Label-Free rapid bone biomarkers assessment via physics-guided machine learning-assisted photoacoustic correlation spectra analysis.

Obtaining information on bone metabolism through intraoperative or non-invasive examination remains a challenge in medical practice. Photoacoustic (PA) spectroscopy offers a method for identifying molecules within biological tissues by exploiting the contrast in their optical absorption properties. However, difficulties arise when analyzing bone tissue, which comprises a complex mixture of organic and inorganic components. The overlapping optical absorption peaks of various chemical constituents in bone tissue can significantly hinder the accuracy of PA absorption spectra decoupling inversion. In this study, we developed a decoupling technique that integrates PA correlation spectra (PCS) with physics-guided machine learning to analyze the chemical components of bone tissue quantitatively. The feasibility of using PCS and machine learning for bone metabolism was evaluated through numerical simulations and experimental studies on bone models with varying chemical compositions. The calculated quantitative parameters for the chemical components closely matched the ground truth values, thus allowing for the characterization of changes in bone composition. This non-invasive, radiation-free PA technology holds promise for advancing the diagnosis and monitoring of bone diseases.