Craniosynostosis affects close to one in 2000 newborns and causes growth restriction perpendicular to the affected suture. Metopic craniosynostosis is the second most common form of craniosynostosis. The metopic suture is an important sight of cranial growth as the brain rapidly expands in the ?rst year of life. Patients affected by metopic craniosynostosis will present in the ?rst few months of life with varying degrees of narrowing of the forehead and brow, a triangular shaped head, and an abnormal eye position. Surgery is recommended early in childhood to normalize the head shape and expand the restricted skull to prevent complications such as headaches, cognitive impairment, and visual disturbances including blindness. Imaging with computed tomography (CT) is employed to con?rm new diagnoses of metopic craniosynostosis and, together with the physical exam, is used in a descriptive and qualitative manner to assess the degree of head shape abnormality. Several methods have been employed to interpret the information provided in the CT scans to allow surgeons to utilize data for surgical decision making. However, these indices reduce the complex three-dimensional skull dysmorphology into isolated measurements of angles or proportions, require detailed calculations to perform, and no universally accepted standard has emerged so far despite signi?cant research efforts and clinical motivation. In this grant proposal, we aim to increase our understanding of the cranial dysmorphology in patients with metopic craniosynostosis by employing latest results from statistical shape modeling and deep learning. Specif- ically, we will build a statistical shape model of pediatric skulls from CT images of patients with metopic cran- iosynostosis as well as a group of normal controls capturing normal phenotypical shape variations. The distance of a new shape from the normative shape space will represent the proposed Shape Normality Metric (SNM). The SNM will be validated against ratings from experts in the surgical community (current standard of care) who will be asked to assess the dysmorphology of the skulls in our database. To avoid surgeons' subjective bias, we will aggregate their response using statistical methods that compensate for potential individual bias. Finally, to streamline data collection for future research we will develop a head-shape portal that will allow users to upload CT scans of their patients and the system will automatically calculate the SNM. By developing a severity metric that encompasses the entire extent of dysmorphology in metopic craniosyn- ostosis and establishing a head-shape portal, we will improve our understanding of the spectrum of metopic craniosynostosis, aid in pre-operative and surgical decision making, enable future research, and help facilitate longitudinal outcomes assessments and multi-center communication and collaboration.
This grant proposal aims to improve our understanding of the head shape anomaly associated with metopic craniosynostosis by using recent results from statistical shape analysis and deep learning, with the goal of developing an objective metopic cranioynostosis severity scale. Different from previously proposed metrics, our approach evaluates the entire shape as a whole. With this information, surgeons will be able to objectively determine how severely affected their patients are and will be better able to tailor their interventions to the needs of their individual patients. Additionally, surgeons will be able to better communicate with each other and study the effects of surgical intervention on their patients which will improve patient care in the long run.
