CranioRate: An imaging-based, deep-phenotyping analysis toolset, repository, and online clinician interface for craniosynostosis

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PROJECT SUMMARY Title: CranioRate™: An image-based, deep-phenotyping analysis toolset, repository, and online clinician interface for craniosynostosis. The purpose of this research grant application is to build on the advanced machine learning (ML) tool developed as part of a pilot study (R21EB026061) that objectively quantifies cranial dysmorphology, or deep phenotypes, in patients with metopic craniosynostosis (MC). Abnormal cranial suture fusion (craniosynostosis) occurs in one of every 2500 infants born in the US, resulting in disrupted regional skull growth and an increased risk of elevated intracranial pressure, neurocognitive impairment and visual disturbances including blindness. Impaired skull growth along the fused suture and subsequent growth compensation in other areas of the skull lead to predictable head shape patterns in patients with craniosynostosis; surgery is recommended early in childhood to restore normal head shape and prevent neurocognitive sequelae. In our work to date, our team has developed an ML/statistical shape analysis system utilizing computed tomography (CT) scans of patients with MC. We have demonstrated that our deep ML algorithm is as effective as expert clinician ratings in assessing severity and more effective than standard craniometric tools. We have expanded our processes to include the analysis of 3D photography to increase accessibility and study post-operative head shape. Thus far, we have demonstrated equivalent severity ratings between 3D photographs and CT scans when obtained on the same patients. Finally, we have designed and implemented an online head shape portal (CranioRate™) that automates preprocessing and analysis such that users can upload their own patient images, where the resulting data contributes to clinical patient care as well as research endeavors. To date, over 30 clinicians have contributed almost 400 MC CT scans to our portal, making our metopic craniosynostosis imaging collection the largest reported. In the proposed work, we will refine our processing pipeline and shape analysis technologies, while expanding our capabilities to encompass all forms of craniosynostosis and a wider array of imaging modalities, and improve the functionality and security of the CranioRate™ portal. To pursue these aims, we will bring together a robust consortium of collaborators to contribute imaging and clinical data, empanel a scientific advisory board to ensure data integrity, and establish an open access human craniosynostosis image bank to allow further collaborations through FaceBase. Specific goals for the current project are to: 1) Further develop a set of robust, general morphological quantification technologies and cloud-based implementations that result in effective scientific and clinical tools; 2) Establish a shared-access, well-curated dataset that will leverage our multicenter collaborative network and partnership with FaceBase; 3) Identify and collect pertinent clinical data to extend the utility of our shape analysis tool and shared-access database. The results of this study will significantly improve the understanding of the phenotypic variation in patients with craniosynostosis and will pave the way for more substantial imaging-based research in this understudied population.