Andreeva, R., Sarkar, A. orcid.org/0000-0003-1742-2122 and Sarkar, R. (2023) Machine learning and topological data analysis identify unique features of human papillae in 3D scans. Scientific Reports, 13 (1). 21529. ISSN 2045-2322
Abstract
The tongue surface houses a range of papillae that are integral to the mechanics and chemistry of taste and textural sensation. Although gustatory function of papillae is well investigated, the uniqueness of papillae within and across individuals remains elusive. Here, we present the first machine learning framework on 3D microscopic scans of human papillae ( ), uncovering the uniqueness of geometric and topological features of papillae. The finer differences in shapes of papillae are investigated computationally based on a number of features derived from discrete differential geometry and computational topology. Interpretable machine learning techniques show that persistent homology features of the papillae shape are the most effective in predicting the biological variables. Models trained on these features with small volumes of data samples predict the type of papillae with an accuracy of 85%. The papillae type classification models can map the spatial arrangement of filiform and fungiform papillae on a surface. Remarkably, the papillae are found to be distinctive across individuals and an individual can be identified with an accuracy of 48% among the 15 participants from a single papillae. Collectively, this is the first evidence demonstrating that tongue papillae can serve as a unique identifier, and inspires a new research direction for food preferences and oral diagnostics.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2023. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Food Science and Nutrition (Leeds) > FSN Colloids and Food Processing (Leeds) |
Funding Information: | Funder Grant number EU - European Union 757993 |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Jan 2024 11:29 |
Last Modified: | 31 Jan 2024 12:01 |
Status: | Published |
Publisher: | Nature Research |
Identification Number: | 10.1038/s41598-023-46535-9 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:206735 |