Humanitarian Facial Recognition for Rare Craniofacial Malformations.

ABSTRACT

Children with congenital disorders are unfortunate collateral victims of wars and natural disasters. Improved diagnosis could help organize targeted medical support campaigns. Patient identification is a key issue in the management of life-threatening conditions in extreme situations, such as in oncology or for diabetes, and can be challenging when diagnosis requires biological or radiological investigations. Dysmorphology is a central element of diagnosis for craniofacial malformations, with high sensibility and specificity. Massive amounts of public data, including facial pictures circulate daily on news channels and social media, offering unique possibilities for automatic diagnosis based on facial recognition. Furthermore, AI-based algorithms assessing facial features are currently being developed to decrease diagnostic delays. Here, as a case study, we used a facial recognition algorithm trained on a large photographic database to assess an online picture of a family of refugees. Our aim was to evaluate the relevance of using an academic tool on a journalistic picture and discuss its potential application to large-scale screening in humanitarian perspectives. This group picture featured one child with signs of Apert syndrome, a rare condition with risks of severe complications in cases of delayed management. We report the successful automatic screening of Apert syndrome on this low-resolution picture, suggesting that AI-based facial recognition could be used on public data in crisis conditions to localize at-risk patients.