PhD defence T. (Tareq) Abdel Alim

Brief summary: 

This thesis focuses on craniosynostosis, a condition where the bones of a baby’s skull fuse prematurely, potentially leading to abnormal skull shapes and developmental issues. The research emphasizes the use of non-invasive 3D imaging techniques, such as photogrammetry, to assess and analyze cranial and facial structures without the need for radiation exposure. By developing and validating a new software tool called 'CraniumPy,' the study standardizes the way 3D data is processed, making it easier to measure and compare skull shapes across different patients and centers.

The thesis also compares various surgical techniques for treating craniosynostosis, providing insights into which methods are most effective in normalizing skull shape over time. In addition, it explores the potential for conservative (non-surgical) treatments in certain cases, challenging the traditional reliance on surgery.

A significant part of the research introduces advanced computational approaches, including artificial intelligence, to create a more comprehensive understanding of cranial deformities. These methods allow for the analysis of complex shape changes that go beyond what traditional measurements can capture. Overall, this work aims to improve the accuracy and effectiveness of craniosynostosis diagnosis and treatment, ultimately enhancing patient outcomes and quality of life.