Every day, millions of people around the world live with a rare disease. There are more than 7,000 such disorders, each affecting fewer than 5 in 10,000 people, and they often pose a major diagnostic challenge: it can take between 1 and 9 years to receive a diagnosis, and in 43% of cases, no treatment is available. Many of these conditions are invisible, while others involve severe alterations. Up to 40% of rare diseases also present with characteristic facial features — and this is where the BeNeXT project comes into play.
BeNeXT (Biomarker Enhanced diagNostic and prognostic Tools for rarE disorders: using X-chromosome alterations and heterogeneity in Turner syndrome as a model) is a multidisciplinary research project combining biology, medicine, engineering, and artificial intelligence to improve the diagnosis of rare diseases through 3D facial analysis. The goal is to develop accessible, accurate, and bias-free tools that help detect these diseases more quickly and reliably.
The disease chosen as a model is Turner syndrome, a genetic disorder that affects only females and involves the complete or partial absence of one X chromosome. Women with Turner syndrome often experience growth delays, short stature, delayed sexual development, infertility, heart defects, immune conditions, learning difficulties, and mental health challenges. They also tend to share certain facial traits, such as a small lower jaw, prominent cheekbones, and low-set ears and hairline. Despite this, diagnosis can take more than a decade. As a result, many girls miss the opportunity to benefit from hormone treatments that could regulate and improve their development.
The BeNeXT project aims to overcome the limitations of current clinical tools, such as the well-known Face2Gene app, which has shown inconsistent and often poor performance depending on the patient’s geographic background. BeNeXT includes data from a large sample of women with Turner syndrome from Spain and Latin America. This inclusive perspective will help optimize diagnostic tools by accounting for genetic diversity — something that has often been overlooked in research focused mainly on people of European descent.
BeNeXT uses 3D imaging technologies, including photogrammetry, mobile phones, morphometric analysis, and artificial intelligence algorithms to identify facial patterns associated with Turner syndrome. The project also gathers other genetic, clinical, cognitive, and biometric data — such as voice, body proportions, and movement — opening the door to new diagnostic pathways beyond facial analysis.
This approach is not only clinically valuable but also socially and educationally impactful. The BeNeXT team works closely with patient associations and carries out numerous science outreach activities. The name itself is a statement of intent: to bring the “next” — the future of diagnosis — closer to those who need it most: the millions of people living with rare diseases who, for too long, have been overlooked.
Because understanding a rare disease shouldn’t be so rare.
Associate Professor at the Faculty of Biology of the University of Barcelona (UB) since 2019. She holds a degree (2002) and a PhD in Biology (2007) from UB. She completed postdoctoral research stays in the United States, Canada, and Spain (2008–2017), and worked as a researcher at the European Molecular Biology Laboratory (2017–2019). Her research combines morphometrics, 3D imaging, genetics, and artificial intelligence to understand human variation and identify biomarkers that improve the diagnosis and prognosis of genetic, rare, and neurodevelopmental disorders. In addition to her research work, she teaches courses in Biological Anthropology, Human Diversity, and Anatomy, and is actively engaged in science communication and gender equality in science.
Associate Professor and researcher in the Human-Environment Research Group at La Salle – Ramon Llull University (URL). He holds degrees in Telecommunications Engineering (Technical and Superior, 1997 and 2000) and in Electronic Engineering, as well as a Master's in Project Management (2002), all from La Salle-URL. He earned his PhD from URL in 2009. In 2011, he was a visiting researcher with the Multimedia and Vision Research Group at Queen Mary, University of London. His research focuses on the application of computer vision and machine learning to address fundamental questions in medicine and biology, with particular emphasis on developing non-invasive, low-cost tools for the early and objective diagnosis of rare diseases.