The ability to directly print compliant biomedical devices on live human organs could benefit patient monitoring and wound treatment, which requires the 3D printer to adapt to the various deformations of the biological surface. We developed an in situ 3D printing system that estimates the motion and deformation of the target surface to adapt the toolpath in real time. With this printing system, a hydrogel-based sensor was printed on a porcine lung under respiration-induced deformation. The sensor was compliant to the tissue surface and provided continuous spatial mapping of deformation via electrical impedance tomography. This adaptive 3D printing approach may enhance robot-assisted medical treatments with additive manufacturing capabilities, enabling autonomous and direct printing of wearable electronics and biological materials on and inside the human body.
|Original language||English (US)|
|State||Published - Jun 2020|
Bibliographical noteFunding Information:
We thank W. Upchurch from the University of Minnesota (UMN) Visible Heart Lab (VHL) for assistance in acquiring porcine lung tissue, and D. Giles for assistance with the mechanical and rheological characterizations carried out in the UMN Polymer Characterization Facility. Research reported in this publication was supported by Medtronic plc (for sensor development) and the National Institute of Biomedical Imaging and Bioengineering of the NIH under award number DP2EB020537. The content is solely the responsibility of the authors and does not necessarily represent the official views of Medtronic plc nor the NIH. Z.Z. acknowledges support from the graduate school of the University of Minnesota (2019-2020 Doctoral Dissertation Fellowship).
© 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).
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Supporting data for "3D Printed Deformable Sensors"
Zhu, Z., Park, H. S. & McAlpine, M., Data Repository for the University of Minnesota, 2020
DOI: 10.13020/vqfp-vq57, http://hdl.handle.net/11299/212709