AI-powered nonlinear optical imaging reveals protein spatial homogenization as an indicator of impaired bone quality in type 2 diabetes
人工智能驱动的非线性光学成像揭示了蛋白质空间均匀化是2型糖尿病骨质量受损的指标
人工知能駆動の非線形光学イメージングは蛋白質の空間均一化が2型糖尿病の骨品質損傷の指標であることを明らかにした
인공지능 비선형 광학 이미징은 단백질 공간적 동질화를 2형 당尿병의 2형 2형 당尿병의 인공지능
La imagen óptica no lineal impulsada por IA revela la homogeneización espacial de proteínas como un indicador de deterioro de la calidad ósea en la diabetes tipo 2
L'imagerie optique non linéaire alimentée par l'IA révèle l'homogénéisation spatiale des protéines comme indicateur d'une altération de la qualité osseuse chez le diabète de type 2
Нелинейная оптическая визуализация на основе ИИ показывает пространственную гомогенизацию белка как индикатор нарушения качества костей при диабете 2 типа
Bowen Zhang 张博文 ¹, Jiangbo Pu 蒲江波 ¹, Tao Hu 胡韬 ², Junjie Zeng 曾钧杰 ³, Han Zhang 张涵 ³, Zemeng Chen 陈泽蒙 ¹, Xiang Ji 吉祥 ¹, Shuhua Yue 岳蜀华 ³, Lin Z. Li ⁴, Ting Li 李婷 ¹
¹ Biomedical Engineering Institute, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, China
中国 天津 中国医学科学院生物医学工程研究所 北京协和医学院
² Department of Spine Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200092, China
中国 上海 上海市东方医院（同济大学附属东方医院）脊柱外科
³ Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
中国 北京 北京航空航天大学生物与医学工程学院 北京市生物医学工程高精尖创新中心 生物力学与力生物学教育部重点实验室
⁴ Britton Chance Laboratory of Redox Imaging, Department of Radiology, Perelman School of Medicine University of Pennsylvania, Philadelphia, PA 19104, USA
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.
