Congenital anomalies are a major cause of infant mortality and childhood morbidity, affecting 3% of newborns. It is estimated that 30% to 40% of genetic disorders produce alterations in the normal morphology of the face and the head (dysmorphology), which can impact swallowing, breathing, hearing, vision, speech, and -more importantly- cognitive development. Thus, craniofacial anomalies have been highlighted as an index of developmental disturbance at early stages of life. Initial diagnosis is often based on visual inspection from paediatricians but, unfortunately, dysmorphology is hard to identify in this way, and massive genetic screening is expensive and impractical. For these reasons, there is a growing interest in using facial imaging as a low-cost tool for genetic pre-screening, i.e., to highlight suspicious cases for further study. In this context, our on-going project eSCANFace (Plan Nacional) is developing the technology necessary to make such early screening more accurate, more accessible, and more comprehensive, and to allow its deployment as early as possible in life. We are developing a facial analysis framework that leverages deep-learning technology and computer vision for face dysmorphology assessment in babies (including fetuses) to achieve better syndrome prioritization and diagnosis. The project is coordinated by two DTIC research groups (BCN-MedTech and CMTech) and with the support and collaboration of 5 other national and international institutions.

During the last year, we have realized the importance of having software developers embedded in our research group to facilitate the clinical translation and use of our tools. The development of clinician-friendly software prototypes and infrastructure at a professional level is beyond our skills.

In this project, we want to provide interactive visual analytics and data visualization methods to explore the data and results generated in eSCANFace. This will maximize the outcomes of the project and promote active multidisciplinary collaboration. To achieve this, we need a dedicated person to design, develop, and implement a clinician/researcher-friendly user-interface and visual analytics while ensuring data safety and privacy.