Application of Artificial Intelligence to an Impact-Based Analysis Method to Estimate the Soft Tissue Phantom Stiffness: A Proof-of-Concept

Assessing the biomechanical properties of soft tissues can be useful because they are related to their pathophysiological state. This study explores the application of artificial intelligence (AI) to an Impact-Based Analysis Method (IBAM) to predict the mechanical properties of soft tissues. 40 agar-based soft tissue phantoms with different stiffness were prepared. For each phantom, Young’s modulus was estimated using dynamic mechanical analysis and IBAM measurements were performed. Various AI-based models were applied to the results obtained with the IBAM approach to predict Young’s modulus. Principal component analysis shows that three parameters can explain 85% of the variation in IBAM data. The times of the different maxima of the force signal peaks are significantly correlated with Young’s modulus (R2 = 0.99). Most AI-based models allow a decrease in the prediction error compared to the standard IBAM approach (from 5.4% down to 1.2%). Decision trees and ensemble stacking, as well as convolutional neural networks, show a decrease in the prediction error of 74% and 50%, respectively. Applying AI approaches within the IBAM framework is a powerful approach to identify Young’s modulus of soft tissues. This study paves the way for using AI-based methods to characterize superficial soft tissue biomechanical properties.