High-fidelity, personalized cardiac modeling via AI-driven 3D reconstruction and embedded silicone rubber printing

The burgeoning clinical demand for patient-specific cardiac modeling encounters significant challenges. The current clinical cardiac models are either difficult to manufacture or lack of detailed geometric structures and hence, often fail to incorporate important patient-specific characteristics. Moreover, most 3D-printable soft materials, such as Thermoplastic Poly-Urethane (TPU) or elastic resins, exhibit insufficient flexibility and biocompatibility to accurately mimic cardiac tissues, therefore limiting their ability to truly replicate patient-specific cardiac conditions. To address these limitations, we propose an innovative method for patient-specific cardiac substructure reconstruction based on the integration of Artificial Intelligence (AI) and embedded 3D printing. First, by combining medical imaging data (CT scan) with AI-driven high-precision 3D reconstruction algorithms, the new method segments the patient-specific cardiac structure into 10 substructures. The average Dice coefficient across the ten substructures is 0.87. Second, it uses an embedded 3D printing technique which utilizes silicone rubber matrix as supporting structure and uses diluted catalyst ink to extrude onto the supporting matrix. Through precise regulation of the matrix composition, material deposition rate and curing time, it can fabricate high-fidelity, complex 3D patient-specific silicone heart models with the average dimensional error less than 0.5 mm. The proposed method can substantially reduce manual intervention and post-processing time. The fabricated models provide valuable morphological insights for cardiovascular diagnosis and treatment planning. It is believed that many clinic applications will follow.