Physics-informed neural networks for quantitative assessment of cancellous bone microstructure from photoacoustic signals

TL;DR

This paper introduces Biot-PINN, a physics-informed neural network that leverages Biot's poroelasticity theory to accurately decode photoacoustic signals for bone microstructure assessment, achieving 97% accuracy.

Contribution

The study develops a novel AI framework integrating physics-based modeling with neural networks for precise skeletal microstructure evaluation from photoacoustic data.

Findings

Biot-PINN achieves 97% accuracy in bone microstructure grading.

It outperforms traditional data-driven methods.

Provides a robust tool for early skeletal health diagnosis.

Abstract

Artificial intelligence (AI) empowers innovative diagnostic tools for common diseases, yet its clinical application in skeletal health evaluation is constrained by unsatisfactory accuracy, owing to the inherent porous and poroelastic biophysical features of bone. To address such bottlenecks amid global population aging, this study targets skeletal health and develops a reliable AI framework for precise bone microstructural characterization. We proposed Biot-PINN, a physics-informed neural network embedded with Biot's poroelasticity theory to characterize mechanical responses and wave propagation in poroelastic bone tissues. By decoding photoacoustic signals encoding bone mineral and microstructural features, the framework enables automatic bone microstructural grading. Experimental results reveal that Biot-PINN reaches an accuracy of 97%, markedly surpassing traditional data-driven approaches and providing a robust solution for early skeletal health diagnosis.