EPINN-GF: An enhanced physics-informed neural network integrating geometric features for stress prediction in complex spinal geometries
Abstract
BACKGROUND
Understanding spinal biomechanics is essential for exploring the functions of the spine and the pathogenesis of related diseases. Traditional numerical methods for biomechanical analysis are computationally expensive, while Physics-Informed Neural Network (PINN) struggles with complex solid geometries. This study develops an Enhanced Physics-Informed Neural Network that integrates Geometric Features (EPINN-GF) to address these limitations in predicting stress distribution for the complex spinal geometries.
STUDY OBJECTIVES
The primary objective of this study is to improve both the accuracy and efficiency of predicting stress distributions in the complex spinal geometries using the proposed EPINN‑GF framework. The secondary objective is to facilitate the clinical translation to support the diagnosis, surgical planning, and personalized treatment for the spinal disorders, particularly Adolescent Idiopathic Scoliosis (AIS).
METHODS
This study proposes EPINN-GF, which innovatively integrates geometric features and dual loss constraints. Specifically, the eigenvectors of the Laplacian operator, derived from spinal geometry coordinates, are utilized to capture both global and local structural characteristics of the spine. These eigenvectors, along with their corresponding normalized coordinates, serve as inputs to EPINN-GF. Furthermore, the equilibrium equations, which describe the balance between internal and external forces acting on the material, are embedded in the…
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