The broader impact/commercial potential of this I-Corps project relates to the provision of mobile and informed decision support to clinicians and caregivers in various pediatric health conditions that involve body malformations, such as head malformation, facial abnormality, ear deformity, and/or bowed legs. About 2.3 billion people owned a smartphone in 2017, using an average of 2-3 hours on 10 different apps daily. In a recent survey in 2016, 79% of moms were interested in trying or learning about telemedicine for non-emergency medical issues and 40% of those owning smartphones had downloaded at least one health or wellness app. It is estimated that the worldwide market size for mHealth--mobile medical apps-- will reach $11 billion by 2015, a five-fold increase compared to 2017. New parents are particularly involved with mHealth and early detection and continuous monitoring is of utmost importance in the pediatric population. Given the current growth potential of mHealth and its high acceptance by parents, a mobile app is perfectly situated at bridging health gaps in pediatric care. Our approach will use image analysis and artificial intelligence to enable instantaneous decision support, customized recommendations, and low cost scaling up of our technology.
This I-Corps project provides the PediaMetrix team the opportunity to investigate the usability of a new mobile health app, which will enable parents to capture photographs of their infant's head using a smartphone and receive instant analysis of their head shape. We demonstrated that our image-based algorithms accurately measure the cranial index and cranial vault asymmetry index--two clinical parameters used to diagnose and plan treatment for flat head syndrome (FHS). Based on the determined FHS type and severity, the app recommends infant-specific therapeutic re-positioning instructions and then follows up with the caregiver on a regular basis to ensure compliance and progress during therapy. The app will send the information to the infant's electronic health records to be reviewed by pediatricians. We are also investigating cranial shape features with machine learning methods to distinguish between FHS and craniosynostosis, another condition that causes head malformations, but which typically requires surgical treatment. PediaMetrix filed for protection of intellectual property March 2018.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
