ArticleThe Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association2023
"Validation of Artificial Intelligence Severity Assessment in 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 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 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
- Non-syndromic craniosynostosis.Nature reviews. Disease primers · 2025Review
- Artificial Intelligence Applications in Pediatric Craniofacial Surgery.Diagnostics (Basel, Switzerland) · 2025Review
- 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
- Review
- 3D Photography to Quantify the Severity of Metopic Craniosynostosis.The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association · 2023Article
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Authors and funding
10 authors.
Funding
Abstract
objectiveSeveral severity metrics have been developed for metopic craniosynostosis, including a recent machine learning-derived algorithm. This study assessed the diagnostic concordance between machine learning and previously published severity indices.
designPreoperative computed tomography (CT) scans of patients who underwent surgical correction of metopic craniosynostosis were quantitatively analyzed for severity. Each scan was manually measured to derive manual severity scores and also received a scaled metopic severity score (MSS) assigned by the machine learning algorithm. Regression analysis was used to correlate manually captured measurements to MSS. ROC analysis was performed for each severity metric and were compared to how accurately they distinguished cases of metopic synostosis from controls.
resultsIn total, 194 CT scans were analyzed, 167 with metopic synostosis and 27 controls. The mean scaled MSS for the patients with metopic was 6.18 ± 2.53 compared to 0.60 ± 1.25 for controls. Multivariable regression analyses yielded an R-square of 0.66, with significant manual measurements of endocranial bifrontal angle (EBA) (P = 0.023), posterior angle of the anterior cranial fossa (p < 0.001), temporal depression angle (P = 0.042), age (P < 0.001), biparietal distance (P < 0.001), interdacryon distance (P = 0.033), and orbital width (P < 0.001). ROC analysis demonstrated a high diagnostic value of the MSS (AUC = 0.96, P < 0.001), which was comparable to other validated indices including the adjusted EBA (AUC = 0.98), EBA (AUC = 0.97), and biparietal/bitemporal ratio (AUC = 0.95).
conclusionsThe machine learning algorithm offers an objective assessment of morphologic severity that provides a reliable composite impression of severity. The generated score is comparable to other severity indices in ability to distinguish cases of metopic synostosis from controls.
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