Where are we with artificial intelligence in forensic anthropology
Charlotte Primeau
Forensic Centre for Digital Scanning and 3D Printing, WMG, University of Warwick
Presented at the 26th Annual Conference of the British Association for Biological Anthropology and Osteoarchaeology (BABAO)
18-20 September 2025, University of Leiceste
Introduction:
1: Thurzo et al. 2021. Use of advance artificial intelligence in forensic medicine, forensic anthropology and clinical anatomy. Healthcare, 9, 1545.
Sex estimation: 2: Bu et al. 2023. Automated sex estimation using deep convoluted neural network based on orthopantomogram. Forensic Science International, 348, 111704.
3: Anic-Milošević et al. 2023. Artificial neural network model for predicting sex using dental and orthodontic measurements. The Korean Journal of Orthodontics, 53(3), 194-204.
4: Erkartal et al. 2023. Gender estimation with parameters obtained from the upper dental arcade by using machine learning algorithms and artificial neural networks.
5: Fidya & Priyambadha 2018. Automation of gender determination in human canines using artificial intelligence. Dental Journal, 50(3), 116-120.
6: Senol et al. 2023. Sex and age estimation with machine learning algorithms with parameters obtained from cone beam computed tomography images of maxillary first molar and canine teeth. Egyptian Journal of Forensic Sciences, 13, 27.
7: Bewes et al. 2019. Artificial intelligence for sex determination of skeletal remains: Application of a deep learning artificial neural network to human skulls. Journal of Forensic and Legal Medicine, 62, 40-43.
8: Kartal et al. 2022. Sex estimation using foramen magnum measurements, discriminant analysis and artificial neural networks on an eastern Turkish population sample. Legal Medicine, 59, 102353.
9: Chomean et al. 2025. Enhancing forensic sec identification through AI-based analysis of the foramen magnum. Forensic Science International: Reports, 11, 100411.
10: Patil et al. 2020. Artificial neural network for gender determination using mandibular morphometric parameters: A comparative retrospective study. Cogent Engineering, 7, 1723783.
11: Poodendan et al. 2023. Morphometric analysis of dry vertebrae in a northeastern Thai population and possible correlation with sex. Surgical and Radiologic Anatomy, 45, 175-181.
12: Bidmos et al. 2023. Machine learning and discriminant function analysis in the formulation of generic models for sex prediction using patella measurements. International Journal of Legal Medicine, 137, 471-485.
13: Knecht et al. 2025. Sex estimation from patellar measurements in a contemporary Italian population: a machine learning approach. International Journal of Legal Medicine, 139, 1371-1380.
14: Nogueira et al. 2023. Sex assessment using the radius bone in a French samples when applying various statistical models. International Journal of Legal Medicine, 137, 925-934.
15: Venema et al. 2023. Employing deep learning for sex estimation of adult individuals using 2D images of the humerus. Neural Computing and Applications, 35, 5987-5998.
16: Blanc et al. 2025. Sexual dimorphism of the humerus bone is a French sample: comparison of several statistical models including machine learning models. International Journal of Legal Medicine, 139, 1395-1408.
17: Knecht et al. 2023. Sex estimation from long bones: a machine learning approach. International Journal of Legal Medicine, 137, 1887-1895.
18: Oura et al. 2023. Deep learning in sex estimation from knee radiographs – A proof-of-concept study utilising the Terry Anatomical Collection. Legal Medicine, 61, 102211.
Age estimation:
19: Abuabara et al. 2025. Evaluating the accuracy of generative artificial intelligence models in dental age estimation based on the Demirjian’s method. Frontiers in Dental Medicine, 6, 1634006.
20: Aljameel et al. 2023. Predictive Artificial Intelligence Model for detecting dental age using panoramic radiographic images. Big Data and Cognitive Computing, 7, 8.
21: Matthijs et al. 2024. Artificial intelligence and dental age estimation: development and validation of an automated stage allocation technique on all mandibular tooth types in panoramic radiographs. International Journal of Legal Medicine, 138, 2469-2479.
22: Kahm et al. 2023. Application of entire dental panoramic image data in artificial intelligence model for age estimation. BMC Oral Health, 23, 1007.
23: Kim et al. 2021. Age-group determination of living individuals using first molar images based on artificial intelligence. Nature Scientific Reports, 11, 1073.
24: Saric et al. 2022. Dental age assessment based on CBCT images using machine learning algorithms. Forensic Science International, 334, 111245.
25: Gámez-Granados et al. 2022. Automating the decision making process of Todd’s age estimation method from the pubic symphysis with explainable machine learning. Information Sciences, 612, 514-535.
Stature calculation:
26: Czibula et al. 2016. Machine learning-based approached for predicating stature from archaeological skeletal remains using long bone lengths. Journal of Archaeological Science, 69, 85-99.
27: Kira et al. 2022. Stature estimation by semi-automatic measurements of 3D CT images of the femur. International Journal of Legal Medicine, 137, 359-377.
28: Simon et al. 2023. Body height estimation from automated length measurements in standing long leg radiographs using artificial intelligence. Nature Scientific Reports, 13, 8504.
Ancestry:
29: Pengyue et al. 2021. ANINet: a deep neural network for skull ancestry estimation. BMC Bioinformatics, 22, 550.
30: Navega et al. 2015. AncesTrees: ancestry estimation with randomized decision trees. International Journal of Legal Medicine, 129, 1145-1153.
Identification:
31: Gómez et al. 2021. Deep architectures for the segmentation of frontal sinuses in X-ray images: Towards an automated forensic identification system in comparative radiography. Naurocomputing, 456, 575-585.
32: Wen et al. 2022. Human identification performed with skull’s sphenoid sinus based on deep learning. International Journal of Legal Medicine, 136, 1067-1074.
33: Kavousinejad et al. 2024. A. novel algorithm for forensic identification using geometric Cranial Patterns in digital lateral cephalometric radiographs in forensic dentistry. Diagnostics, 14, 1840.
34: Kin et al. 2025. Applications of deep learning for detecting implants in computed tomography scout images with multi-institution and multi-vendor for personal identification. Science & Justice, 65, 101315.