Three-Dimensional Calvarial Growth in Spring-Assisted Cranioplasty for Correction of Sagittal Synostosis

3D Analysis of the Cranial and Facial Shape in Craniosynostosis Patients: A Systematic Review

With increasing interest in 3D photogrammetry, diverse methods have been developed for craniofacial shape analysis in craniosynostosis patients. This review provides an overview of these methods and offers recommendations for future studies. A systematic literature search was used to identify publications on 3D photogrammetry analyses in craniosynostosis patients until August 2023. Inclusion criteria were original research reporting on 3D photogrammetry analyses in patients with craniosynostosis and written in English. Sixty-three publications that had reproducible methods for measuring cranial, forehead, or facial shape were included in the systematic review. Cranial shape changes were commonly assessed using heat maps and curvature analyses. Publications assessing the forehead utilized volumetric measurements, angles, ratios, and mirroring techniques. Mirroring techniques were frequently used to determine facial asymmetry. Although 3D photogrammetry shows promise, methods vary widely between standardized and less conventional measurements. A standardized protocol for the selection and documentation of landmarks, planes, and measurements across the cranium, forehead, and face is essential for consistent clinical and research applications.

Towards a radiation free numerical modelling framework to predict spring assisted correction of scaphocephaly

Sagittal Craniosynostosis (SC) is a congenital craniofacial malformation, involving premature sagittal suture ossification; spring-assisted cranioplasty (SAC) - insertion of metallic distractors for skull reshaping - is an established method for treating SC. Surgical outcomes are predictable using numerical modelling, however published methods rely on computed tomography (CT) scans availability, which are not routinely performed. We investigated a simplified method, based on radiation-free 3D stereophotogrammetry scans.Eight SAC patients (age 5.1 ± 0.4 months) with preoperative CT and 3D stereophotogrammetry scans were included. Information on osteotomies, spring model and post-operative spring opening were recorded. For each patient, two preoperative models (PREOP) were created: i) CT model and ii) S model, created by processing patient specific 3D surface scans using population averaged skin and skull thickness and suture locations. Each model was imported into ANSYS Mechanical (Analysis System Inc., Canonsburg, PA) to simulate spring expansion. Spring expansion and cranial index (CI - skull width over length) at times equivalent to immediate postop (POSTOP) and follow up (FU) were extracted and compared with in-vivo measurements.Overall expansion patterns were very similar for the 2 models at both POSTOP and FU. Both models had comparable outcomes when predicting spring expansion. Spring induced CI increase was similar, with a difference of 1.2%±0.8% for POSTOP and 1.6%±0.6% for FU.This work shows that a simplified model created from the head surface shape yields acceptable results in terms of spring expansion prediction. Further modelling refinements will allow the use of this predictive tool during preoperative planning.

Automated surgical planning in spring-assisted sagittal craniosynostosis correction using finite element analysis and machine learning

Sagittal synostosis is a condition caused by the fused sagittal suture and results in a narrowed skull in infants. Spring-assisted cranioplasty is a correction technique used to expand skulls with sagittal craniosynostosis by placing compressed springs on the skull before six months of age. Proposed methods for surgical planning in spring-assisted sagittal craniosynostosis correction provide information only about the skull anatomy or require iterative finite element simulations. Therefore, the selection of surgical parameters such as spring dimensions and osteotomy sizes may remain unclear and spring-assisted cranioplasty may yield sub-optimal surgical results. The aim of this study is to develop the architectural structure of an automated tool to predict post-operative surgical outcomes in sagittal craniosynostosis correction with spring-assisted cranioplasty using machine learning and finite element analyses. Six different machine learning algorithms were tested using a finite element model which simulated a combination of various mechanical and geometric properties of the calvarium, osteotomy sizes, spring characteristics, and spring implantation positions. Also, a statistical shape model representing an average sagittal craniosynostosis calvarium in 5-month-old patients was used to assess the machine learning algorithms. XGBoost algorithm predicted post-operative cephalic index in spring-assisted sagittal craniosynostosis correction with high accuracy. Finite element simulations confirmed the prediction of the XGBoost algorithm. The presented architectural structure can be used to develop a tool to predict the post-operative cephalic index in spring-assisted cranioplasty in patients with sagittal craniosynostosis can be used to automate surgical planning and improve post-operative surgical outcomes in spring-assisted cranioplasty.