Machine Learning-Based Identification of Craniosynostosis From Nonsynostotic Cranial Deformities

ObjectiveCraniosynostosis, the premature fusion of cranial sutures, affects 5.2 cases per 10,000 live births and can result in impaired neurodevelopment if not treated early, often necessitating surgery. The absence of efficient screening tools for pediatricians during routine visits contributes to unnecessary referrals, treatment delays, and increased healthcare costs. Through machine learning (ML)-based identification of single-suture craniosynostosis and the affected suture, we aim to assist pediatric providers in the early differentiation of synostotic and nonsynostotic cranial conditions using head volume analysis.Design/MethodsHead volumes were collected via photogrammetry or CT. Volumes represented as 3D points were augmented to address data imbalance, and statistical shape modeling was used alongside Principal Component Analysis and an XGBoost ML model to predict head shapes. Three models were trained and evaluated using 10-fold cross-validation: Model-1 identifying synostosis from nonsynostotic shapes, Model-2 identifying the synostotic suture, Model-3 classifying across synostotic and nonsynostotic head shapes.ParticipantsFive hundred seventy-one subjects (158 normal, 281 deformational plagiocephaly/brachycephaly, 132 single-suture craniosynostosis-including metopic, sagittal, coronal, and lambdoid).ResultsModel-1 identified craniosynostosis from nonsynostotic deformities and healthy controls with an average accuracy of 97.8 ± 1.8%, sensitivity of 98.1 ± 2.3%, and specificity of 96.9 ± 1.1%. Model-2 performed with an average sensitivity of 86.1 ± 12.3% and a specificity of 96.8 ± 2.7%. Model-3 had average sensitivity of 76.8 ± 15.9% and specificity of 97.8 ± 1.7%ConclusionWe developed and evaluated an ML approach that reliably differentiates between synostotic and nonsynostotic head shapes, and distinguishes between types of synostotic sutures, including metopic, sagittal, coronal, and lambdoid.