Introduction: The Chapel of St. Dominic of the Cathedral of 'Al - Tahira' in Qaraqosh (Iraq) allegedly contained human remains belonging to two martyrs, according to the testimonies of locals. In this article, the authors discuss the recovery and study of human remains that took place there in May 2022. Methods: A team of Forensic Anthropologist and Forensic Pathologists from Italy were involved in the excavations, which allowed discovery of skeletal remains referred to two individuals (here named C1 and C2). Their position was first described, then each bone remain was examined also inferring information about sex estimation, height calculation, age at death calculation, and dental formula. Bone elements underwent microscopic examination, and the identity of the two remains found was investigated through genetic analysis. Both anatomically repositioned bone remains were subjected to CT examination, and facial reconstructions of the two individuals were performed. Circumstantial data were also acquired. Finally, the cause and manner of death were hypothesized. Results: In the occipital region of C1, the presence of an extensive fracture complex with transverse orientation was observed. C2 showed a cranial disintegration with fracture fragments affecting the structures of the left neuro- and splanchnic skull and to a greater extent the left hemifacial, orbital, zygomatic regions and thethe same side of the face. In addition, no bones attributable to those of the left hand of C2 were found. Discussion: With reference to C1, nothing precludes that the death occurred due to severe blunt-fracture trauma with greater expression at the level of the cranial district. On the other hand, it seems reasonable to assume that C2 was the victim of an assault in which his left hand was amputated, he was beaten and pierced by a bayonet, and finally he was killed with a rifle shot to the head.

Artificial intelligence (AI) has emerged as a promising tool in forensic sciences, offering new opportunities for personal identification through automated analysis of biological and imaging data. AI-based approaches have been increasingly applied to tasks such as sex estimation, human identification, ancestry estimation, and kinship analysis. This systematic review aims to synthesize the available evidence regarding the applications, methodological characteristics, and performance of AI models in forensic personal identification. A systematic literature search was conducted in PubMed/MEDLINE and Scopus following PRISMA guidelines. Studies investigating AI applications for forensic identification were included. Data extraction focused on study characteristics, dataset type, AI model architecture, forensic task, validation strategy, and reported performance metrics. A total of 89 studies published between 2012 and 2026 met the inclusion criteria. The majority of studies focused on sex estimation (63%), followed by human identification, ancestry estimation, multi-task prediction, and kinship verification. Most studies relied on imaging datasets, particularly computed tomography and radiographic images. Deep learning models represented the most frequently used analytical approaches. Reported accuracy values were generally high, with a median accuracy of 91.4% and an interquartile range of 88.9–95.0% in studies reporting single-value accuracy metrics. Deep learning approaches tended to achieve slightly higher performance than traditional machine learning models. AI shows considerable potential to support forensic personal identification, particularly in imaging-based applications. However, methodological heterogeneity, population-specific datasets, and limited external validation remain important challenges. Future research should prioritize standardized validation protocols, multi-population datasets, and transparent reporting to ensure the forensic applicability of AI-based identification systems.

Processing a photo aimed at the aging of the person depicted is a delicate process. The technique performed in these cases, i.e. age progression, takes into consideration the physiognomic elements of the face and its variations over time. This study aims to deepen knowledge regarding the morphological similarities of the physiognomic traits found in two generations of an Italian family. Age progression is carried out on the two daughters to compare the result with their current appearance. Twelve photos were studied, depicting the members of an Italian family in close-up. Three photos of each member of the family were analysed, consisting of the father, mother, and two sisters (L. and E.). Facial features, such as shape of the face, eyes, eyebrows, nose and mouth were evaluated at age 1, 7 and 30. In addition, age progression technique was applied. Through the analysis of the images, facial features are studied to find similarities, and the problems encountered in studying the photos are discussed. Furthermore, the age progressions are obtained, and they do not show 100% accuracy. This study represents an analysis of age progression technique and explains how the provision of photographic documentation of family members can be important.