  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.
