Objective: The purpose of this study is to objectively quantify the degree of overcorrection in our current practice and to evaluate longitudinal morphological changes using CranioRateTM, a novel machine learning skull morphology assessment tool. .
Design: Retrospective cohort study across multiple time points.
Setting: Tertiary care children's hospital.
Patients: Patients with preoperative and postoperative CT scans who underwent fronto-orbital advancement (FOA) for metopic craniosynostosis.
Main outcome measures: We evaluated preoperative, postoperative, and two-year follow-up skull morphology using CranioRateTM to generate a Metopic Severity Score (MSS), a measure of degree of metopic dysmorphology, and Cranial Morphology Deviation (CMD) score, a measure of deviation from normal skull morphology.
Results: Fifty-five patients were included, average age at surgery was 1.3 years. Sixteen patients underwent follow-up CT imaging at an average of 3.1 years. Preoperative MSS was 6.3 ± 2.5 (CMD 199.0 ± 39.1), immediate postoperative MSS was -2.0 ± 1.9 (CMD 208.0 ± 27.1), and longitudinal MSS was 1.3 ± 1.1 (CMD 179.8 ± 28.1). MSS approached normal at two-year follow-up (defined as MSS = 0). There was a significant relationship between preoperative MSS and follow-up MSS (R2 = 0.70).
Conclusions: MSS quantifies overcorrection and normalization of head shape, as patients with negative values were less "metopic" than normal postoperatively and approached 0 at 2-year follow-up. CMD worsened postoperatively due to postoperative bony changes associated with surgical displacements following FOA. All patients had similar postoperative metopic dysmorphology, with no significant association with preoperative severity. More severe patients had worse longitudinal dysmorphology, reinforcing that regression to the metopic shape is a postoperative risk which increases with preoperative severity.
Keywords: craniosynostosis; machine learning; metopic; morphology; outcome; overcorrection.