Abstract

Type 2 diabetes mellitus (T2DM) significantly elevates fracture risk, a severe complication often underestimated by conventional bone mineral density (BMD) assessments. Here, we applied label-free multimodal nonlinear optical (NLO) imaging with AI-powered texture feature analysis to characterize T2DM-related bone quality alterations.

Our results identified aberrant spatial protein distribution, characterized by increased homogeneity and reduced contrast, as a distinctive pathological feature in T2DM bone. The alterations in spatial distribution were also observed in hydroxyapatite (HA) and autofluorescent metabolites. A K-nearest neighbor (KNN) model, trained on fused texture features from these three components, achieved a superior classification accuracy of 93.56% in distinguishing T2DM-related bone tissues, markedly outperforming single-component models (~70%).

This demonstrated that fused multi-component spatial distribution features offer enhanced discriminative power for quantifying T2DM-associated pathological changes. Collectively, aberrant molecular spatial distribution, particularly of protein, represents a potentially unappreciated indicator of diabetic bone quality alterations. Integrating multimodal NLO imaging with explainable AI offers a novel approach for unraveling the mechanistic underpinnings of complex pathological alterations, which not only overcomes the limitations of conventional biomarker assessment but also establishes a powerful framework for discovering new pathological targets.