ArticleThe Journal of craniofacial surgery
Quantifying the Severity of Metopic Craniosynostosis: A Pilot Study Application of Machine Learning in Craniofacial Surgery.
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 35 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it, 73 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
- Distraction in anterior plagiocephaly: technique, risks, and long-term results.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2026Article
- 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
- Neurodevelopmental outcomes in children with craniosynostosis: a retrospective cross-sectional analysis.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2026Article
- Imaging Modalities in Craniosynostosis: A Systematic Review and Proposal of the ARCANA Protocol for Multimodal Radiation-Free Assessment.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial Intelligence Applications in Pediatric Craniofacial Surgery.Diagnostics (Basel, Switzerland) · 2025Review
- Normscan: open-source Python software to create average models from CT scans.International journal of computer assisted radiology and surgery · 2025Article
- Quantifying dysmorphologies of the neurocranium using artificial neural networks.Journal of anatomy · 2024Article
- Radiographic severity is associated with worse executive function in metopic craniosynostosis.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2024Article
- OPTIMIZATION-DRIVEN STATISTICAL MODELS OF ANATOMIES USING RADIAL BASIS FUNCTION SHAPE REPRESENTATION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- DeepSSM: A blueprint for image-to-shape deep learning models.Medical image analysis · 2024Article
- Pocket Predictors: Are Smartphones the Future of Artificial Intelligence in Plastic Surgery.Plastic surgery (Oakville, Ont.) · 2023Article
- Particle-Based Shape Modeling for Arbitrary Regions-of-Interest.Shape in medical imaging : International Workshop, ShapeMI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ShapeMI (Workshop) (2023 : Vancouver, B.C.) · 2023Article
- Geometric learning and statistical modeling for surgical outcomes evaluation in craniosynostosis using 3D photogrammetry.Computer methods and programs in biomedicine · 2023Article
- 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
- ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images.Shape in medical imaging : International Workshop, ShapeMI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ShapeMI (Workshop) (2023 : Vancouver, B.C.) · 2023Article
- Image2SSM: Reimagining Statistical Shape Models from Images with Radial Basis Functions.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- 3D Photography to Quantify the Severity of Metopic Craniosynostosis.The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association · 2023Article
- The Use of Machine Learning for Predicting Complications of Free-Flap Head and Neck Reconstruction.Annals of surgical oncology · 2023Review
- "Validation of Artificial Intelligence Severity Assessment in Metopic Craniosynostosis".The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
The standard for diagnosing metopic craniosynostosis (CS) utilizes computed tomography (CT) imaging and physical exam, but there is no standardized method for determining disease severity. Previous studies using interfrontal angles have evaluated differences in specific skull landmarks; however, these measurements are difficult to readily ascertain in clinical practice and fail to assess the complete skull contour. This pilot project employs machine learning algorithms to combine statistical shape information with expert ratings to generate a novel objective method of measuring the severity of metopic CS.Expert ratings of normal and metopic skull CT images were collected. Skull-shape analysis was conducted using ShapeWorks software. Machine-learning was used to combine the expert ratings with our shape analysis model to predict the severity of metopic CS using CT images. Our model was then compared to the gold standard using interfrontal angles.Seventeen metopic skull CT images of patients 5 to 15 months old were assigned a severity by 18 craniofacial surgeons, and 65 nonaffected controls were included with a 0 severity. Our model accurately correlated the level of skull deformity with severity (P < 0.10) and predicted the severity of metopic CS more often than models using interfrontal angles (χ = 5.46, P = 0.019).This is the first study that combines shape information with expert ratings to generate an objective measure of severity for metopic CS. This method may help clinicians easily quantify the severity and perform robust longitudinal assessments of the condition.
Indexed as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.