Tissues are composed of cells organized into specialized compartments that, together with the extracellular matrix, form complex architectures. In the oral epithelium, basal cells divide and differentiate as they migrate toward the surface, forming stratified layers whose number and organization serve as key diagnostic markers in oral premalignant disorders (OPMD). Characterizing tissue morphological architecture through the spatial arrangement of diverse cell types provides critical insights into disease initiation, progression, and therapeutic response. In this talk, we present a computational framework that bridges artificial intelligence (AI) and statistical modeling to infer layered cellular structures directly from oral tissue pathology images. Recent advances in AI enable automated segmentation and classification of millions of cell nuclei, producing rich spatial point pattern data at unprecedented scale. However, existing statistical tools lack a principled framework for inference on layered point patterns. To address this gap, we developed a Bayesian nonparametric (BNP) hierarchical model that formally tests the existence of a layered structure and, when present, estimates the number of layers while quantifying the associated uncertainty. Our approach employs a mixture of finite mixtures (MFM) framework to assign cells to ordered layers without predefining their number, and a generalized Beta distribution to effectively characterize cell nuclear shape. Simulation studies demonstrate that our method surpasses existing clustering benchmarks, achieves linear scalability with respect to cell count, and maintains high accuracy even when using only 5% of nuclear pixels, highlighting its robustness and computational efficiency for large-scale pathology data. In an analysis of 128 oral OPMD patients from UT MDACC, we further establish a significant clinical association between the estimated epithelial layer count and dysplasia severity (p = 0.001).