Multimodal abd Multifactorial Image-based Age Estimation

Age estimation (AE) is an important topic that has witnessed an increased attention, given its role in various daily issues, from health assessment and social interaction to human identification. In turn, age-at-death estimation can contribute to the identification of a deceased by reducing the number of possible candidates while providing a further line of evidence to guide a possible identification. A related sensitive casuistry is the AE of unaccompanied minors, required in court so that migrants whose real age is unknown do not have their rights threatened. Other problems where legal age estimation could have an impact are child brides and prostitution. Last but not least, precise AE can contribute to solving disputes in competitive sports with strict age limits.In practice, all the methods currently used for AE continue to be carried out manually, based on observations of macroscopic (visual observation) and morphometric (linear measurements) type.The nature of all these problems fits perfectly within the scope of Artificial Intelligence (AI). The massive digitisation of skeletal remains would open the door to the application of AI, especially to models based on Deep Learning, that excel with complex data such as images and 3D models. The final goal and expected impact of M2IbAE is to allow practitioners to shift from current observational methods to automatic, objective and robust AE with unprecedented accuracy. Considering current limitations and challenges, together with the different forensic and societal needs and possibilities at a global scale, Panacea aim to achieve this goal by: (1) Developing the first set of age-at-death estimation methods from bone photographs together with a novel multifactorial and multimodal approach; (2) Developing a novel methodological approach to legal AE by exploiting synergies of different radiological images, x-ray and CT, of several anatomical regions, wrist, teeth, clavicle.