Mobile Three-Dimensional Screening for Cranial Malformations

ABSTRACTDelayed identification of infant head malformation is causing unnecessary medical complications and societal costs. A critical challenge in the early detection is the absence of tools available to pediatric offices to perform quantitative head shape assessment during well-child visits. Delays in diagnosis limit the opportunity for early, less invasive and effective treatment options. In this Fast-track SBIR project, PediaMetrix Inc. has joined forces with pediatric hospitals and providers to develop and evaluate SoftSpotTM, which is the first mobile digital tool for 3D data collection and analysis of infant cranial malformations at the point-of-care.Head malformations during infancy can be synostotic (i.e., craniosynostosis) or nonsynostotic (such as deformational plagiocephaly and brachycephaly or DPB). Both types of conditions require immediate attention and benefit from early treatment to avoid long-term health complications. The prevalence of DPB increased dramatically in recent years, from 5% to approximately 20%-30%, causing the condition to be called a pediatric epidemic. Craniosynostosis is less common affecting 1 in 2,000 children. To improve the early management of these conditions and to prevent more complex treatment and associated morbidities, it is essential to monitor the growth of the infant head at the point-of-care.To address this unmet clinical need, we will develop and evaluate a mobile digital tool that will enable pediatricians to capture and analyze 3D scans of every infant for the early diagnosis and management of cranial malformations. In the Phase I of this project, we will develop a novel technology to rapidly capture and analyze 3D data of the top of cranium in just seconds. We will use machine learning methods to automatically compute the head shape parameters, including the head circumference which is routinely performed during every child visit, but currently with an outdated and unreliable measuring tape. Our technology will be designed for the general cranial evaluation of all infants during well-child visits. In Phase II, we will develop methods for the 3D reconstruction and analysis of the full cranium from a smartphone. We will also train deep learning models to classify types of craniosynostosis and other cranial conditions and conduct clinical evaluation and user-feasibility studies.The overall mission of PediaMetrix is to provide accurate decision support tools for pediatric health at the point-of-care. This will be achieved through machine learning and quantitative imaging algorithms that in combination with smartphone technological advances will be packaged as mobile digital health solutions accessible to pediatric health providers at any time and location. Successful demonstration of SoftSpot3DTM will lead to a significant reduction of the number of children left with untreated cranial conditions in addition to lowering the associated healthcare costs and social anxiety.