1932

Abstract

Medical errors are a major concern in clinical practice, suggesting the need for advanced surgical aids for preoperative planning and rehearsal. Conventionally, CT and MRI scans, as well as 3D visualization techniques, have been utilized as the primary tools for surgical planning. While effective, it would be useful if additional aids could be developed and utilized in particularly complex procedures involving unusual anatomical abnormalities that could benefit from tangible objects providing spatial sense, anatomical accuracy, and tactile feedback. Recent advancements in 3D printing technologies have facilitated the creation of patient-specific organ models with the purpose of providing an effective solution for preoperative planning, rehearsal, and spatiotemporal mapping. Here, we review the state-of-the-art in 3D printed, patient-specific organ models with an emphasis on 3D printing material systems, integrated functionalities, and their corresponding surgical applications and implications. Prior limitations, current progress, and future perspectives in this important area are also broadly discussed.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-anchem-061417-125935
2018-06-12
2024-07-21
Loading full text...

Full text loading...

/deliver/fulltext/anchem/11/1/annurev-anchem-061417-125935.html?itemId=/content/journals/10.1146/annurev-anchem-061417-125935&mimeType=html&fmt=ahah

Literature Cited

  1. 1.  Owen H 2012. Early use of simulation in medical education. Simul. Healthc. 7:102–16
    [Google Scholar]
  2. 2.  Meller G 1997. A typology of simulators for medical education. J. Digit. Imaging 10:194–96
    [Google Scholar]
  3. 3.  Badash I, Burtt K, Solorzano CA, Carey JN 2016. Innovations in surgery simulation: a review of past, current and future techniques. Ann. Transl. Med. 4:453
    [Google Scholar]
  4. 4.  Zevin B, Aggarwal R, Grantcharov TP 2014. Surgical simulation in 2013: Why is it still not the standard in surgical training?. J. Am. Coll. Surg. 218:294–301
    [Google Scholar]
  5. 5.  Makary MA, Daniel M 2016. Medical error—the third leading cause of death in the US. BMJ 353:i2139
    [Google Scholar]
  6. 6.  Mehtsun WT, Ibrahim AM, Diener-West M, Pronovost PJ, Makary MA 2013. Surgical never events in the United States. Surgery 153:465–72
    [Google Scholar]
  7. 7.  Mottl-Link S, Hübler M, Kühne T, Rietdorf U, Krueger JJ et al. 2008. Physical models aiding in complex congenital heart surgery. Ann. Thorac. Surg. 86:273–77
    [Google Scholar]
  8. 8.  Dankowski R, Baszko A, Sutherland M, Firek L, Kalmucki P et al. 2014. 3D heart model printing for preparation of percutaneous structural interventions: description of the technology and case report. Kardiol. Pol. 72:546–51
    [Google Scholar]
  9. 9.  Gross BC, Erkal JL, Lockwood SY, Chen C, Spence DM 2014. Evaluation of 3D printing and its potential impact on biotechnology and the chemical sciences. Anal. Chem. 86:3240–53
    [Google Scholar]
  10. 10.  Low ZX, Chua YT, Ray BM, Mattia D, Metcalfe IS, Patterson DA 2017. Perspective on 3D printing of separation membranes and comparison to related unconventional fabrication techniques. J. Membr. Sci. 523:596–613
    [Google Scholar]
  11. 11.  McWilliams A 2016. Global markets for 3D printing Res. Rep. IAS102B, BCC Res Wellesley, MA: https://www.bccresearch.com/market-research/instrumentation-and-sensors/3d-printing-global-markets-report-ias102b.html
    [Google Scholar]
  12. 12.  Mitsouras D, Liacouras P, Imanzadeh A, Giannopoulos AA, Cai T et al. 2015. Medical 3D printing for the radiologist. Radiographics 35:1965–88
    [Google Scholar]
  13. 13.  Giannopoulos AA, Mitsouras D, Yoo SJ, Liu PP, Chatzizisis YS, Rybicki FJ 2016. Applications of 3D printing in cardiovascular diseases. Nat. Rev. Cardiol. 13:701–18
    [Google Scholar]
  14. 14.  Qiu K, Zhao Z, Haghiashtiani G, Guo S-Z, He M et al. 2018. 3D printed organ models with physical properties of tissue and integrated sensors. Adv. Mater. Technol. 3:1700235
    [Google Scholar]
  15. 15.  Gómez-Ciriza G, Hussain T, Gómez-Cía T, Valverde I 2015. Potential of 3D-printed models in planning structural interventional procedures. Interv. Cardiol. 7:345–52
    [Google Scholar]
  16. 16.  Rengier F, Mehndiratta A, von Tengg-Kobligk H, Zechmann CM, Unterhinninghofen R et al. 2010. 3D printing based on imaging data: review of medical applications. Int. J. Comput. Assist. Radiol. Surg. 5:335–41
    [Google Scholar]
  17. 17.  Marro A, Bandukwala T, Mak W 2016. Three-dimensional printing and medical imaging: a review of the methods and applications. Curr. Probl. Diagn. Radiol. 45:2–9
    [Google Scholar]
  18. 18.  Vukicevic M, Mosadegh B, Min JK, Little SH 2017. Cardiac 3D printing and its future directions. JACC Cardiovasc. Imaging 10:171–84
    [Google Scholar]
  19. 19.  Sun Z, Lee SY 2017. A systematic review of 3-D printing in cardiovascular and cerebrovascular diseases. Anatol. J. Cardiol. 17:423–35
    [Google Scholar]
  20. 20.  Young RJ, Lovell PA 2011. Introduction to Polymers Boca Raton, FL: CRC Press
    [Google Scholar]
  21. 21.  Wurm G, Tomancok B, Pogady P, Holl K, Trenkler J 2004. Cerebrovascular stereolithographic biomodeling for aneurysm surgery. Technical note. J. Neurosurg. 100:139–45
    [Google Scholar]
  22. 22.  Farooqi KM, Lengua CG, Weinberg AD, Nielsen JC, Sanz J 2016. Blood pool segmentation results in superior virtual cardiac models than myocardial segmentation for 3D printing. Pediatr. Cardiol. 37:1028–36
    [Google Scholar]
  23. 23.  Farooqi KM, Saeed O, Zaidi A, Sanz J, Nielsen JC et al. 2016. 3D printing to guide ventricular assist device placement in adults with congenital heart disease and heart failure. JACC Heart Fail 4:301–11
    [Google Scholar]
  24. 24.  Schievano S, Migliavacca F, Coats L, Khambadkone S, Carminati M et al. 2007. Percutaneous pulmonary valve implantation based on rapid prototyping of right ventricular outflow tract and pulmonary trunk from MR data. Radiology 242:490–97
    [Google Scholar]
  25. 25.  Wake N, Chandarana H, Huang WC, Taneja SS, Rosenkrantz AB 2016. Application of anatomically accurate, patient-specific 3D printed models from MRI data in urological oncology. Clin. Radiol. 71:610–14
    [Google Scholar]
  26. 26.  Kusaka M, Sugimoto M, Fukami N, Sasaki H, Takenaka M et al. 2015. Initial experience with a tailor-made simulation and navigation program using a 3-D printer model of kidney transplantation surgery. Transplant. Proc. 47:596–99
    [Google Scholar]
  27. 27.  Komai Y, Sugimoto M, Gotohda N, Matsubara N, Kobayashi T et al. 2016. Patient-specific 3-dimensional printed kidney designed for “4D” surgical navigation: a novel aid to facilitate minimally invasive off-clamp partial nephrectomy in complex tumor cases. Urology 91:226–33
    [Google Scholar]
  28. 28.  Bernhard JC, Isotani S, Matsugasumi T, Duddalwar V, Hung AJ et al. 2016. Personalized 3D printed model of kidney and tumor anatomy: a useful tool for patient education. World J. Urol. 34:337–45
    [Google Scholar]
  29. 29.  Anderson JR, Thompson WL, Alkattan AK, Diaz O, Klucznik R et al. 2016. Three-dimensional printing of anatomically accurate, patient specific intracranial aneurysm models. J. Neurointerv. Surg. 8:517–20
    [Google Scholar]
  30. 30.  Erbano BO, Opolski AC, Olandoski M, Foggiatto JA, Kubrusly LF et al. 2013. Rapid prototyping of three-dimensional biomodels as an adjuvant in the surgical planning for intracranial aneurysms. Acta Cir. Bras. 28:756–61
    [Google Scholar]
  31. 31.  Zein NN, Hanouneh IA, Bishop PD, Samaan M, Eghtesad B et al. 2013. Three-dimensional print of a liver for preoperative planning in living donor liver transplantation. Liver Transpl 19:1304–10
    [Google Scholar]
  32. 32.  Souzaki R, Kinoshita Y, Ieiri S, Hayashida M, Koga Y et al. 2015. Three-dimensional liver model based on preoperative CT images as a tool to assist in surgical planning for hepatoblastoma in a child. Pediatr. Surg. Int. 31:593–96
    [Google Scholar]
  33. 33.  Loadman MJ 2012. Analysis of Rubber and Rubber-Like Polymers Amsterdam: Springer
    [Google Scholar]
  34. 34.  Yoo SJ, Spray T, Austin EH3rd, Yun TJ, van Arsdell GS 2017. Hands-on surgical training of congenital heart surgery using 3-dimensional print models. J. Thorac. Cardiovasc. Surg. 153:1530–40
    [Google Scholar]
  35. 35.  Kiraly L, Tofeig M, Jha NK, Talo H 2016. Three-dimensional printed prototypes refine the anatomy of post-modified Norwood-1 complex aortic arch obstruction and allow presurgical simulation of the repair. Interact. Cardiovasc. Thorac. Surg. 22:238–40
    [Google Scholar]
  36. 36.  Shiraishi I, Yamagishi M, Hamaoka K, Fukuzawa M, Yagihara T 2010. Simulative operation on congenital heart disease using rubber-like urethane stereolithographic biomodels based on 3D datasets of multislice computed tomography. Eur. J. Cardiothorac. Surg. 37:302–6
    [Google Scholar]
  37. 37.  Yang DH, Kang JW, Kim N, Song JK, Lee JW, Lim TH 2015. Myocardial 3-dimensional printing for septal myectomy guidance in a patient with obstructive hypertrophic cardiomyopathy. Circulation 132:300–1
    [Google Scholar]
  38. 38.  Kimura T, Morita A, Nishimura K, Aiyama H, Itoh H et al. 2009. Simulation of and training for cerebral aneurysm clipping with 3-dimensional models. Neurosurgery 65:719–26
    [Google Scholar]
  39. 39.  Khan IS, Kelly PD, Singer RJ 2014. Prototyping of cerebral vasculature physical models. Surg. Neurol. Int. 5:11
    [Google Scholar]
  40. 40.  Wurm G, Lehner M, Tomancok B, Kleiser R, Nussbaumer K 2011. Cerebrovascular biomodeling for aneurysm surgery: simulation-based training by means of rapid prototyping technologies. Surg. Innov. 18:294–306
    [Google Scholar]
  41. 41.  Bustamante S, Bose S, Bishop P, Klatte R, Norris F 2014. Novel application of rapid prototyping for simulation of bronchoscopic anatomy. J. Cardiothorac. Vasc. Anesth. 28:1122–25
    [Google Scholar]
  42. 42.  Kurenov SN, Ionita C, Sammons D, Demmy TL 2015. Three-dimensional printing to facilitate anatomic study, device development, simulation, and planning in thoracic surgery. J. Thorac. Cardiovasc. Surg. 149:973–79
    [Google Scholar]
  43. 43.  Schmauss D, Haeberle S, Hagl C, Sodian R 2015. Three-dimensional printing in cardiac surgery and interventional cardiology: a single-centre experience. Eur. J. Cardiothorac. Surg. 47:1044–52
    [Google Scholar]
  44. 44.  Kondo K, Harada N, Masuda H, Sugo N, Terazono S et al. 2016. A neurosurgical simulation of skull base tumors using a 3D printed rapid prototyping model containing mesh structures. Acta Neurochir 158:1213–19
    [Google Scholar]
  45. 45.  Oishi M, Fukuda M, Yajima N, Yoshida K, Takahashi M et al. 2013. Interactive presurgical simulation applying advanced 3D imaging and modeling techniques for skull base and deep tumors. J. Neurosurg. 119:94–105
    [Google Scholar]
  46. 46.  Li W, Belmont B, Greve JM, Manders AB, Downey BC et al. 2016. Polyvinyl chloride as a multimodal tissue-mimicking material with tuned mechanical and medical imaging properties. Med. Phys. 43:5577–92
    [Google Scholar]
  47. 47.  Culjat MO, Goldenberg D, Tewari P, Singh RS 2010. A review of tissue substitutes for ultrasound imaging. Ultrasound Med. Biol. 36:861–73
    [Google Scholar]
  48. 48.  Lazebnik M, Madsen EL, Frank GR, Hagness SC 2005. Tissue-mimicking phantom materials for narrowband and ultrawideband microwave applications. Phys. Med. Biol. 50:4245–58
    [Google Scholar]
  49. 49.  Farrer AI, Odeen H, de Bever J, Coats B, Parker DL et al. 2015. Characterization and evaluation of tissue-mimicking gelatin phantoms for use with MRgFUS. J. Ther. Ultrasound 3:9
    [Google Scholar]
  50. 50.  Langeland S, D'Hooge J, Claessens T, Claus P, Verdonck P et al. 2004. RF-based two-dimensional cardiac strain estimation: a validation study in a tissue-mimicking phantom. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 51:1537–46
    [Google Scholar]
  51. 51.  Belohlavek M, Bartleson VB, Zobitz ME 2001. Real-time strain rate imaging: validation of peak compression and expansion rates by a tissue-mimicking phantom. Echocardiography 18:565–71
    [Google Scholar]
  52. 52.  McDonald M, Lochhead S, Chopra R, Bronskill MJ 2004. Multi-modality tissue-mimicking phantom for thermal therapy. Phys. Med. Biol. 49:2767–78
    [Google Scholar]
  53. 53.  Yuan Y, Wyatt C, Maccarini P, Stauffer P, Craciunescu O et al. 2012. A heterogeneous human tissue mimicking phantom for RF heating and MRI thermal monitoring verification. Phys. Med. Biol. 57:2021–37
    [Google Scholar]
  54. 54.  Adams F, Qiu T, Mark A, Fritz B, Kramer L et al. 2017. Soft 3D-printed phantom of the human kidney with collecting system. Ann. Biomed. Eng. 45:963–72
    [Google Scholar]
  55. 55.  Öpik R, Hunt A, Ristolainen A, Aubin PM, Kruusmaa M 2012. Development of high fidelity liver and kidney phantom organs for use with robotic surgical systems Presented at IEEE RAS/EMBS Int. Conf. Biomed. Robot. Biomechatron., 4th Rome:
    [Google Scholar]
  56. 56.  Cook JR, Bouchard RR, Emelianov SY 2011. Tissue-mimicking phantoms for photoacoustic and ultrasonic imaging. Biomed. Opt. Express 2:3193–206
    [Google Scholar]
  57. 57.  Inglis S, Ramnarine KV, Plevris JN, McDicken WN 2006. An anthropomorphic tissue-mimicking phantom of the oesophagus for endoscopic ultrasound. Ultrasound Med. Biol. 32:249–59
    [Google Scholar]
  58. 58.  Holt RG, Roy RA 2001. Measurements of bubble-enhanced heating from focused, MHz-frequency ultrasound in a tissue-mimicking material. Ultrasound Med. Biol. 27:1399–412
    [Google Scholar]
  59. 59.  Madsen EL, Zagzebski JA, Banjavie RA, Jutila RE 1978. Tissue mimicking materials for ultrasound phantoms. Med. Phys. 5:391–94
    [Google Scholar]
  60. 60.  Baba M, Matsumoto K, Yamasaki N, Shindo H, Yano H et al. 2017. Development of a tailored thyroid gland phantom for fine-needle aspiration cytology by three-dimensional printing. J. Surg. Educ 74:1039–46
    [Google Scholar]
  61. 61.  Lurie KL, Smith GT, Khan SA, Liao JC, Ellerbee AK 2014. Three-dimensional, distendable bladder phantom for optical coherence tomography and white light cystoscopy. J. Biomed. Opt. 19:36009
    [Google Scholar]
  62. 62.  Shen S, Wang H, Xue Y, Yuan L, Zhou X et al. 2017. Freeform fabrication of tissue-simulating phantom for potential use of surgical planning in conjoined twins separation surgery. Sci. Rep. 7:11048
    [Google Scholar]
  63. 63.  Wang B, Stender B, Long T, Zhang Z, Schlaefer A 2013. An approach to validate ultrasound surface segmentation of the heart. Biomed. Eng. 58:Suppl. 1 https://doi.org/10.1515/bmt-2013-4283
    [Crossref] [Google Scholar]
  64. 64.  Chen SJ, Hellier P, Marchal M, Gauvrit JY, Carpentier R et al. 2012. An anthropomorphic polyvinyl alcohol brain phantom based on Colin27 for use in multimodal imaging. Med. Phys. 39:554–61
    [Google Scholar]
  65. 65.  Kadoya N, Miyasaka Y, Nakajima Y, Kuroda Y, Ito K et al. 2017. Evaluation of deformable image registration between external beam radiotherapy and HDR brachytherapy for cervical cancer with a 3D‐printed deformable pelvis phantom. Med. Phys. 44:1445–55
    [Google Scholar]
  66. 66.  Forte AE, Galvan S, Manieri F, Baena FRY, Dini D 2016. A composite hydrogel for brain tissue phantoms. Mater. Des. 112:227–38
    [Google Scholar]
  67. 67.  Knox K, Kerber CW, Singel SA, Bailey MJ, Imbesi SG 2005. Stereolithographic vascular replicas from CT scans: choosing treatment strategies, teaching, and research from live patient scan data. Am. J. Neuroradiol. 26:1428–31
    [Google Scholar]
  68. 68.  Allard L, Soulez G, Chayer B, Qin Z, Roy D, Cloutier G 2013. A multimodality vascular imaging phantom of an abdominal aortic aneurysm with a visible thrombus. Med. Phys. 40:063701
    [Google Scholar]
  69. 69.  Ploch CC, Mansi C, Jayamohan J, Kuhl E 2016. Using 3D printing to create personalized brain models for neurosurgical training and preoperative planning. World Neurosurg 90:668–74
    [Google Scholar]
  70. 70.  Kalejs M, von Segesser LK 2009. Rapid prototyping of compliant human aortic roots for assessment of valved stents. Interact. Cardiovasc. Thorac. Surg. 8:182–86
    [Google Scholar]
  71. 71.  Stein N, Saathoff T, Antoni S-T, Schlaefer A 2015. Creating 3D gelatin phantoms for experimental evaluation in biomedicine. Curr. Dir. Biomed. Eng. 1:331–34
    [Google Scholar]
  72. 72.  Pogue BW, Patterson MS 2006. Review of tissue simulating phantoms for optical spectroscopy, imaging and dosimetry. J. Biomed. Opt. 11:041102
    [Google Scholar]
  73. 73.  Chmarra MK, Hansen R, Marvik R, Lango T 2013. Multimodal phantom of liver tissue. PLOS ONE 8:e64180
    [Google Scholar]
  74. 74.  Czerner M, Fasce LA, Martucci JF, Ruseckaite R, Frontini PM 2016. Deformation and fracture behavior of physical gelatin gel systems. Food Hydrocoll 60:299–307
    [Google Scholar]
  75. 75.  Czerner M, Martucci JF, Fasce LA, Ruseckaite RA, Frontini PM 2013. Mechanical and fracture behavior of gelatin gels. Int. Conf. Fract. 6:4439–48
    [Google Scholar]
  76. 76.  Xie L, Jiang M, Dong XG, Bai X, Tong J, Zhou J 2012. Controlled mechanical and swelling properties of poly(vinyl alcohol)/sodium alginate blend hydrogels prepared by freeze-thaw followed by Ca2+ crosslinking. J. Appl. Polym. Sci. 124:823–31
    [Google Scholar]
  77. 77.  Lewis JA 2008. Novel inks for direct-write assembly of 3-D periodic structures. Mater. Matters 3:4–7
    [Google Scholar]
  78. 78.  Lewis JA 2006. Direct ink writing of 3D functional materials. Adv. Funct. Mater. 16:2193–204
    [Google Scholar]
  79. 79.  Abdel-Sayed P, Kalejs M, von Segesser LK 2009. A new training set-up for trans-apical aortic valve replacement. Interact. Cardiovasc. Thorac. Surg. 8:599–601
    [Google Scholar]
  80. 80.  von Rundstedt FC, Scovell JM, Agrawal S, Zaneveld J, Link RE 2017. Utility of patient-specific silicone renal models for planning and rehearsal of complex tumour resections prior to robot-assisted laparoscopic partial nephrectomy. BJU Int 119:598–604
    [Google Scholar]
  81. 81.  Schuster GA, Schuster TG 1999. The relative amount of epithelium, muscle, connective tissue and lumen in prostatic hyperplasia as a function of the mass of tissue resected. J. Urol. 161:1168–73
    [Google Scholar]
  82. 82.  Huijing PA 1999. Muscle as a collagen fiber reinforced composite: a review of force transmission in muscle and whole limb. J. Biomech. 32:329–45
    [Google Scholar]
  83. 83.  Muth JT, Vogt DM, Truby RL, Menguc Y, Kolesky DB et al. 2014. Embedded 3D printing of strain sensors within highly stretchable elastomers. Adv. Mater. 26:6307–12
    [Google Scholar]
  84. 84.  Shui W, Zhou M, Chen S, Pan Z, Deng Q et al. 2017. The production of digital and printed resources from multiple modalities using visualization and three-dimensional printing techniques. Int. J. Comput. Assist. Radiol. Surg. 12:13–23
    [Google Scholar]
  85. 85.  Ogden RW 1972. Large deformation isotropic elasticity: on the correlation of theory and experiment for incompressible rubberlike solids. Proc. R. Soc. A. 326:565–84
    [Google Scholar]
  86. 86.  Kim B, Lee SB, Lee J, Cho S, Park H et al. 2012. A comparison among Neo-Hookean model, Mooney-Rivlin model, and Ogden model for chloroprene rubber. Int. J. Precis. Eng. Man. 13:759–64
    [Google Scholar]
  87. 87.  Bouguet J-Y 2004. Camera calibration toolbox for Matlab Tech. Rep., Calif. Inst. Technol., Pasadena
    [Google Scholar]
  88. 88.  Zhang Z 1999. Flexible camera calibration by viewing a plane from unknown orientations Presented at IEEE Int. Conf. Comp. Vision, 7th, Kerkya, Greece
    [Google Scholar]
  89. 89.  Wang B, Borazjani A, Tahai M, Curry AL, Simionescu DT et al. 2010. Fabrication of cardiac patch with decellularized porcine myocardial scaffold and bone marrow mononuclear cells. J. Biomed. Mater. Res. A 94:1100–10
    [Google Scholar]
  90. 90.  Poornejad N, Frost TS, Scott DR, Elton BB, Reynolds PR et al. 2015. Freezing/thawing without cryoprotectant damages native but not decellularized porcine renal tissue. Organogenesis 11:30–45
    [Google Scholar]
  91. 91. NinjaTek. 2016. NinjaFlex® 3D printing filament Tech. Spec., NinjaTek Manheim, PA: https://ninjatek.com/wp-content/uploads/2016/05/NinjaFlex-TDS.pdf
    [Google Scholar]
  92. 92. NinjaTek. 2016. SemiFlex3D printing filament Tech. Spec., NinjaTek Manheim, PA: https://ninjatek.com/wp-content/uploads/2016/05/SemiFlex-TDS.pdf
    [Google Scholar]
  93. 93. Polymaker. 2017. PolyFlex3D printing filament Tech. Data Sheet, Polymaker, Shanghai. https://www.lulzbot.com/sites/default/files/PolyFlex_TDS-v1.pdf
    [Google Scholar]
  94. 94.  Baeck K, Lopes P, Verschueren P, Biglino G, Capelli C 2013. State of the art in 3D printing of compliant cardiovascular models: HeartPrint. Material characterization of HeartPrint models and comparison with arterial tissue properties. Presented at Joint Workshop New Technol. Comput. Robot Assist. Surg., 3rd Verona, Italy:
    [Google Scholar]
  95. 95. Objet Geom. Inc. 2012. FullCure®930 TangoPlus Tech. Data Sheet, Objet Geom. Inc Billerica, MA: http://www.intechrp.com/wp-content/uploads/2012/09/TangoFamily.pdf
    [Google Scholar]
  96. 96. Stratasys. 2014. PolyJet materials Tech. Data Sheet, Stratasys Eden Prairie, MN: http://usglobalimages.stratasys.com/Main/Files/Material_Spec_Sheets/MSS_PJ_PJMaterialsDataSheet.pdf?v=635785205440671440
    [Google Scholar]
  97. 97.  Tymrak BM, Kreiger M, Pearce JM 2014. Mechanical properties of components fabricated with open-source 3-D printers under realistic environmental conditions. Mater. Des. 58:242–46
    [Google Scholar]
  98. 98.  Lind JU, Busbee TA, Valentine AD, Pasqualini FS, Yuan H et al. 2017. Instrumented cardiac microphysiological devices via multimaterial three-dimensional printing. Nat. Mater. 16:303–8
    [Google Scholar]
  99. 99.  Zhang YS, Aleman J, Shin SR, Kilic T, Kim D et al. 2017. Multisensor-integrated organs-on-chips platform for automated and continual in situ monitoring of organoid behaviors. PNAS 114:E2293–302
    [Google Scholar]
  100. 100.  Yeo W-H, Webb RC, Lee W, Jung S, Rogers JA 2013. Bio-integrated electronics and sensor systems. Proc. SPIE 8725, Micro- Nanotechnol. Sensors Syst. Appl. V, 872511. https://doi.org/10.1117/12.2016380
    [Crossref]
  101. 101.  Zhang YH, Webb RC, Luo HY, Xue YG, Kurniawan J et al. 2016. Theoretical and experimental studies of epidermal heat flux sensors for measurements of core body temperature. Adv. Healthc. Mater. 5:119–27
    [Google Scholar]
  102. 102.  Guo SZ, Qiu K, Meng F, Park SH, McAlpine MC 2017. 3D printed stretchable tactile sensors. Adv. Mater. 29:1701218
    [Google Scholar]
  103. 103.  Mannoor MS, Tao H, Clayton JD, Sengupta A, Kaplan DL et al. 2012. Graphene-based wireless bacteria detection on tooth enamel. Nat. Commun. 3:763
    [Google Scholar]
  104. 104.  Xu L, Gutbrod SR, Bonifas AP, Su Y, Sulkin MS et al. 2014. 3D multifunctional integumentary membranes for spatiotemporal cardiac measurements and stimulation across the entire epicardium. Nat. Commun. 5:3329
    [Google Scholar]
  105. 105.  Dagdeviren C, Yang BD, Su Y, Tran PL, Joe P et al. 2014. Conformal piezoelectric energy harvesting and storage from motions of the heart, lung, and diaphragm. PNAS 111:1927–32
    [Google Scholar]
  106. 106.  Lu B, Chen Y, Ou D, Chen H, Diao L et al. 2015. Ultra-flexible piezoelectric devices integrated with heart to harvest the biomechanical energy. Sci. Rep. 5:16065
    [Google Scholar]
  107. 107.  Laufer S, Rasske K, Stopfer L, Kurzynski C, Abbott T et al. 2016. Fabric force sensors for the clinical breast examination simulator. Stud. Health Technol. Inform. 220:193–98
    [Google Scholar]
  108. 108.  Poniatowski LH, Wolf JS Jr., Nakada SY, Reihsen TE, Sainfort F, Sweet RM 2014. Validity and acceptability of a high-fidelity physical simulation model for training of laparoscopic pyeloplasty. J. Endourol. 28:393–98
    [Google Scholar]
  109. 109.  Robinson SS, O'Brien KW, Zhaob H, Peele BN, Larson CM et al. 2015. Integrated soft sensors and elastomeric actuators for tactile machines with kinesthetic sense. Extreme Mech. Lett. 5:47–53
    [Google Scholar]
  110. 110.  Sun JY, Keplinger C, Whitesides GM, Suo Z 2014. Ionic skin. Adv. Mater. 26:7608–14
    [Google Scholar]
  111. 111.  Johnson BN, Lancaster KZ, Zhen G, He J, Gupta MK et al. 2015. 3D printed anatomical nerve regeneration pathways. Adv. Funct. Mater. 25:6205–17
    [Google Scholar]
  112. 112.  Zou R, Xia Y, Liu SY, Hu P, Hou WB et al. 2016. Isotropic and anisotropic elasticity and yielding of 3D printed material. Compos. Pt. B Eng. 99:506–13
    [Google Scholar]
  113. 113.  Gnanasekaran K, Heijmans T, van Bennekom S, Woldhuis H, Wijnia S et al. 2017. 3D printing of CNT- and graphene-based conductive polymer nanocomposites by fused deposition modeling. Appl. Mater. Today 9:21–28
    [Google Scholar]
  114. 114.  Valentine AD, Busbee TA, Boley JW, Raney JR, Chortos A et al. 2017. Hybrid 3D printing of soft electronics. Adv. Mater. 29:1703817
    [Google Scholar]
/content/journals/10.1146/annurev-anchem-061417-125935
Loading
/content/journals/10.1146/annurev-anchem-061417-125935
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error