A deep‐learning‐based workflow to assess taxonomic affinity of hominid teeth with a test on discriminating <i>Pongo</i> and <i>Homo</i> upper molars

Yi, Zhixing; Zanolli, Clément; Liao, Wei; Wang, Wei

doi:10.1002/ajpa.24286

American J Phys Anthropol

2021

DOI: 10.1002/ajpa.24286

|View full text |Cite

A deep‐learning‐based workflow to assess taxonomic affinity of hominid teeth with a test on discriminating Pongo and Homo upper molars

Zhixing Yi

Clément Zanolli

Wei Liao

et al.

Abstract: HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des labor… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 61 publications

(94 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

Artificial neural networks reconstruct missing perikymata in worn teeth

Modesto‐Mata,

de la Fuente Valentín,

Hlusko

et al. 2024

The Anatomical Record

View full text Add to dashboard Cite

Dental evolutionary studies in hominins are key to understanding how our ancestors and close fossil relatives grew from the early stages of embryogenesis into adults. In a sense, teeth are like an airplane's ‘black box’ as they record important variables for assessing developmental timing, enabling comparisons within and between populations, species, and genera. The ability to discern this type of nuanced information is embedded in the nature of how tooth enamel and dentin form: incrementally and over years. This incremental growth leaves chronological indicators in the histological structure of enamel, visible on the crown surface as perikymata. These structures are used in the process of reconstructing the rate and timing of tooth formation. Unfortunately, the developmentally earliest growth lines in lateral enamel are quickly lost to wear once the tooth crown erupts. We developed a method to reconstruct these earliest, missing perilymata from worn teeth through knowledge of the later‐developed, visible perikymata for all tooth types (incisors, canines, premolars, and molars) using a modern human dataset. Building on our previous research using polynomial regressions, here we describe an artificial neural networks (ANN) method. This new ANN method mostly predicts within 2 counts the number of perikymata present in each of the first three deciles of the crown height for all tooth types. Our ANN method for estimating perikymata lost through wear has two immediate benefits: more accurate values can be produced and worn teeth can be included in dental research. This tool is available on the open‐source platform R within the package teethR released under GPL v3.0 license, enabling other researchers the opportunity to expand their datasets for studies of periodicity in histological growth, dental development, and evolution.

show abstract

Artificial neural networks reconstruct missing perikymata in worn teeth

Modesto‐Mata,

de la Fuente Valentín,

Hlusko

et al. 2024

The Anatomical Record

View full text Add to dashboard Cite

show abstract

Distinguishing primate taxa with enamel incremental variables

Kufeldt

Wood

2022

Journal of Human Evolution

View full text Add to dashboard Cite

Enamel thickness in the deciduous postcanine dentition of fossil and extant Pongo

Yi,

Zanolli,

Liao

et al. 2024

Journal of Human Evolution

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A deep‐learning‐based workflow to assess taxonomic affinity of hominid teeth with a test on discriminating Pongo and Homo upper molars

Cited by 3 publications

References 61 publications

Artificial neural networks reconstruct missing perikymata in worn teeth

Artificial neural networks reconstruct missing perikymata in worn teeth

Distinguishing primate taxa with enamel incremental variables

Enamel thickness in the deciduous postcanine dentition of fossil and extant Pongo

Contact Info

Product

Resources

About