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Research Progress in AI for Robotic Surgery

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 1142

Special Issue Editors


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Guest Editor
Faculty of Engineering Science, University College London, London, UK
Interests: computer-assisted interventions; surgical data science; surgical robotics; biomedical signal processing; sensor networks

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Guest Editor
Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
Interests: artificial intelligence and its applications on medical image computing; robotic surgical data science

Special Issue Information

Dear Colleagues,

Over the previous three decades, robotic systems have transformed surgical practice, introducing manoeuvrability beyond human dexterity, and enabling surgical tasks to be carried out with enhanced precision and safety. Although many medical interventions are routinely carried out under robotic assistance, with tangible benefits, current platforms are passive machines fully controlled by, and dependent on, the manual skills of a human operator. With advances in digital technology underpinning the recording, curation, and processing of large volumes of data, considerable research efforts now focus on developing the next generation of intelligent surgical robotics, integrating increased levels of autonomy.

The proposed Special Issue will highlight recent progress in the integration of artificial intelligence with surgical robotic systems focusing on innovative methodologies on the application of AI to improve the navigation, efficiency, and overall clinical performance of robotic systems in surgical applications. We invite submissions presenting new and original research on topics including but not limited to the following fields:

  • Intelligent control and visual servoing;
  • Tracking and segmentation of surgical tools;
  • Semantic detection and segmentation of anatomical targets;
  • Depth estimation and 3D reconstruction of the surgical environment;
  • Activity recognition and surgical workflow;
  • Surgical skill analysis;
  • Multimodal sensing and AI.

Dr. Evangelos Mazomenos
Dr. Yueming Jin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

21 pages, 5170 KiB  
Article
A Deep Learning Approach to Classify Surgical Skill in Microsurgery Using Force Data from a Novel Sensorised Surgical Glove
by Jialang Xu, Dimitrios Anastasiou, James Booker, Oliver E. Burton, Hugo Layard Horsfall, Carmen Salvadores Fernandez, Yang Xue, Danail Stoyanov, Manish K. Tiwari, Hani J. Marcus and Evangelos B. Mazomenos
Sensors 2023, 23(21), 8947; https://doi.org/10.3390/s23218947 - 03 Nov 2023
Viewed by 885
Abstract
Microsurgery serves as the foundation for numerous operative procedures. Given its highly technical nature, the assessment of surgical skill becomes an essential component of clinical practice and microsurgery education. The interaction forces between surgical tools and tissues play a pivotal role in surgical [...] Read more.
Microsurgery serves as the foundation for numerous operative procedures. Given its highly technical nature, the assessment of surgical skill becomes an essential component of clinical practice and microsurgery education. The interaction forces between surgical tools and tissues play a pivotal role in surgical success, making them a valuable indicator of surgical skill. In this study, we employ six distinct deep learning architectures (LSTM, GRU, Bi-LSTM, CLDNN, TCN, Transformer) specifically designed for the classification of surgical skill levels. We use force data obtained from a novel sensorized surgical glove utilized during a microsurgical task. To enhance the performance of our models, we propose six data augmentation techniques. The proposed frameworks are accompanied by a comprehensive analysis, both quantitative and qualitative, including experiments conducted with two cross-validation schemes and interpretable visualizations of the network’s decision-making process. Our experimental results show that CLDNN and TCN are the top-performing models, achieving impressive accuracy rates of 96.16% and 97.45%, respectively. This not only underscores the effectiveness of our proposed architectures, but also serves as compelling evidence that the force data obtained through the sensorized surgical glove contains valuable information regarding surgical skill. Full article
(This article belongs to the Special Issue Research Progress in AI for Robotic Surgery)
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