S12-09 SESSION 12: FACIOCRANIOSYNOSTOSIS – PART II A MACHINE LEARNING APPROACH FOR OUTCOME PREDICTION OF MIDFACIAL BIPARTITION DISTRACTION IN APERT PATIENTS

Abstract

Introduction: Apert syndrome is a craniofacial syndrome characterized by deformities including hypertelorism with a negative canthal axis, exorbitism due to shallow orbits and maxillary hypoplasia resulting in a biconcave face in both midsagittal and axial planes. These abnormalities can be corrected surgically by a procedure such as midfacial bipartition distraction. Overall, surgical planning is done by the surgeon’s experience and expertise. Even though these operations seem successful based on this approach, little research has been done on the predicted outcomes of an operation based on statistical methods. This study uses various computer vision and machine learning tools to show a possible outcome of the operation. Methods: This study included pre-, and postoperative CT scans of Apert patients who had midfacial bipartition distraction. Each CT scan was cleaned and manually annotated using a set of 68 3-D sparse annotations. Then, it was aligned to the mean mesh of the Syndromic 3D Morphable Model, which is a powerful statistical model of 3D facial shape by means of Procrustes Analysis. Finally, the mesh was brought into dense correspondence to the mean face by exploiting Gaussian Processes and Non-Rigid iterative closest point (ICP) algorithms. At the end of this process, the mesh could be represented by a set of shape parameters. Results: CT scans of 17 Apert patients could be included. Every mesh was generated using only the shape parameters because every mesh in our training set had been registered. Thus, in order to predict an outcome of the operation, we can calculate a projection matrix from the pre-operative shape parameters to the post-operative parameters. In order to check the generalization ability of our model, we used a cross-validation scheme. Due to the small size of our dataset, we used the Leave-One-Out scheme. This means that we used 16 couples of pre- and postoperative CT scans to train our model and test it in the left-out sample. For the prediction, a linear regression to the shape parameters of the datasets was applied. A successful model for the prediction on the surgical outcome for midfacial bipartition distraction in Apert patients was created. Conclusion: We present a novel method for the prediction of the outcome of Midfacial bipartition distraction in Apert patients. Although the available dataset was small, we show that our model works. With this model applications such as treatment simulation could be created.