Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Deep learning robotic guidance for autonomous vascular access

Nature Machine Intelligence volume 2, pages 104–115 (2020)Cite this article

Subjects

Abstract

Medical robots have demonstrated the ability to manipulate percutaneous instruments into soft tissue anatomy while working beyond the limits of human perception and dexterity. Robotic technologies further offer the promise of autonomy in carrying out critical tasks with minimal supervision when resources are limited. Here, we present a portable robotic device capable of introducing needles and catheters into deformable tissues such as blood vessels to draw blood or deliver fluids autonomously. Robotic cannulation is driven by predictions from a series of deep convolutional neural networks that encode spatiotemporal information from multimodal image sequences to guide real-time servoing. We demonstrate, through imaging and robotic tracking studies in volunteers, the ability of the device to segment, classify, localize and track peripheral vessels in the presence of anatomical variability and motion. We then evaluate robotic performance in phantom and animal models of difficult vascular access and show that the device can improve success rates and procedure times compared to manual cannulations by trained operators, particularly in challenging physiological conditions. These results suggest the potential for autonomous systems to outperform humans on complex visuomotor tasks, and demonstrate a step in the translation of such capabilities into clinical use.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Autonomous image-guided robotic vascular access, blood drawing and fluid delivery.
Fig. 2: Bimodal vessel imaging and analysis with deep convolutional neural networks.
Fig. 3: Comparison of deep learning and expert assessment of upper-extremity forearm vessels.
Fig. 4: Real-time robotic tracking and motion compensation.
Fig. 5: Autonomous cannulation in tissue-mimicking models over a broad demographic spectrum.
Fig. 6: In vivo autonomous blood drawing and fluid delivery in submillimetre vessels of rats.

Similar content being viewed by others

Data availability

Test datasets for evaluating source code are available at https://github.com/alvchn/nmi-vasc-robot. Public data used in the study are available in the SPLab Ultrasound Image Database (http://splab.cz/wp-content/uploads/2014/05/ARTERY_TRANSVERSAL.zip and http://splab.cz/wp-content/uploads/2013/11/us_images.zip), the PICMUS Database (https://www.creatis.insa-lyon.fr/Challenge/IEEE_IUS_2016/download) and the SPLab Tecnocampus Hand Image Database (http://splab.cz/en/download/databaze/tecnocampus-hand-image-database).

Code availability

Source code are available from the Github repository: https://github.com/alvchn/nmi-vasc-robot. Use of the code is subject to a limited right to use for academic, governmental or not-for-profit research. Use of the code for commercial or clinical purposes is prohibited in the absence of a Commercial License Agreement from Rutgers, The State University of New Jersey. References to open-source software used in the study are provided within the paper.

References

  1. Yang, G. Z. et al. Medical robotics—regulatory, ethical, and legal considerations for increasing levels of autonomy. Sci. Robot.2, eaam8638 (2017).

    Article  Google Scholar 

  2. Moustris, G. P., Hiridis, S. C., Deliparaschos, K. M. & Konstantinidis, K. M. Evolution of autonomous and semi-autonomous robotic surgical systems: a review of the literature. Int. J. Med. Robot. Comput. Assist. Surg.7, 375–392 (2011).

    Article  Google Scholar 

  3. Shademan, A. et al. Supervised autonomous robotic soft tissue surgery. Sci. Transl. Med.4, 337 (2016).

    Google Scholar 

  4. Edwards, T. L. et al. First-in-human study of the safety and viability of intraocular robotic surgery. Nat. Biomed. Eng.2, 649–656 (2018).

    Article  Google Scholar 

  5. Fagogenis, G. et al. Autonomous robotic intracardiac catheter navigation using haptic vision. Sci. Robot.4, eaaw1977 (2019).

    Article  Google Scholar 

  6. Weber, S. et al. Instrument flight to the inner ear. Sci. Robot.2, eaal4916 (2017).

    Article  Google Scholar 

  7. Daudelin, J. et al. An integrated system for perception-driven autonomy with modular robots. Sci. Robot.3, eaat4983 (2018).

    Article  Google Scholar 

  8. Niska, R., Bhuiya, F. & Xu, J. National hospital ambulatory medical care survey: 2010 emergency department summary. Natl Health Stat. Report2010, 1–31 (2010).

    Google Scholar 

  9. Horattas, M. C. et al. Changing concepts in long-term central venous access: catheter selection and cost savings. Am. J. Infect. Control29, 32–40 (2001).

    Article  Google Scholar 

  10. Sampalis, J. S., Lavoie, A., Williams, J. I., Mulder, D. S. & Kalina, M. Impact of on-site care, prehospital time, and level of in-hospital care on survival in severely injured patients. J. Trauma32, 252–261 (1993).

    Article  Google Scholar 

  11. Hulse, E. J. & Thomas, G. O. Vascular access on the 21st century military battlefield. J. R. Army Med. Corps156, 285–390 (2010).

    Article  Google Scholar 

  12. Armenteros-Yeguas, V. et al. Prevalence of difficult venous access and associated risk factors in highly complex hospitalised patients. J. Clin. Nurs.26, 4267–4275 (2017).

    Article  Google Scholar 

  13. Lamperti, M. & Pittiruti, M. II. Difficult peripheral veins: turn on the lights. Br. J. Anaesth.110, 888–891 (2013).

    Article  Google Scholar 

  14. Rauch, D. et al. Peripheral difficult venous access in children. Clin. Pediatr.(Phila)48, 895–901 (2009).

    Article  Google Scholar 

  15. Ortiz, D. et al. Access site complications after peripheral vascular interventions: incidence, predictors, and outcomes. Circ. Cardiovasc. Interv.7, 821–828 (2014).

    Article  Google Scholar 

  16. Lee, S. et al. A transparent bending-insensitive pressure sensor. Nat. Nanotechnol.11, 472–478 (2016).

    Article  Google Scholar 

  17. Chen, Z. et al. Non-invasive multimodal optical coherence and photoacoustic tomography for human skin imaging. Sci. Rep.7, 17975 (2017).

    Article  Google Scholar 

  18. Kolkman, R. G. M., Hondebrink, E., Steenbergen, W. & De Mul, F. F. M. In vivo photoacoustic imaging of blood vessels using an extreme-narrow aperture sensor. IEEE J. Sel. Top. Quantum Electron.9, 343–346 (2003).

    Article  Google Scholar 

  19. Matsumoto, Y. et al. Label-free photoacoustic imaging of human palmar vessels: a structural morphological analysis. Sci. Rep.8, 786 (2018).

    Article  Google Scholar 

  20. Meiburger, K. M. et al. Skeletonization algorithm-based blood vessel quantification using in vivo 3D photoacoustic imaging. Phys. Med. Biol.61, 7994–8009 (2016).

    Article  Google Scholar 

  21. Bashkatov, A. N., Genina, E. A., Kochubey, V. I. & Tuchin, V. V. Optical properties of human skin, subcutaneous and mucous tissues in the wavelength range from 400 to 2,000 nm. J. Phys. D38, 2543–2555 (2005).

    Article  Google Scholar 

  22. Paquit, V. C., Tobin, K. W., Price, J. R. & Mèriaudeau, F. 3D and multispectral imaging for subcutaneous veins detection. Opt. Express17, 11360–11365 (2009).

    Article  Google Scholar 

  23. Lamperti, M. et al. International evidence-based recommendations on ultrasound-guided vascular access. Intensive Care Med.38, 1105–1117 (2012).

    Article  Google Scholar 

  24. Egan, G. et al. Ultrasound guidance for difficult peripheral venous access: systematic review and meta-analysis. Emerg. Med. J.30, 521–526 (2013).

    Article  Google Scholar 

  25. Seto, A. H. et al. Real-time ultrasound guidance facilitates femoral arterial access and reduces vascular complications: FAUST (Femoral Arterial Access with Ultrasound Trial). JACC Cardiovasc. Interv.3, 751–758 (2010).

    Article  Google Scholar 

  26. Stolz, L. A., Stolz, U., Howe, C., Farrell, I. J. & Adhikari, S. Ultrasound-guided peripheral venous access: a meta-analysis and systematic review. J. Vasc. Access16, 321–326 (2015).

    Article  Google Scholar 

  27. Antoniou, G. A., Riga, C. V., Mayer, E. K., Cheshire, N. J. W. & Bicknell, C. D. Clinical applications of robotic technology in vascular and endovascular surgery. J. Vasc. Surgery53, 493–499 (2011).

    Article  Google Scholar 

  28. Zivanovic, A. & Davies, B. L. A robotic system for blood sampling. IEEE Trans. Inf. Technol. Biomed.4, 8–14 (2000).

    Article  Google Scholar 

  29. Cheng, Z. et al. A hand-held robotic device for peripheral intravenous catheterization. Proc. Inst. Mech. Eng. H J. Eng. Med.231, 1165–1177 (2017).

    Article  Google Scholar 

  30. Kobayashi, Y. et al. Use of puncture force measurement to investigate the conditions of blood vessel needle insertion. Med. Eng. Phys.35, 684–689 (2013).

    Article  Google Scholar 

  31. Kobayashi, Y. et al. Preliminary in vivo evaluation of a needle insertion manipulator for central venous catheterization. Robomech. J.1, 1–18 (2014).

    Article  Google Scholar 

  32. Hong, J., Dohi, T., Hashizume, M., Konishi, K. & Hata, N. An ultrasound-driven needle-insertion robot for percutaneous cholecystostomy. Phys. Med. Biol.49, 441–455 (2004).

    Article  Google Scholar 

  33. de Boer, T., Steinbuch, M., Neerken, S. & Kharin, A. Laboratory study on needle–tissue interaction: toward the development of an instrument for automated venipuncture. J. Mech. Med. Biol.7, 325–335 (2007).

    Article  Google Scholar 

  34. Carvalho, P., Kesari, A., Weaver, S., Flaherty, P. & Fischer, G. Robotic assistive device for phlebotomy. In Proc. ASME 2015 International Design and Engineering Technical Conferences & Computers and Information in Engineering Conference Vol. 3, 47620 (ASME, 2015).

  35. Brewer, R. Improving Peripheral IV Catheterization Through Robotics—From Simple Assistive Devices to a Fully Autonomous System (Stanford University, 2015).

  36. Chen, A. I., Nikitczuk, K., Nikitczuk, J., Maguire, T. J. & Yarmush, M. L. Portable robot for autonomous venipuncture using 3D near infrared image guidance. Technology1, 72–87 (2013).

    Article  Google Scholar 

  37. Harris, R., Mygatt, J. & Harris, S. System and methods for autonomous intravenous needle insertion. US patent 9,364,171 (2011).

  38. Balter, M. L., Chen, A. I., Maguire, T. J. & Yarmush, M. L. Adaptive kinematic control of a robotic venipuncture device based on stereo vision, ultrasound, and force guidance. IEEE Trans. Ind. Electron.64, 1626–1635 (2017).

    Article  Google Scholar 

  39. Lecun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature521, 436–444 (2015).

    Article  Google Scholar 

  40. Long, J., Shelhamer, E. & Darrell, T. Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell.39, 640–651 (2017).

    Article  Google Scholar 

  41. Valipour, S., Siam, M., Jagersand, M. & Ray, N. Recurrent fully convolutional networks for video segmentation. In Proc. 2017 IEEE Conference on Applications of Computer Vision 26–36 (IEEE, 2017).

  42. Bjærum, S., Torp, H. & Kristoffersen, K. Clutter filter design for ultrasound color flow imaging. IEEE Trans. Ultrason. Ferroelectr. Freq. Control49, 204–216 (2002).

    Article  Google Scholar 

  43. Chen, A. I. et al. Multilayered tissue mimicking skin and vessel phantoms with tunable mechanical, optical, and acoustic properties. Med. Phys.43, 3117–3131 (2016).

    Article  Google Scholar 

  44. Lewis, G. C., Crapo, S. A. & Williams, J. G. Critical skills and procedures in emergency medicine: vascular access skills and procedures. Emerg. Med. Clin. North Am.31, 59–86 (2013).

    Article  Google Scholar 

  45. Galena, H. J. Complications occurring from diagnostic venipuncture. J. Fam. Pract.34, 582–584 (1992).

    Google Scholar 

  46. Mnih, V. et al. Human-level control through deep reinforcement learning. Nature518, 529–533 (2015).

    Article  Google Scholar 

  47. Nguyen, N. D., Nguyen, T., Saeid, N., Bhatti, A. & Guest, G. Manipulating soft tissues by deep reinforcement learning for autonomous robotic surgery. In Proc. 2019 IEEE International Systems Conference 1–7 (IEEE, 2019).

  48. Bullitt, E., Muller, K. E., Jung, I., Lin, W. & Aylward, S. Analyzing attributes of vessel populations. Med. Image Anal.9, 39–49 (2005).

    Article  Google Scholar 

  49. Balter, M. L. et al. Automated end-to-end blood testing at the point-of-care: integration of robotic phlebotomy with downstream sample processing. Technology6, 59–66 (2018).

    Article  Google Scholar 

  50. Drain, P. K. et al. Diagnostic point-of-care tests in resource-limited settings. Lancet Infect. Dis.14, 239–249 (2014).

    Article  Google Scholar 

  51. Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. Med. Image Comput. Comput. Interv.9351, 234–241 (2015).

    Google Scholar 

  52. Ioffe, S. & Szegedy, C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In Proc. 32nd International Conference on Machine Learning Vol. 37, 448–456 (JMLR, 2015).

  53. He, K., Zhang, X., Ren, S. & Sun, J. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In Proc. 2015 IEEE International Conference on Computer Vision 1026–1034 (IEEE, 2015).

  54. He, K., Zhang, X., Ren, S. & Sun, J. Identity mappings in deep residual networks. In Proc. 14th European Conference on Computer Vision 630–645 (Springer, 2016).

  55. Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature323, 533–536 (1986).

    Article  MATH  Google Scholar 

  56. Shi, X. et al. Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In Proc. 28th International Conference on Neural Information Processing Systems 802–810 (MIT Press, 2015).

  57. Cho, K. et al. Learning phrase representations using RNN encoder–decoder for statistical machine translation. In Proc. 2014 Conference on Empirical Methods in Natural Language Processing 1724–1734 (ACL, 2014).

  58. Chung, J., Çağlar, G., Cho, K. & Bengio, Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. Preprint at https://arxiv.org/abs/1412.3555 (2014).

  59. Chen, A. I., Balter, M. L., Maguire, T. J. & Yarmush, M. L. 3D near infrared and ultrasound imaging of peripheral blood vessels for real-time localization and needle guidance. In Medical Image Computing and Computer-Assisted Interventations Vol. 9902, 130–137 (Springer, 2016).

  60. Zhao, H., Gallo, O., Frosio, I. & Kautz, J. Loss functions for image restoration with neural networks. IEEE Trans. Comput. Imaging3, 47–57 (2017).

    Article  Google Scholar 

  61. Sudre, C. H., Li, W., Vercauteren, T., Ourselin, S. & Jorge Cardoso, M. in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (eds Cardoso M. et al.) 240–248 (Springer, 2017).

  62. Chambolle, A., Caselles, V., Novaga, M., Cremers, D. & Pock, T. in Theoretical Foundations and Numerical Methods for Sparse Recovery (ed. Fornasier, M) 263–340 (2010).

  63. Říha, K. Artery Databases (Brno University of Technology, 2014); http://splab.cz/wp-content/uploads/2014/05/ARTERY_TRANSVERSAL.zip

  64. Zukal, M., Beneš, R., Číka, I. P. & Říha, K. Ultrasound Image Database (Brno University of Technology, 2013); http://splab.cz/wp-content/uploads/2013/11/us_images.zip

  65. Le Guennec, A., Malinowski, S. & Tavenard, R. Data augmentation for time series classification using convolutional neural networks. Preprint at https://halshs.archives-ouvertes.fr/halshs-01357973 (2016).

  66. Kingma, D. P. & Ba, J. L. Adam: a method for stochastic optimization. In Proc. 3rd International Conference on Learning Representations (ICLR, 2015).

  67. Ng, A. Y. Feature selection, L1 vs. L2 regularization, and rotational invariance. In Proc. 21st International Conference on Machine Learning 78–85 (ACM, 2004).

  68. Abadi, M. et al. TensorFlow: a system for large-scale machine learning. In Proc. 12th USENIX Conference on Operating Systems Design and Implementation Vol.16, 265–283 (USENIX, 2016).

  69. Liu, B., Zhang, F. & Qu, X. A method for improving the pose accuracy of a robot manipulator based on multi-sensor combined measurement and data fusion. Sensors15, 7933–7952 (2015).

    Article  Google Scholar 

  70. Bradski, G. The OpenCV Library. Dr Dobbs J. Softw. Tools120, 122–125 (2000).

    Google Scholar 

  71. Rusu, R. B. & Cousins, S. 3D is here: Point Cloud Library (PCL). In Proc. 2011 IEEE International Conference on Robotics and Automation 1–4 (IEEE, 2011).

  72. Yoo, T. S. et al. Engineering and algorithm design for an image processing API: a technical report on ITK—The Insight Toolkit. Stud. Health Technol. Inform.85, 586–592 (2002).

    Google Scholar 

  73. Myronenko, A. & Song, X. Point set registration: coherent point drifts. IEEE Trans. Pattern Anal. Mach. Intell.32, 2262–2275 (2010).

    Article  Google Scholar 

  74. Gunst, R. F. & Mason, R. L. Fractional factorial design. WIREs Comput. Stat.1, 234–244 (2009).

    Article  Google Scholar 

Download references

Acknowledgements

We thank J. Leiphemer and N. DeMaio for their assistance and support in designing and implementing the device, E. Pantin and A. Davidovich for support in the human imaging studies and phantom studies, including review of imaging data and overall clinical guidance, E. Yurkow, D. Adler, M. Lo and G. Yarmush for assistance in the animal studies, and B. Lee for code used in our deep learning approach. Research reported in this publication was supported by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under awards R01-EB020036 and T32-GM008339. This work was also supported by a National Institutes of Health Ruth L. Kirschstein Fellowship F31-EB018191 awarded to A.I.C. and a National Science Foundation Fellowship DGE-0937373 awarded to M.L.B. The authors acknowledge additional support from the Rutgers University School of Engineering, Rutgers University Department of Biomedical Engineering and the Robert Wood Johnson University Hospital.

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA

    Alvin I. Chen, Max L. Balter, Timothy J. Maguire & Martin L. Yarmush

Authors
  1. Alvin I. Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Max L. Balter
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Timothy J. Maguire
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Martin L. Yarmush
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

A.I.C. and M.L.B. designed the system, developed the algorithms and annotation software, and implemented the software and hardware. A.I.C. led execution of the imaging, in vitro and in vivo studies and analysed the primary data. M.L.Y. and T.J.M. provided the general direction for the project and provided valuable comments on the system design and manuscript. Correspondence and requests for materials should be addressed to A.I.C.

Corresponding author

Correspondence to Alvin I. Chen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary infomation

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, A.I., Balter, M.L., Maguire, T.J. et al. Deep learning robotic guidance for autonomous vascular access. Nat Mach Intell 2, 104–115 (2020). https://doi.org/10.1038/s42256-020-0148-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42256-020-0148-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing