Fostering equity in precision health through diverse 3D facial data

The promise of precision medicine lies in its ability to provide greater diagnostic accuracy and customized therapy by filtering out patients less likely to benefit from it. Our study focuses on the importance of reducing uncertainty in interpretation of individuals 3D facial data to support more equitable precision medicine applications. The Human Genome Project and subsequent advances in sequencing have led to the creation of vast genetic datasets, predominantly representing individuals of European origin. However, there is a significant underrepresentation of individuals of African, Asian, and Indigenous ancestries.

The study involved 1,218 participants from various genetic ancestries backgrounds, with a focus on the paediatric population of Chinese genetic ancestry. The study subjects underwent 3D facial photogrammetry in outpatient department setting and with the aid of Cliniface software growth curves were obtained to produce reference statistics of 3D facial norms.

The results showed measurable and distinct facial differences in children with Chinese genetic ancestry when compared with other groups representing different genetic ancestries highlighting the need for population diversity and inclusion enrichment in genetic databases. Also, these facial differences and markers are uniquely poised to be correlated in clinic as disease specific digital biomarkers with further investigation and validation in conditions such as hereditary angioedema.

The study underscores the importance of creating larger datasets involving more diverse genetic ancestry groups to enhance the evidence base for advanced and equitable disease diagnosis, treatment monitoring, prognostication and customized drug development.