Skip to main content Accessibility help
×
Hostname: page-component-857557d7f7-9f75d Total loading time: 0 Render date: 2025-12-03T10:06:34.890Z Has data issue: false hasContentIssue false

Chapter 13 - Robotics: Refined Assistance and Enhanced Minimalism

from Section IV - The Surgeon’s Armamentarium

Published online by Cambridge University Press:  29 November 2025

Benjamin Hartley
Affiliation:
Weill Cornell Medical Center
Philip E. Stieg
Affiliation:
Weill Cornell Medical College
Rohan Ramakrishna
Affiliation:
Weill Cornell Medical College
Michael L. J. Apuzzo
Affiliation:
Adjunct of Yale Medical School and Weill Cornell Medical College
Get access

Summary

Intra-operative pathology interrogation and cellular-level visualization remain significant challenges in surgery. Advances in genetic and molecular disease understanding, combined with biomedical innovation, suggest a need for an intelligent robotic surgical system. CellARM represents a state-of-the-art robotic device designed for minimally invasive, precise surgery. Equipped with a dexterous robotic arm, endoscopic camera, and sensors, CellARM offers real-time data and feedback, enhancing surgeons’ capabilities. It integrates machine learning, allowing the system to learn and improve from each procedure. By democratizing access to advanced surgical tools and data, CellARM promises to level the playing field in healthcare, making high-quality surgery accessible globally. This technology aims to standardize care, improve patient outcomes, and reduce costs by providing detailed, real-time information on patient tissues and cells during surgery.

Information

Type
Chapter
Information
Neurosurgery
Beyond the Cutting Edge
, pp. 258 - 268
Publisher: Cambridge University Press
Print publication year: 2025

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

Tang, B, Hanna, GB, Cuschieri, A. Analysis of errors enacted by surgical trainees during skills training courses. Surgery. 138(1):1420, 2005.10.1016/j.surg.2005.02.014CrossRefGoogle ScholarPubMed
Sarker, SK, Vincent, C. Erros in Surgery. International Journal of Surgery. 3:7581, 2005.10.1016/j.ijsu.2005.04.003CrossRefGoogle Scholar
Sutherland, GR, Louw, D, McBeth, P, et al. MRI Compatible Neurosurgical Robot System. U.S. Patent No. 7155316 B2. December 26, 2006. Expiry date December 26, 2026.Google Scholar
Sutherland, GR, Lama, S, Gan, LS, et al. Merging machines with microsurgery: clinical experience with neuroArm. Journal of Neurosurgery. 118:521529, 2013.10.3171/2012.11.JNS12877CrossRefGoogle ScholarPubMed
Armstrong, T, Tu, D, Frischknecht, A, et al. Standardization of surgical procedures for identifying best practices and training. 41 Suppl 1:4673–9, 2012. doi:10.3233/WOR-2012-0108-4673.Google ScholarPubMed
Buis, DR, Idema, S, Feller, R et al. Standardization of surgical procedures: beyond checklists? World Neurosurgery 82:e376-e377, 2014.10.1016/j.wneu.2012.04.028CrossRefGoogle ScholarPubMed
Committee on Patient Safety and Quality Improvement. Clinical guidelines and standardization of practice to improve outcomes. ACOG Interim Update. 792, 134:e122–5, 2019. https://doi.org/10.1016/j.wneu.2012.04.028Google Scholar
Likosky, DS, Goldberg, JB, DiScipio, AW, et al. Variability in surgeons’ perioperative practices may influence the incidence of low-output failure after coronary artery bypass grafting surgery. AHA Circulation: Cardiovascular Quality and Outcomes. 5:638–44, 2012.Google ScholarPubMed
Kurmann, A, Tschan, F, Semmer, NK, et al. Human factors in the operating room – the surgeons’ view. Trends in Anesthesia and Critical Care. 2:224–7, 2012.Google Scholar
Deweerdt, S. Below the surface. Nature, 561:S54–5, 2018.Google Scholar
Rheinbay, E. The genomic landscape of advanced cancer. Nature Cancer, 1:372–3, 2020.10.1038/s43018-020-0057-zCrossRefGoogle ScholarPubMed
Bernards, R, Jaffee, E, Joyce, JA, et al. A roadmap for the next decade in cancer research. Nature Cancer, 1:1217, 2020.10.1038/s43018-019-0015-9CrossRefGoogle ScholarPubMed
Patel, ZM, Fernandez-Miranda, J, Hwang, PH, et al. Precautions for the endoscopic transnasal skull base surgery during the Covid-19 pandemic. 87:E66–7, 2020. doi:10.1093/neuros/nyaa125.Google ScholarPubMed
Den Bos J, Van, Rustagi, K, Gray, T, et al. The 17.1 billion problem: the annual cost of measurable medical errors. Health Affairs. 30:596603, 2011.10.1377/hlthaff.2011.0084CrossRefGoogle ScholarPubMed
Spearling, SM. Materials issues in microelectromechanical systems (MEMS). Acta Materialia. 48:179–96, 2000.Google Scholar
Fischer, AC, Forsberg, F, Lapisa, M, et al. Integrating MEMS and ICs. Nature Microsystems & Nanoengineering. 15005:116, 2015.Google Scholar
Novak, R, Ingram, M, Marquez, S, et al. Robotic fluidic coupling and interrogation of multiple vascularized organ chips. Nature Biomedical Engineering. 4:407–20, 2020.10.1038/s41551-019-0497-xCrossRefGoogle ScholarPubMed
Tomanek, B, Iqbal, U, Blasiak, B, et al. Evaluation of brain tumor vessels specific contrast agents for glioblastoma imaging. Neurology and Oncology. 14:5363, 2012.10.1093/neuonc/nor183CrossRefGoogle ScholarPubMed
Iqbal, U, Trojahn, U, Albaghdadi, H, et al. Kinetic analysis of novel mono- and multivalent VHH-fragments and their application for molecular imaging of brain tumors. British Journal of Pharmacology. 160:1016–28, 2010.10.1111/j.1476-5381.2010.00742.xCrossRefGoogle Scholar
Sutherland, GR, Arbabi-Ghahroudi, M, Lama, S. Anti-tau Antibody and Uses thereof. US Provisional Application No. 62/173,452, Date of Filing: June 10, 2015; Final Submission: October 10, 2015.Google Scholar
Nelson, SL, Proctor, DT, Ghasemloonia, A, et al. Vibrational profiling of brain tumours. Theranostics. 7: 2417–30, 2017.10.7150/thno.19172CrossRefGoogle Scholar
Sutherland, GR, Wolfsberger, S, Lama, S, et al. The evolution of neuroArm. Neurosurgery. 72:A27–32, 2013.10.1227/NEU.0b013e318270da19CrossRefGoogle ScholarPubMed
Hoshyarmanesh, H, Zareinia, K, Lama, S, et al. Microsurgery-specific Haptic Hand Controller. US Provisional Application No. 62611024, Date of Filing: Dec 28, 2017.Google Scholar
Torabi, A, Zareinia, K, Sutherland, GR, et al. Dynamic reconfiguration of redundant haptic interfaces for rendering soft and hard contacts. IEEE Transactions on Haptics, 2020 doi:10.1109/TOH.2020.2988495.CrossRefGoogle Scholar
Baghdadi, A, Hoshyarmanesh, H, de Lotbiniere-Bassett, MP, et al. Data analytics interrogates robotic surgical performance using microsurgery-specific haptic device. Expert Review in Medical Devices. 2020. doi:0.1080/17434440.2020.1782736.Google Scholar
Shi, Y, Polat, B, Huang, Q et al., Nanoscale fiber-optic force sensors for mechanical probing at the molecular and cellular level. Nature Protocols. 13:2714–39, 2018.10.1038/s41596-018-0059-9CrossRefGoogle ScholarPubMed
He, X, Handa, J, Gehlbach, P et al., A sub-millimetric 3-DOF force sensing instrument with integrated fiber Bragg grating for retinal microsurgery. IEEE Transactions in Biomedical Engineering. 61:522–34, 2014.Google Scholar
Sugiyama, T, Lama, S, Gan, LS, et al. Forces of tool-tissue interaction to assess surgical skill level. JAMA Sugery. 153:234–42, 2018.Google ScholarPubMed
Sugiyama, T, Gan, LS, Zareinia, K, et al. Tool-tissue interaction forces in brain arteriovenous malformation surgery. World Neurosurgery. 102:221–8, 2017.10.1016/j.wneu.2017.03.006CrossRefGoogle ScholarPubMed
Zareinia, K, Maddahi, Y, Gan, LS, et al. A force-sensing bipolar forceps to quantify tool-tissue interaction forces in microsurgery. IEEE/ASME Transactions on Mechatronics. 21:2365–77, 2016.10.1109/TMECH.2016.2563384CrossRefGoogle Scholar
Greer, AD, Newhook, P, Sutherland, GR. Human-machine interface for robotic surgery and stereotaxy. IEEE/ ASME Transactions on MRI Compatible Mechatronic Systems. 13: 355–61, 2008.Google Scholar
Guo, E, Gupta, M, Sinha, S, et al. neuroGPT-X: Towards a Clinic-ready Large Language Model. JNS https://doi.org/10.3171/2023.7.JNS23573.CrossRefGoogle Scholar
Riva-Cambrin, H, Singh, R, Lama, S, Sutherland, GR. Surgical System Leveraging Language Models. US Appln. No. 18/465,042. Filed Sept. 11, 2023.Google Scholar
Neftci, EO, Averbeck, BB. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence. 1:133–43, 2019. doi.org/10.1038/s42256-019-0025-4.CrossRefGoogle Scholar
Bishop, CM. Pattern Recognition and Machine Learning. Springer. ISBN 9788132209065, 2013.Google Scholar
Sejnowski, TJ. The unreasonable effectiveness of deep learning in artificial intelligence. PNAS. 2020, https://doi.org/10.1073/pnas.1907373117.CrossRefGoogle Scholar
Lee, JK, Choi, MJ. Robust inertial measurement unit-based attitude determination Kalman Filter for Kinematically Constrained Links. Sensors (Basel). 19:768, 111, 2019. doi:10.3390/s19040768.Google ScholarPubMed

Accessibility standard: Inaccessible, or known limited accessibility

Why this information is here

This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.

Accessibility Information

The PDF of this book is known to have missing or limited accessibility features. We may be reviewing its accessibility for future improvement, but final compliance is not yet assured and may be subject to legal exceptions. If you have any questions, please contact accessibility@cambridge.org.

Content Navigation

Table of contents navigation
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Index navigation
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.

Reading Order & Textual Equivalents

Single logical reading order
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
Short alternative textual descriptions
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.

Visual Accessibility

Use of colour is not sole means of conveying information
You will still understand key ideas or prompts without relying solely on colour, which is especially helpful if you have colour vision deficiencies.

Structural and Technical Features

ARIA roles provided
You gain clarity from ARIA (Accessible Rich Internet Applications) roles and attributes, as they help assistive technologies interpret how each part of the content functions.

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×