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.

翻译：舌表面分布着多种乳头，它们在味觉和触觉的机械与化学机制中发挥着整合作用。尽管乳头味觉功能的研究已较为深入，但单个个体内及不同个体间乳头的独特性仍不明确。本研究首次基于人类舌乳头三维微观扫描数据（n=2092）构建机器学习框架，揭示了乳头几何与拓扑特征的独特性。通过离散微分几何与计算拓扑方法提取的多项特征，我们计算分析了乳头形状的细微差异。可解释机器学习技术表明，乳头形状的持续同调特征在预测生物学变量方面最为有效。基于这些特征的小样本数据训练模型对乳头类型的预测准确率达85%。乳头类型分类模型可映射舌表面丝状乳头和菌状乳头的空间分布。值得注意的是，研究发现不同个体的乳头具有显著差异性——单乳头即可在15名参与者中以48%的准确率识别个体身份。本研究首次提供确凿证据表明舌乳头可作为独特标识符，为食物偏好研究和口腔诊断开辟新方向。