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A combined Machine Learning and Finite Element Modelling tool for the surgical planning of craniosynostosis correction

arXiv:2506.03202 - [arXiv,PDF]
Authors
  • Name
    Itxasne Ant\'unez S\'aenz
  • Name
    Ane Alberdi Aramendi
  • Name
    David Dunaway
  • Name
    Juling Ong
  • Name
    Lara Deli\`ege
  • Name
    Amparo S\'aenz
  • Name
    Anita Ahmadi Birjandi
  • Name
    Noor UI Owase Jeelani
  • Name
    Silvia Schievano
  • Name
    Alessandro Borghi
  • Affiliation
  • Affiliation
    University of the Basque Country
  • Affiliation
    University of California, Berkeley
  • Affiliation
    National University of Singapore
  • Affiliation
    University of Tehran
  • Affiliation
    University of Karachi
  • Affiliation
    University of Milan
  • Affiliation
    Université de Liège
Craniosynostosis is a medical condition that affects the growth of babies’ heads, caused by an early fusion of cranial sutures. In recent decades, surgical treatments for craniosynostosis have significantly improved, leading to reduced invasiveness, faster recovery, and less blood loss. At Great Ormond Street Hospital (GOSH), the main surgical treatment for patients diagnosed with sagittal craniosynostosis (SC) is spring assisted cranioplasty (SAC). This procedure involves a 15x15 mm2 osteotomy, where two springs are inserted to induce distraction. Despite the numerous advantages of this surgical technique for patients, the outcome remains unpredictable due to the lack of efficient preoperative planning tools. The surgeon’s experience and the baby’s age are currently relied upon to determine the osteotomy location and spring selection. Previous tools for predicting the surgical outcome of SC relied on finite element modeling (FEM), which involved computed tomography (CT) imaging and required engineering expertise and lengthy calculations. The main goal of this research is to develop a real-time prediction tool for the surgical outcome of patients, eliminating the need for CT scans to minimise radiation exposure during preoperative planning. The proposed methodology involves creating personalised synthetic skulls based on three-dimensional (3D) photographs, incorporating population average values of suture location, skull thickness, and soft tissue properties. A machine learning (ML) surrogate model is employed to achieve the desired surgical outcome. The resulting multi-output support vector regressor model achieves a R2 metric of 0.95 and MSE and MAE below 0.13. Furthermore, in the future, this model could not only simulate various surgical scenarios but also provide optimal parameters for achieving a maximum cranial index (CI).