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

Jenson Jacob, Selim Bozkurt

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
12 Downloads (Pure)

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.
Original languageEnglish
Article numbere0294879
Pages (from-to)1-15
Number of pages15
JournalPLoS ONE
Volume18
Issue number11
DOIs
Publication statusPublished (in print/issue) - 28 Nov 2023

Bibliographical note

Publisher Copyright:
Copyright: © 2023 Jacob, Bozkurt. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Keywords

  • Craniosynostoses - surgery
  • Craniotomy - methods
  • Finite Element Analysis
  • Humans
  • Infant
  • Plastic Surgery Procedures
  • Retrospective Studies
  • Skull - surgery

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