Research in surgical robotics has an established track record at Imperial College, and a number of research and commercial surgical robot platforms have been developed over the years. The Hamlyn Centre is a champion for technological innovation and clinical adoption of robotic, minimally invasive surgery. We work in partnership with major industrial leaders in medical devices and surgical robots, as well as developing our own platforms such as the i-Snake® and Micro-IGES platforms. The Da Vinci surgical robot is used extensively for endoscopic radical prostatectomy, hiatal hernia surgery, and low pelvic and rectal surgery, and in 2003, St Mary’s Hospital carried out its first Totally Endoscopic Robotic Coronary Artery Bypass (TECAB).

The major focus of the Hamlyn Centre is to develop robotic technologies that will transform conventional minimally invasive surgery, explore new ways of empowering robots with human intelligence, and develop[ing miniature 'microbots' with integrated sensing and imaging for targeted therapy and treatment. We work closely with both industrial and academic partners in open platforms such as the DVRK, RAVEN and KUKA. The Centre also has the important mission of driving down costs associated with robotic surgery in order to make the technology more accessible, portable, and affordable. This will allow it to be fully integrated with normal surgical workflows so as to benefit a much wider patient population.

The Hamlyn Centre currently chairs the UK Robotics and Autonomous Systems (UK-RAS) Network. The mission of the Network is to to provide academic leadership in Robotics and Autonomous Systems (RAS), expand collaboration with industry and integrate and coordinate activities across the UK Engineering and Physical Sciences Research Council (EPSRC) funded RAS capital facilities and Centres for Doctoral Training (CDTs).


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  • Journal article
    Elson DS, Cleary K, Dupont P, Merrifield R, Riviere Cet al., 2018,

    Medical robotics

    , Annals of Biomedical Engineering, Vol: 46, Pages: 1433-1436, ISSN: 0090-6964

    Medical robotics encompasses surgical, therapeutic and rehabilitative devices that are changing medicine and healthcare. Although the field of medical robotics predates Intuitive’s da Vinci by more than a decade, it was the clinical and commercial achievements of that system that brought medical robotics to widespread patient and public attention. It is now more than 15 years since the robot began to be used for laparoscopic prostatectomy.1 Since then, research in the field has advanced tremendously due to various technological breakthroughs. Over the last few years, there has been a surge in commercial activities in medical robotics, led both by traditional medical device and technology companies as well as new start-ups. This special issue has been commissioned to capture some of the latest research being carried out by these multidisciplinary bioengineering teams and to showcase how some of these advances can impact clinical care.

  • Conference paper
    Triantafyllou P, Wisanuvej P, Giannarou S, Liu J, Yang G-Zet al., 2018,

    A Framework for Sensorless Tissue Motion Tracking in Robotic Endomicroscopy Scanning

    , IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE COMPUTER SOC, Pages: 2694-2699, ISSN: 1050-4729
  • Journal article
    Li Y, Charalampaki P, Liu Y, Yang G-Z, Giannarou Set al., 2018,

    Context aware decision support in neurosurgical oncology based on an efficient classification of endomicroscopic data

    , International Journal of Computer Assisted Radiology and Surgery, Vol: 13, Pages: 1187-1199, ISSN: 1861-6429

    Purpose: Probe-based confocal laser endomicroscopy (pCLE) enables in vivo, in situ tissue characterisation without changesin the surgical setting and simplifies the oncological surgical workflow. The potential of this technique in identifying residualcancer tissue and improving resection rates of brain tumours has been recently verified in pilot studies. The interpretation ofendomicroscopic information is challenging, particularly for surgeons who do not themselves routinely review histopathology.Also, the diagnosis can be examiner-dependent, leading to considerable inter-observer variability. Therefore, automatic tissuecharacterisation with pCLE would support the surgeon in establishing diagnosis as well as guide robot-assisted interventionprocedures.Methods: The aim of this work is to propose a deep learning-based framework for brain tissue characterisation for contextaware diagnosis support in neurosurgical oncology. An efficient representation of the context information of pCLE datais presented by exploring state-of-the-art CNN models with different tuning configurations. A novel video classificationframework based on the combination of convolutional layers with long-range temporal recursion has been proposed to estimatethe probability of each tumour class. The video classification accuracy is compared for different network architectures anddata representation and video segmentation methods.Results: We demonstrate the application of the proposed deep learning framework to classify Glioblastoma and Meningiomabrain tumours based on endomicroscopic data. Results show significant improvement of our proposed image classificationframework over state-of-the-art feature-based methods. The use of video data further improves the classification performance,achieving accuracy equal to 99.49%.Conclusions This work demonstrates that deep learning can provide an efficient representation of pCLE data and accuratelyclassify Glioblastoma and Meningioma tumours. The performance ev

  • Journal article
    Dugasani SR, Paulson B, Ha T, Jung TS, Gnapareddy B, Kim JA, Kim T, Kim HJ, Kim JH, Oh K, Park SHet al., 2018,

    Fabrication and optoelectronic characterisation of lanthanide-and metal-ion-doped DNA thin films

    , JOURNAL OF PHYSICS D-APPLIED PHYSICS, Vol: 51, ISSN: 0022-3727
  • Journal article
    Constantinescu MA, Lee S-L, Ernst S, Hemakom A, Mandic D, Yang G-Zet al., 2018,

    Probabilistic guidance for catheter tip motion in cardiac ablation procedures

    , Medical Image Analysis, Vol: 47, Pages: 1-14, ISSN: 1361-8415

    Radiofrequency catheter ablation is one of the commonly available therapeutic methods for patients suffering from cardiac arrhythmias. The prerequisite of successful ablation is sufficient energy delivery at the target site. However, cardiac and respiratory motion, coupled with endocardial irregularities, can cause catheter drift and dispersion of the radiofrequency energy, thus prolonging procedure time, damaging adjacent tissue, and leading to electrical reconnection of temporarily ablated regions. Therefore, positional accuracy and stability of the catheter tip during energy delivery is of great importance for the outcome of the procedure. This paper presents an analytical scheme for assessing catheter tip stability, whereby a sequence of catheter tip motion recorded at sparse locations on the endocardium is decomposed. The spatial sliding component along the endocardial wall is extracted from the recording and maximal slippage and its associated probability are computed at each mapping point. Finally, a global map is generated, allowing the assessment of potential areas that are compromised by tip slippage. The proposed framework was applied to 40 retrospective studies of congenital heart disease patients and further validated on phantom data and simulations. The results show a good correlation with other intraoperative factors, such as catheter tip contact force amplitude and orientation, and with clinically documented anatomical areas of high catheter tip instability.

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