Artificial intelligence for sex determination of skeletal remains: Application of a deep learning artificial neural network to human skulls

Bewes, James; Low, Andrea; Morphett, Antony; Pate, F. Donald; Henneberg, Maciej

doi:10.1016/j.jflm.2019.01.004

Cited by 115 publications

(70 citation statements)

References 8 publications

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“…Presently, some studies have peaked to over 90% accuracy in ancestry estimation, using new approaches such as geometric morphometrics, but these methods as applied to sex estimation have yielded a much lower accuracy of ~70% (Fortes de Oliveira et al, 2012;Jiménez-Arenas & Esquivel, 2013;Murphy & Garvin, 2018;Webster & David Sheets, 2010;Zelditch, Swiderski, & David Sheets, 2012). Our study however harnesses an innovative method-machine learning (Bejdová, Dupej, Krajíček, Velemínská, & Velemínský, 2018;Bewes, Low, Morphett, Pate, & Henneberg, 2019;Gao, Geng, & Yang, 2018).…”

Section: | Introductionmentioning

confidence: 92%

“…Indeed, a recent study used a trained model to identify the sex in Han Chinese, using CT scans of their skull, reaching an accuracy of 98% (Gao et al, 2018). Likewise, another study trained a model using over 900 CT scans, reaching over 95% accuracy (Bewes et al, 2019). Such techniques however, require expensive CT scans, where discriminant analysis requires only osteometric measurements (Martin & Saller, 1956), making the latter more prevalent in physical anthropology.…”

Section: | Introductionmentioning

confidence: 99%

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Sex estimation in cranial remains: A comparison of machine learning and discriminant analysis in Italian populations

Pozzi

Raffone

Belcastro

et al. 2020

Preprint

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Objectives:Using cranial measurements in two Italian populations, we compare machine learning methods to the more traditional method of linear discriminant analysis in estimating sex. We use crania in sex estimation because it is useful especially when remains are fragmented or displaced, and the cranium may be the only remains found. Materials and Methods:Using the machine learning methods of decision tree learning, support-vector machines, knearest neighbor algorithm, and ensemble methods we estimate the sex of two populations:Samples from Bologna and samples from the island of Sardinia. We used two datasets, one containing 17 cranial measurements, and one measuring the foramen magnum. Results and Discussion:Our results indicate that machine learning models produce similar results to linear discriminant analysis, but in some cases machine learning produces more consistent accuracy between the sexes. Our study shows that sex can be accurately predicted (> 80%) in Italian populations using the cranial measurements we gathered, except for the foramen magnum, which shows a level of accuracy of ~70% accurate which is on par with previous geometric morphometrics studies using crania in sex estimation. We also find that our trained machine learning models produce population-specific results; we see that Italian crania are sexually dimorphic, but the features that are important to this dimorphism differ between the populations. 2

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Section: | Introductionmentioning

confidence: 92%

Section: | Introductionmentioning

confidence: 99%

Sex estimation in cranial remains: A comparison of machine learning and discriminant analysis in Italian populations

Pozzi

Raffone

Belcastro

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…In the study shown [43] below, a neural network is proposed that is trained to recognize, starting from photos of skulls, the sex of the individual. Neural networks are very useful for this task: thanks to their structure at levels of connected "neurons" which each analyze a different part of the image and, finally, the output probability of belonging to a class of the input image is processed.…”

Section: Determination Of Sex From Human Skullsmentioning

confidence: 99%

The Computerization of Archaeology: Survey on Artificial Intelligence Techniques

Mantovan

Nanni

2020

SN COMPUT. SCI.

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This paper analyses the application of artificial intelligence techniques to various areas of archaeology and more specifically: (1) the use of software tools as a creative stimulus for the organization of exhibitions; the use of humanoid robots and holographic displays as guides that interact and involve museum visitors; (2) the analysis of methods for the classification of fragments found in archaeological excavations and for the reconstruction of ceramics, with the recomposition of the parts of text missing from historical documents and epigraphs; (3) the cataloging and study of human remains to understand the social and historical context of belonging with the demonstration of the effectiveness of the AI techniques used; (4) the detection of particularly difficult terrestrial archaeological sites with the analysis of the architectures of the Artificial Neural Networks most suitable for solving the problems presented by the site; the design of a study for the exploration of marine archaeological sites, located at depths that cannot be reached by man, through the construction of a freely explorable 3D version.

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“…The pioneering introduction of Artificially Intelligent Algorithms (AIAs) in fields of archaeology and paleoanthropology has revolutionized numerous sub-disciplines such as those related with genetic sequencing (Mondal, Bertranpetit & Lao, 2019), site and object detection (Anemone, Emerson & Conroy, 2011;Conroy et al, 2012;Emerson et al, 2015;Benhabiles & Tabia, 2016;Block et al, 2016;Wills, Choiniere & Barrett, 2018;Anemone & Conroy, 2018;Caspari & Crespo, 2019;Verschoof-van der Vaart & Lambers, 2019), physical anthropology (Bewes et al, 2019), biomechanics (Püschel et al, 2018) restoration (Derech, Tal & Shimshoni, 2018;Hermoza & Sipiarn, 2018), as well as taphonomy (Arriaza & Domínguez-Rodrigo, 2016;Domínguez-Rodrigo, 2019;Egeland et al, 2018;Byeon et al, 2019;Courtenay et al, 2019;Moclán, Domínguez-Rodrigo & Yravedra, 2019).…”

Section: Introductionmentioning

confidence: 99%

Application of artificially intelligent systems for the identification of discrete fossiliferous levels

Martín-Perea

Courtenay

Domingo

et al. 2020

PeerJ

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The separation of discrete fossiliferous levels within an archaeological or paleontological site with no clear stratigraphic horizons has historically been carried out using qualitative approaches, relying on two-dimensional transversal and longitudinal projection planes. Analyses of this type, however, can often be conditioned by subjectivity based on the perspective of the analyst. This study presents a novel use of Machine Learning algorithms for pattern recognition techniques in the automated separation and identification of fossiliferous levels. This approach can be divided into three main steps including: (1) unsupervised Machine Learning for density based clustering (2) expert-in-the-loop Collaborative Intelligence Learning for the integration of geological data followed by (3) supervised learning for the final fine-tuning of fossiliferous level models. For evaluation of these techniques, this method was tested in two Late Miocene sites of the Batallones Butte paleontological complex (Madrid, Spain). Here we show Machine Learning analyses to be a valuable tool for the processing of spatial data in an efficient and quantitative manner, successfully identifying the presence of discrete fossiliferous levels in both Batallones-3 and Batallones-10. Three discrete fossiliferous levels have been identified in Batallones-3, whereas another three have been differentiated in Batallones-10.

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Artificial intelligence for sex determination of skeletal remains: Application of a deep learning artificial neural network to human skulls

Cited by 115 publications

References 8 publications

Sex estimation in cranial remains: A comparison of machine learning and discriminant analysis in Italian populations

Sex estimation in cranial remains: A comparison of machine learning and discriminant analysis in Italian populations

The Computerization of Archaeology: Survey on Artificial Intelligence Techniques

Application of artificially intelligent systems for the identification of discrete fossiliferous levels

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