Abstract 004 | Digital and synthetic twins to improve the evaluation of diseases

Damiano Coato ^1|2, Gianmarco Dolino ^1|2, Alice Berardo ^2|4, Emanuele L. Carniel ^2|3, Paolo Gargiulo ^1|2 | ¹Institute of Biomedical and Neural Engineering, Reykjavík University, Reykjavík, Iceland ; ²Department of Industrial Engineering, University of Padova, Padua, Italy; ³Centre for Mechanics of Biological Materials, University of Padova, Padua, Italy; ⁴Department of Civil Environmental and Architectural Engineering, University of Padova, Padua, Italy.

The concept of the digital twin has already transformed engineering and medicine by enabling virtual replicas of organs and tissues for simulation, diagnostics, and treatment planning [1]. Yet, many biomedical applications require physical counterparts that replicate not only the anatomy but also the mechanical behavior of living tissues [2]. To address this gap, we introduce the Synthetic Twin: a sustainable, physical analog of human tissue, fabricated via advanced 3D printing with tunable microstructure that reproduce native mechanical properties [3,4]. Unlike conventional phantoms, which depend exclusively on material composition, Synthetic Twins leverage microstructural engineering (e.g., fiber reinforcement, layering, porosity modulation) to replicate a wide spectrum of healthy and pathological conditions. As a proof-of-concept, we present the design and characterization of 3D printed fiber-reinforced cartilage phantoms, demonstrating how microstructural tuning enables anisotropic behavior that closely resembles native tissue. Using mechanical testing combined with Digital Image Correlation (DIC), we evaluated samples with single fiber orientations (superficial, middle, deep) as well as a novel multilayered construct mimicking the stratification of native cartilage. Results show that microstructural design governs stiffness and Poisson’s ratio in a depth-dependent manner, with multilayered constructs displaying the highest fidelity. This work demonstrates that Synthetic Twins can provide reproducible, customizable, and ethically sustainable alternatives to animal and cadaveric models, with applications in diagnostics, surgical training, and personalized healthcare.