Forensic Tools for Species Identification of Skeletal Remains: Metrics, Statistics, and OsteoID.
Heather M GarvinRachel DunnSabrina B SholtsM Schuyler LittenMerna MohamedNathan KuttickatNoah SkantzPublished in: Biology (2021)
Although nonhuman remains constitute a significant portion of forensic anthropological casework, the potential use of bone metrics to assess the human origin and to classify species of skeletal remains has not been thoroughly investigated. This study aimed to assess the utility of quantitative methods in distinguishing human from nonhuman remains and present additional resources for species identification. Over 50,000 measurements were compiled from humans and 27 nonhuman (mostly North American) species. Decision trees developed from the long bone data can differentiate human from nonhuman remains with over 90% accuracy (>98% accuracy for the human sample), even if all long bones are pooled. Stepwise discriminant function results were slightly lower (>87.4% overall accuracy). The quantitative models can be used to support visual identifications or preliminarily assess forensic significance at scenes. For species classification, bone-specific discriminant functions returned accuracies between 77.7% and 89.1%, but classification results varied highly across species. From the study data, we developed a web tool, OsteoID, for users who can input measurements and be shown photographs of potential bones/species to aid in visual identification. OsteoID also includes supplementary images (e.g., 3D scans), creating an additional resource for forensic anthropologists and others involved in skeletal species identification and comparative osteology.
Keyphrases
- endothelial cells
- induced pluripotent stem cells
- deep learning
- bone mineral density
- pluripotent stem cells
- machine learning
- genetic diversity
- randomized controlled trial
- magnetic resonance imaging
- high resolution
- electronic health record
- clinical trial
- postmenopausal women
- bioinformatics analysis
- body composition
- risk assessment
- bone regeneration
- artificial intelligence
- convolutional neural network
- contrast enhanced