Machine learning and Topological data analysis identify unique features of human papillae in 3D scans

TL;DR

This study employs machine learning and topological data analysis on 3D scans of human tongue papillae to reveal their individual uniqueness and classify papillae types with high accuracy, opening new avenues in oral diagnostics.

Contribution

It introduces the first machine learning framework analyzing 3D papillae scans, highlighting the effectiveness of topological features in classification and individual identification.

Findings

Persistent homology features are most effective for prediction.

Papillae type classification accuracy is 85%.

Individuals can be identified with 48% accuracy among 15 participants.

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 (n = 2092), 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 unprecedented evidence demonstrating that tongue papillae can serve as a unique identifier inspiring new research direction for food preferences and oral diagnostics.