ArticleThe Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association2023
3D Photography to Quantify the Severity of Metopic Craniosynostosis.
Article in The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Imaging Modalities in Craniosynostosis: A Systematic Review and Proposal of the ARCANA Protocol for Multimodal Radiation-Free Assessment.Diagnostics (Basel, Switzerland) · 2025Review
- Non-syndromic craniosynostosis.Nature reviews. Disease primers · 2025Review
- Artificial Intelligence Applications in Pediatric Craniofacial Surgery.Diagnostics (Basel, Switzerland) · 2025Review
- SHAPE: A visual computing pipeline for interactive landmarking of 3D photograms and patient reporting for assessing craniosynostosis.Computers & graphics · 2024Article
- Geometric Morphometric Study on Distinguishing Metopic Craniosynostosis from Metopic Ridging.Plastic and reconstructive surgery. Global open · 2024Article
- DeepSSM: A blueprint for image-to-shape deep learning models.Medical image analysis · 2024Article
- Geometric learning and statistical modeling for surgical outcomes evaluation in craniosynostosis using 3D photogrammetry.Computer methods and programs in biomedicine · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
This study aims to determine the utility of 3D photography for evaluating the severity of metopic craniosynostosis (MCS) using a validated, supervised machine learning (ML) algorithm. This single-center retrospective cohort study included patients who were evaluated at our tertiary care center for MCS from 2016 to 2020 and underwent both head CT and 3D photography within a 2-month period. The analysis method builds on our previously established ML algorithm for evaluating MCS severity using skull shape from CT scans. In this study, we regress the model to analyze 3D photographs and correlate the severity scores from both imaging modalities. 14 patients met inclusion criteria, 64.3% male (n = 9). The mean age in years at 3D photography and CT imaging was 0.97 and 0.94, respectively. Ten patient images were obtained preoperatively, and 4 patients did not require surgery. The severity prediction of the ML algorithm correlates closely when comparing the 3D photographs to CT bone data (Spearman correlation coefficient [SCC] r = 0.75; Pearson correlation coefficient [PCC] r = 0.82). The results of this study show that 3D photography is a valid alternative to CT for evaluation of head shape in MCS. Its use will provide an objective, quantifiable means of assessing outcomes in a rigorous manner while decreasing radiation exposure in this patient population.
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