AI-Assisted Label-Free Monitoring Bone Mineral Metabolism on Demineralized Bone Paper.

Abstract

Effective drug development for bone-related diseases, such as osteoporosis and metastasis, is hindered by the lack of physiologically relevant in vitro models. Traditional platforms, including standard tissue culture plastic, fail to replicate the structural and functional complexity of the natural bone extracellular matrix. Recently, osteoid-mimicking demineralized bone paper (DBP), which preserves the intrinsic collagen structure of mature bone and exhibits semitransparency, has demonstrated the ability to reproduce in-vivo-relevant osteogenic processes and mineral metabolism. Here, we present a label-free, longitudinal, and quantitative monitoring of mineralized collagen formation by osteoblasts and subsequent osteoclast-driven mineral resorption on DBP using brightfield microscopy. A Segment.ai machine learning algorithm is applied for time-lapse bright-field image analysis, enabling identification of osteoclast resorption areas and automated quantification of large image datasets over a three-week culture period. This work highlights the potential of DBP as a transformative platform for bone-targeting drug screening and osteoporosis research.