal orbital advancement (2012-2017).
• Analysis of preoperative CT scans for IFA, metopic severity score (MSS), and cranial morphology deviation (CMD) using CranioRate.
• Blinded 3-rater esthetic grading of clinical photos assessing Whitaker score and specific facial irregularities.

Main Results:

• Preoperative MSS independently predicted visible facial irregularities (OR=2.18, P=0.024).
• Preoperative IFA significantly predicted Whitaker score, with acute angles correlating to worse outcomes (OR=0.928, P=0.928).
• Significant negative correlations found between MSS/IFA and age at CT/surgery.

Conclusions:

• Increased preoperative metopic craniosynostosis severity is linked to poorer long-term esthetic results.
• Objective measures of preoperative severity effectively predict long-term esthetic outcomes.
• Quantitative analysis aids in predicting and managing esthetic dysmorphology.