Autonomous Tissue Scanning under Free-Form Motion for Intraoperative Tissue Characterisation

In Minimally Invasive Surgery (MIS), tissue scanning with imaging probes is required for subsurface visualisation to characterise the state of the tissue. However, scanning of large tissue surfaces in the presence of deformation is a challenging task for the surgeon. Recently, robot-assisted local tissue scanning has been investigated for motion stabilisation of imaging probes to facilitate the capturing of good quality images and reduce the surgeon's cognitive load. Nonetheless, these approaches require the tissue surface to be static or deform with periodic motion. To eliminate these assumptions, we propose a visual servoing framework for autonomous tissue scanning, able to deal with free-form tissue deformation. The 3D structure of the surgical scene is recovered and a feature-based method is proposed to estimate the motion of the tissue in real-time. A desired scanning trajectory is manually defined on a reference frame and continuously updated using projective geometry to follow the tissue motion and control the movement of the robotic arm. The advantage of the proposed method is that it does not require the learning of the tissue motion prior to scanning and can deal with free-form deformation. We deployed this framework on the da Vinci surgical robot using the da Vinci Research Kit (dVRK) for Ultrasound tissue scanning. Since the framework does not rely on information from the Ultrasound data, it can be easily extended to other probe-based imaging modalities.Comment: 7 pages, 5 figures, ICRA 202

Regularising disparity estimation via multi task learning with structured light reconstruction

3D reconstruction is a useful tool for surgical planning and guidance. However, the lack of available medical data stunts research and development in this field, as supervised deep learning methods for accurate disparity estimation rely heavily on large datasets containing ground truth information. Alternative approaches to supervision have been explored, such as self-supervision, which can reduce or remove entirely the need for ground truth. However, no proposed alternatives have demonstrated performance capabilities close to what would be expected from a supervised setup. This work aims to alleviate this issue. In this paper, we investigate the learning of structured light projections to enhance the development of direct disparity estimation networks. We show for the first time that it is possible to accurately learn the projection of structured light on a scene, implicitly learning disparity. Secondly, we \textcolor{black}{explore the use of a multi task learning (MTL) framework for the joint training of structured light and disparity. We present results which show that MTL with structured light improves disparity training; without increasing the number of model parameters. Our MTL setup outperformed the single task learning (STL) network in every validation test. Notably, in the medical generalisation test, the STL error was 1.4 times worse than that of the best MTL performance. The benefit of using MTL is emphasised when the training data is limited.} A dataset containing stereoscopic images, disparity maps and structured light projections on medical phantoms and ex vivo tissue was created for evaluation together with virtual scenes. This dataset will be made publicly available in the future

Caveats on the first-generation da Vinci Research Kit: latent technical constraints and essential calibrations

Telesurgical robotic systems provide a well established form of assistance in the operating theater, with evidence of growing uptake in recent years. Until now, the da Vinci surgical system (Intuitive Surgical Inc, Sunnyvale, California) has been the most widely adopted robot of this kind, with more than 6,700 systems in current clinical use worldwide [1]. To accelerate research on robotic-assisted surgery, the retired first-generation da Vinci robots have been redeployed for research use as "da Vinci Research Kits" (dVRKs), which have been distributed to research institutions around the world to support both training and research in the sector. In the past ten years, a great amount of research on the dVRK has been carried out across a vast range of research topics. During this extensive and distributed process, common technical issues have been identified that are buried deep within the dVRK research and development architecture, and were found to be common among dVRK user feedback, regardless of the breadth and disparity of research directions identified. This paper gathers and analyzes the most significant of these, with a focus on the technical constraints of the first-generation dVRK, which both existing and prospective users should be aware of before embarking onto dVRK-related research. The hope is that this review will aid users in identifying and addressing common limitations of the systems promptly, thus helping to accelerate progress in the field.Comment: 15 pages, 7 figure

Autonomous tissue scanning for guiding a tumour resection

Robots have become the top-choice technology for Minimally Invasive Surgery (MIS) procedures and due to the rapid advancement of these robotic platforms, it is expected that these robots will play an essential role in the future of surgery. These surgical robots are expected to evolve through higher levels of automation, as these platforms become more sophisticated and capable. One important feature to be added to these robots is the ability to scan tissue for in situ characterization to guide tumour resection. The main advantage of doing tissue scanning autonomously is that it reduces surgical workload, allowing the surgeon to focus on more crucial tasks while the robot scans the tissue automatically. In this thesis, an autonomous tissue scanning framework is presented which allows the robot to capture ultrasound images of the target scanning region. Throughout this thesis, different works contribute to my tissue scanning framework. I have written the first technical review of the da Vinci (dVRK) surgical robot, which describes the essential calibrations required to acquire good-quality data; I created simulations of robotic surgeries to test algorithms before deploying them on the real robot; I designed a cylindrical marker to estimate the pose of surgical instruments. I have found that this cylindrical marker is a practical tool for the hand-eye calibration of the robot; I have created an autonomous scanning framework which improves the previous works by being able to follow moving tissue without assuming periodic breathing motions; Finally, I organized a soft-tissue tracking challenge, which allows researchers to develop tissue trackers using the benchmarking tool and the dataset that I have created.Open Acces

Venous thromboembolism risk and prophylaxis in hospitalised medically ill patients The ENDORSE Global Survey

Limited data are available regarding the risk for venous thromboembolism (VIE) and VIE prophylaxis use in hospitalised medically ill patients. We analysed data from the global ENDORSE survey to evaluate VTE risk and prophylaxis use in this population according to diagnosis, baseline characteristics, and country. Data on patient characteristics, VIE risk, and prophylaxis use were abstracted from hospital charts. VTE risk and prophylaxis use were evaluated according to the 2004 American College of Chest Physicians (ACCP) guidelines. Multivariable analysis was performed to identify factors associated with use of ACCP-recommended prophylaxis. Data were evaluated for 37,356 hospitalised medical patients across 32 countries. VIE risk varied according to medical diagnosis, from 31.2% of patients with gastrointestinal/hepatobiliary diseases to 100% of patients with acute heart failure, active noninfectious respiratory disease, or pulmonary infection (global rate, 41.5%). Among those at risk for VTE, ACCP-recommended prophylaxis was used in 24.4% haemorrhagic stroke patients and 40-45% of cardiopulmonary disease patients (global rate, 39.5%). Large differences in prophylaxis use were observed among countries. Markers of disease severity, including central venous catheters, mechanical ventilation, and admission to intensive care units, were strongly associated with use of ACCP-recommended prophylaxis. In conclusion, VIE risk varies according to medical diagnosis. Less than 40% of at-risk hospitalised medical patients receive ACCP-recommended prophylaxis. Prophylaxis use appears to be associated with disease severity rather than medical diagnosis. These data support the necessity to improve implementation of available guidelines for evaluating VIE risk and providing prophylaxis to hospitalised medical patients

Venous Thromboembolism Risk and Prophylaxis in the Acute Care Hospital Setting (ENDORSE Survey) Findings in Surgical Patients

Objective: To evaluate venous thromboembolism (VTE) risk in patients who underwent a major operation, including the use of, and factors influencing, American College of Chest Physicians-recommended types of VTE prophylaxis

Delayed colorectal cancer care during covid-19 pandemic (decor-19). Global perspective from an international survey

Background The widespread nature of coronavirus disease 2019 (COVID-19) has been unprecedented. We sought to analyze its global impact with a survey on colorectal cancer (CRC) care during the pandemic. Methods The impact of COVID-19 on preoperative assessment, elective surgery, and postoperative management of CRC patients was explored by a 35-item survey, which was distributed worldwide to members of surgical societies with an interest in CRC care. Respondents were divided into two comparator groups: 1) ‘delay’ group: CRC care affected by the pandemic; 2) ‘no delay’ group: unaltered CRC practice. Results A total of 1,051 respondents from 84 countries completed the survey. No substantial differences in demographics were found between the ‘delay’ (745, 70.9%) and ‘no delay’ (306, 29.1%) groups. Suspension of multidisciplinary team meetings, staff members quarantined or relocated to COVID-19 units, units fully dedicated to COVID-19 care, personal protective equipment not readily available were factors significantly associated to delays in endoscopy, radiology, surgery, histopathology and prolonged chemoradiation therapy-to-surgery intervals. In the ‘delay’ group, 48.9% of respondents reported a change in the initial surgical plan and 26.3% reported a shift from elective to urgent operations. Recovery of CRC care was associated with the status of the outbreak. Practicing in COVID-free units, no change in operative slots and staff members not relocated to COVID-19 units were statistically associated with unaltered CRC care in the ‘no delay’ group, while the geographical distribution was not. Conclusions Global changes in diagnostic and therapeutic CRC practices were evident. Changes were associated with differences in health-care delivery systems, hospital’s preparedness, resources availability, and local COVID-19 prevalence rather than geographical factors. Strategic planning is required to optimize CRC care