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Automated surgical planning in spring-assisted sagittal craniosynostosis correction using finite element analysis and
1Ulster University, School of Engineering, Belfast, United Kingdom.
Insights
This study introduces an automated tool using machine learning and finite element analysis to predict surgical outcomes for sagittal synostosis (fused sagittal suture) correction. The XGBoost algorithm accurately predicts post-operative cephalic index, improving surgical planning and outcomes.
Area of Science:
- Biomedical Engineering
- Computational Biology
- Pediatric Neurosurgery
Background:
- Sagittal synostosis, caused by premature fusion of the sagittal suture, leads to skull deformities in infants.
- Current surgical planning for spring-assisted cranioplasty lacks precision, potentially resulting in suboptimal outcomes.
- Existing methods focus on skull anatomy or require extensive simulations, hindering clear parameter selection.
Purpose of the Study:
- To develop an automated tool architecture for predicting post-operative outcomes in spring-assisted cranioplasty for sagittal synostosis.
- To integrate machine learning and finite element analysis for enhanced surgical planning.
- To improve the accuracy and efficiency of surgical parameter selection.
Main Methods:
- Developed a finite element model simulating calvarium properties, osteotomy sizes, and spring characteristics.
- Tested six machine learning algorithms, including XGBoost, against the finite element model.
- Utilized a statistical shape model of a sagittal synostosis calvarium for algorithm assessment.
Main Results:
- The XGBoost algorithm demonstrated high accuracy in predicting the post-operative cephalic index.
- Finite element simulations validated the predictions made by the XGBoost algorithm.
- The developed architectural structure provides a robust framework for outcome prediction.
Conclusions:
- The proposed automated tool architecture can significantly improve surgical planning for spring-assisted cranioplasty.
- Accurate prediction of post-operative cephalic index aids in optimizing surgical parameters.
- This approach has the potential to enhance patient outcomes in sagittal craniosynostosis correction.
Abstract:
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.
