A hybrid biofabrication platform for patient-specific aortic phantoms: from surgical rehearsal to device testing

Fabricating high-fidelity, patient-specific aortic phantoms that possess both complex pathological features and physiological compliance remains a significant challenge for single-method manufacturing techniques. This study presents a complete virtual-to-physical prototyping workflow, enabled by a novel Hybrid Additive Manufacturing Platform (HAMP), for translating clinical imaging data into high-fidelity, patient-specific aortic phantoms. Building upon a validated brush-spin-coating technique capable of precise wall thickness control (±0.1 mm), the HAMP synergistically integrates 3D printing and casting. This integration overcomes the limitations of single-method techniques, uniquely enabling the creation of phantoms with (i) controllable interlayer delamination for mimicking dissection, (ii) enclosed multi-chamber structures for endoleak simulation, (iii) seamless integration of dissimilar materials, and (iv) the replication of complex intra-wall pathologies such as intramural hematoma. The platform's capability was rigorously demonstrated through the successful fabrication and application of four distinct classes of aortic phantoms. These high-fidelity models were directly employed in: fundamental biomechanical studies to visualize dissection propagation; advanced surgical training for complex procedures like ex vivo fenestration; emergency preoperative planning, where a patient-specific model was delivered in under 30 h; and industrial medical device testing using parametric, ISO-compliant models. In each scenario, the phantoms provided functional, anatomically accurate representations suitable for the intended evaluation-whether physical testing, surgical rehearsal, or hydrodynamic assessment. In summary, the HAMP demonstrates a rapid virtual-to-physical prototyping workflow. By enabling the on-demand creation of complex, multi-material, patient-specific phantoms, it provides a versatile tool that bridges digital data and physical reality, addressing needs across research, clinical training, and device development.