Decision tree analysis as a supplementary tool to enhance histomorphological differentiation when distinguishing human from non-human cranial bone in both burnt and unburnt states: A feasibility study

Artificial intelligence in the practice of forensic medicine: a scoping review

Forensic medicine is a thriving application field for artificial intelligence (AI). Indeed, AI applications intended to forensic pathologists or forensic physicians have emerged since the last decade. For example, AI models were developed to help estimate the biological age of migrants or human remains. However, the uses of AI applications by forensic pathologists or physicians and their levels of integration in medicolegal practices are not well described yet. Therefore, a scoping review was conducted on PubMed, ScienceDirect, and Scopus databases. This review included articles that mention any AI application used by forensic pathologists or physicians in practice or any AI model applied in one expertise field of the forensic pathologist or physician. Articles in other languages than English or French or dealing mainly with complementary analyses handled by experts who are not forensic pathologists or physicians or with AI to analyze data for research purposes in forensic medicine were excluded from this review. All the relevant information was retrieved in each article from a grid analysis derived and adapted from the TRIPOD checklist. This review included 35 articles and revealed that AI applications are developed in thanatology and in clinical forensic medicine. However, those applications seem to mainly remain in research and development stages. Indeed, the use of AI applications by forensic pathologists or physicians is not actual due to issues discussed in this article. Finally, the integration of AI in daily medicolegal practice involves not only forensic pathologists or physicians but also legal professionals.

An overview of de novo bone generation in animal models

Some of the earliest success in de novo tissue generation was in bone tissue, and advances, facilitated by the use of endogenous and exogenous progenitor cells, continue unabated. The concept of one health promotes shared discoveries among medical disciplines to overcome health challenges that afflict numerous species. Carefully selected animal models are vital to development and translation of targeted therapies that improve the health and well-being of humans and animals alike. While inherent differences among species limit direct translation of scientific knowledge between them, rapid progress in ex vivo and in vivo de novo tissue generation is propelling revolutionary innovation to reality among all musculoskeletal specialties. This review contains a comparison of bone deposition among species and descriptions of animal models of bone restoration designed to replicate a multitude of bone injuries and pathology, including impaired osteogenic capacity.

Using histomorphometry for human and nonhuman distinction: A test of four methods on fresh and archaeological fragmented bones

Positive identification of human remains is the very first step in anthropological analysis, and the task may be particularly difficult in the case of fragmented bones. Histomorphometry methods have been developed to discriminate human from nonhuman bones, based on differences in the size and shape of Haversian systems between the two groups. Those methods all focus on a very specific type of bone, section, and zone. Therefore, the objective of this study was to test the efficiency of four histomorphometric methods on a sample of fragmented bones. The sample is composed of 37 archaeological and fresh specimens, 25 nonhumans (Bos taurus, Equus caballus, Sus scrofa, Capreolus, Canis familiaris, Cervus elaphus, Ovis, and Capra) and 12 humans (Homo sapiens). Eight histomorphometric criteria were collected from all intact osteons visible on each fragment and then inserted into the corresponding discriminate function of each method. The results were compared with the real origin to establish rates of correct classification for each method. The methods of Martiniaková et al. (2006) and Crescimanno and Stout (2012) obtained very low percentages of good classification (32 % and 67 %). Those of Cattaneo et al. (1999) obtained 94 % correct classification, but only after a correction of the units of measurement for Haversian canal area in their formula. The methods of Dominguez and Crowder (2012) obtained an 86 % rate for well-classified specimens. Some of the methods tested here contain errors in the original publication that make them unusable in their current state. Plus, it seems that histomorphometric methods developed from specific areas are more difficult to apply to fragments. A reduced number of intact osteons analyzed may partially affect the reliability of the method by being unrepresentative of the entire microstructure. Therefore, this study demonstrates that one should be cautious with the use of histomorphometric methods to distinguish human and nonhuman fragmented bone until further research can refine these methods to achieve greater reliability.