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Background: Craniosynostosis, a congenital condition characterized by the premature fusion of cranial sutures, necessitates objective methods for evaluating cranial morphology to enhance patient treatment. Current subjective assessments often lead to inconsistent outcomes. This study introduces a novel, quantitative approach to classify craniosynostosis and measure its severity., Methods: An artificial neural network was trained to classify normocephalic, trigonocephalic, and scaphocephalic head shapes based on a publicly available dataset of synthetic 3D head models. Each 3D model was converted into a low-dimensional shape representation based on the distribution of normal vectors, which served as the input for the neural network, ensuring complete patient anonymity and invariance to geometric size and orientation. Explainable AI methods were utilized to highlight significant features when making predictions. Additionally, the Feature Prominence (FP) score was introduced, a novel metric that captures the prominence of distinct shape characteristics associated with a given class. Its relationship with clinical severity scores was examined using the Spearman Rank Correlation Coefficient., Results: The final model achieved excellent test accuracy in classifying the different cranial shapes from their low-dimensional representation. Attention maps indicated that the network's attention was predominantly directed toward the parietal and temporal regions, as well as toward the region signifying vertex depression in scaphocephaly. In trigonocephaly, features around the temples were most pronounced. The FP score showed a strong positive monotonic relationship with clinical severity scores in both scaphocephalic (ρ = 0.83, p < 0.001) and trigonocephalic (ρ = 0.64, p < 0.001) models. Visual assessments further confirmed that as FP values rose, phenotypic severity became increasingly evident., Conclusion: This study presents an innovative and accessible AI-based method for quantifying cranial shape that mitigates the need for adjustments due to age-specific size variations or differences in the spatial orientation of the 3D images, while ensuring complete patient privacy. The proposed FP score strongly correlates with clinical severity scores and has the potential to aid in clinical decision-making and facilitate multi-center collaborations. Future work will focus on validating the model with larger patient datasets and exploring the potential of the FP score for broader applications. The publicly available source code facilitates easy implementation, aiming to advance craniofacial care and research., (© 2024 The Authors. Journal of Anatomy published by John Wiley & Sons Ltd on behalf of Anatomical Society.)

Craniosynostosis, characterized by premature fusion of one or more cranial sutures, results in a distorted skull shape. Only three studies have assessed facial asymmetry manually in unicoronal synostosis patients. It is therefore important to understand how uni- and bicoronal synostosis affect facial asymmetry with a minimum risk of human bias. An automated algorithm was developed to quantify facial asymmetry from three-dimensional images, generating a mean facial asymmetry (MFA) value in millimeters to reflect the degree of asymmetry. The framework was applied to analyze postoperative 3D images of syndromic patients (N = 35) diagnosed with Muenke syndrome, Saethre-Chotzen syndrome, and TCF12-related craniosynostosis with respect to MFA values from a healthy control group (N = 89). Patients demonstrated substantially higher MFA values than controls: Muenke syndrome (unicoronal 1.74 ± 0.40 mm, bicoronal 0.77 ± 0.21 mm), Saethre-Chotzen syndrome (unicoronal 1.15 ± 0.20 mm, bicoronal 0.69 ± 0.16 mm), and TCF12-related craniosynostosis (unicoronal 1.40 ± 0.51 mm, bicoronal 0.66 ± 0.05 mm), compared with controls (0.49 ± 0.12 mm). Longitudinal analysis identified an increasing MFA trend in unicoronal synostosis patients. Our study revealed higher MFA in syndromic patients with uni- and bicoronal synostosis compared with controls, with the most pronounced MFA in Muenke syndrome patients with unilateral synostosis. Bicoronal synostosis patients demonstrated higher facial asymmetry than expected given the condition's symmetrical presentation., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Abstract: Three-dimensional (3D) stereophotogrammetry is a novel imaging technique that has gained popularity in the medical field as a reliable, non-invasive, and radiation-free imaging modality. It uses optical sensors to acquire multiple 2D images from different angles which are reconstructed into a 3D digital model of the subject's surface. The technique proved to be especially useful in craniofacial applications, where it serves as a tool to overcome the limitations imposed by conventional imaging modalities and subjective evaluation methods. The capability to acquire high-dimensional data in a quick and safe manner and archive them for retrospective longitudinal analyses, provides the field with a methodology to increase the understanding of the morphological development of the cranium, its growth patterns and the effect of different treatments over time.This review describes the role of 3D stereophotogrammetry in the evaluation of craniosynostosis, including reliability studies, current and potential clinical use cases, and practical challenges. Finally, developments within the research field are analyzed by means of bibliometric networks, depicting prominent research topics, authors, and institutions, to stimulate new ideas and collaborations in the field of craniofacial 3D stereophotogrammetry.We anticipate that utilization of this modality's full potential requires a global effort in terms of collaborations, data sharing, standardization, and harmonization. Such developments can facilitate larger studies and novel deep learning methods that can aid in reaching an objective consensus regarding the most effective treatments for patients with craniosynostosis and other craniofacial anomalies, and to increase our understanding of these complex dysmorphologies and associated phenotypes., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2021 by Mutaz B. Habal, MD.)