Advances in 4D Shape-Memory Resins for AI-Aided Personalized Scaffold Bioengineering

Abstract This paper introduces an innovative workflow for developing personalized bioengineered scaffolds by combining AI-driven auto-segmentation of Cone Beam Computed Tomography (CBCT) scans with the design of temperature-responsive photocurable resins, or 4D polymers. Using Diagnocat software, AI segmentation achieves precise morphological replication of anatomical structures, creating accurate 3D models that are tailored to each patient's unique anatomy. These models guide the fabrication of scaffolds with varying porosities and geometries, using TC-85—a biocompatible, temperature-sensitive resin with distinct mechanical and viscoelastic properties. The resin’s temperature responsiveness enables the scaffolds to dynamically adapt to physiological conditions, enhancing functionality by morphologically shifting to stimulate adherent cells. This approach demonstrates the advantages of automated AI segmentation and underscores the potential of 4D scaffolds for advanced bioengineering applications.