A unique global sample has been collected, including thousands of panoramic radiographs (OPT), cone beam computed tomography (CBCT) of the maxillofacial region, hand-wrist X-rays, and chest X-rays or computed tomographies (CT). A sample of more than 10,000 OPTs (10,739) was collected from universities and private dental clinics in more than 20 countries, from subjects of known sex, aged 14-26 years. In addition, medical images stored in public databases including whole body CT scans (New Mexico Decedent Image Database), chest X-rays (Clinical Center – American Research Hospital) and carpal X-rays (Radiological Society of North America – Pediatric Bone Age Challenge) were used. All samples are stored in a highly secure server of the University of Granada (UGR).
Regarding the methodology used for the analysis of the OPTs, different deep learning techniques were applied. In all cases, the images were preprocessed to obtain uniform images in terms of characteristics, regardless of the geographical origin of each sample. Then, different experiments were carried out with convolutional neural networks analyzing both the entire OPT and the third molar only. Furthermore, sex and geographical origin of the study subject has been considered. Finally, the study sample was divided into three parts, training (60%), validation (20%) and test (20%). The best model gave us an error of 1.12 years, with an accuracy of 88.38%. In the case of the analysis of carpal radiographs, an error of 0.57 years was obtained in a sample of subjects aged between 14 and 19 years.
Currently, a validation process is being carried out between human experts and artificial intelligence through the analysis of OPT of real cases of suspected minors from Morocco. Researchers with different expertise have been involved in this process. The aim is to test the efficiency of the models developed in real forensic practice situations and to find out the agreement between different types of observers, human and machine. In addition, a parallel study of three-dimensional images (CBCT and CT) is being carried out to automatically detect, from among the different slices, the sections corresponding to the maxillofacial region and the thorax, i.e. the anatomical areas of most interest for legal age estimation purposes: 4 1) the teeth; and 2) the clavicle. Once the process is automated, artificial neural networks can be applied to determine the accuracy and efficiency of the models in estimating legal age.
Special mention should be made of the section on dissemination, and the section on technology transfer. Apart from the national and international congresses at which the results have been presented (Tenerife, Alicante, Madrid, Bogota, Toulouse, Termoli, San José de Costa Rica, Santiago de Chile, Sydney, Denver), various activities have been organised at schools, institutes, universities and for the general public. These activities have consisted of research seminars, workshops, talks and guest lectures. In addition, Stefano De Luca participated in the “Researchers’ Night” in the city of Granada (Spain). With regard to technology transfer activities, a patent application on age estimation has been filed at European level.