Quantitative Histopathological Validation of Bioprinted Tissue Models in Early Stage Oral Submucous Fibrosis

Vinit, Kanika Singroha

Keywords: Oral Submucous Fibrosis, Bioprinting Validation, Quantitative Tissue Analysis

TL;DR: We developed an AI framework that quantitatively validates bioprinted oral fibrosis tissue by analyzing collagen fiber patterns and cell density, matching early-stage OSMF pathology.

Abstract: Oral Submucous Fibrosis (OSMF) is a progressive fibrotic disorder of the oral mucosa with high malignant potential. Existing models have contributed to OSMF research but cannot capture the complex tissue organization needed to study disease progression and test interventions effectively. While 3D bioprinting offers a promising route for developing in-vitro disease models, validating the pathological fidelity of these constructs remains challenging due to reliance on subjective histological evaluation. We propose a deep learning-based framework for objective, quantitative validation of bioprinted OSMF tissue, centered on two key histopathological markers: (i) the ratio of fine to bundled collagen fibres and (ii) zone-wise cellular density. Our framework first establishes grade-specific marker distributions from real OSMF cases spanning all four clinical grades using histopathological images analyzed with attention-enhanced deep learning models. These models—ParNet-integrated ResNeXt50 for fibre classification and U-Net++ for nuclei segmentation—achieved 97.87\% accuracy and a Dice loss of 0.226, respectively, despite limited training data. We then applied the same analysis pipeline to 3D bioprinted tissue constructs engineered to mimic OSMF features. The bioprinted samples exhibited fibre composition and cellularity profiles closely matching early-stage OSMF, confirming their pathological fidelity and demonstrating the framework’s potential for rigorous validation of bioprinted disease models.

Submission Number: 17