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Steerable catheters are routinely deployed in the treatment of cardiac arrhythmias. During invasive electrophysiology studies, the catheter handle is manipulated by an interventionalist to guide the catheter's distal section toward endocardium for pacing and ablation. Catheter manipulation requires dexterity and experience, and exposes the interventionalist to ionizing radiation. Through the course of this research, a platform was developed to assist and enhance the navigation of the catheter inside the cardiac chambers. This robotic platform replaces the interventionalist's hand in catheter manipulation and provides the option to force the catheter tip in arbitrary directions using a 3D input device or to automatically navigate the catheter to desired positions within a cardiac chamber by commanding the software to do so. To accomplish catheter navigation, the catheter was modeled as a continuum manipulator, and utilizing robot kinematics, catheter tip position control was designed and implemented. An electromagnetic tracking system was utilized to measure the position and orientation of two key points in catheter model, for position feedback to the control system. A software platform was developed to implement the navigation and control strategies and to interface with the robot, the 3D input device and the tracking system. The catheter modeling was validated through in-vitro experiments with a static phantom, and in-vivo experiments on three live swines. The feasibility of automatic navigation was also veri ed by navigating to three landmarks in the beating heart of swine subjects, and comparing their performance with that of an experienced interventionalist using quasi biplane fluoroscopy. The platform realizes automatic, assisted, and motorized navigation under the interventionalist's control, thus reducing the dependence of successful navigation on the dexterity and manipulation skills of the interventionalist, and providing a means to reduce the exposure to X-ray radiation. Upon further development, the platform could be adopted for human deployment.

Steerable catheters are routinely deployed in the treatment of cardiac arrhythmias. During invasive electrophysiology studies, the catheter handle is manipulated by an interventionalist to guide the catheter's distal section toward endocardium for pacing and ablation. Catheter manipulation requires dexterity and experience, and exposes the interventionalist to ionizing radiation. Through the course of this research, a platform was developed to assist and enhance the navigation of the catheter inside the cardiac chambers. This robotic platform replaces the interventionalist's hand in catheter manipulation and provides the option to force the catheter tip in arbitrary directions using a 3D input device or to automatically navigate the catheter to desired positions within a cardiac chamber by commanding the software to do so. To accomplish catheter navigation, the catheter was modeled as a continuum manipulator, and utilizing robot kinematics, catheter tip position control was designed and implemented. An electromagnetic tracking system was utilized to measure the position and orientation of two key points in catheter model, for position feedback to the control system. A software platform was developed to implement the navigation and control strategies and to interface with the robot, the 3D input device and the tracking system. The catheter modeling was validated through in-vitro experiments with a static phantom, and in-vivo experiments on three live swines. The feasibility of automatic navigation was also veri ed by navigating to three landmarks in the beating heart of swine subjects, and comparing their performance with that of an experienced interventionalist using quasi biplane fluoroscopy. The platform realizes automatic, assisted, and motorized navigation under the interventionalist's control, thus reducing the dependence of successful navigation on the dexterity and manipulation skills of the interventionalist, and providing a means to reduce the exposure to X-ray radiation. Upon further development, the platform could be adopted for human deployment.

Yes / To capture stakeholders’ theories concerning how and in what contexts robot-assisted surgery becomes integrated into routine practice. A literature review provided tentative theories that were revised through a realist interview study. Literature-based theories were presented to the interviewees, who were asked to describe to what extent and in what ways those theories reflected their experience. Analysis focused on identifying mechanisms through which robot-assisted surgery becomes integrated into practice and contexts in which those mechanisms are triggered. Nine hospitals in England where robot-assisted surgery is used for colorectal operations. Forty-four theatre staff with experience of robot-assisted colorectal surgery, including surgeons, surgical trainees, theatre nurses, operating department practitioners and anaesthetists. Interviewees emphasised the importance of support from hospital management, team leaders and surgical colleagues. Training together as a team was seen as beneficial, increasing trust in each other’s knowledge and supporting team bonding, in turn leading to improved teamwork. When first introducing robot-assisted surgery, it is beneficial to have a handpicked dedicated robotic team who are able to quickly gain experience and confidence. A suitably sized operating theatre can reduce operation duration and the risk of de-sterilisation. Motivation among team members to persist with robot-assisted surgery can be achieved without involvement in the initial decision to purchase a robot, but training that enables team members to feel confident as they take on the new tasks is essential. We captured accounts of how robot-assisted surgery has been introduced into a range of hospitals. Using a realist approach, we were also able to capture perceptions of the factors that support and constrain the integration of robot-assisted surgery into routine practice. We have translated these into recommendations that can inform future implementations of robot-assisted surgery.

Yes / The implementation of robot-assisted surgery (RAS) can be challenging, with reports of surgical robots being underused. This raises questions about differences compared with open and laparoscopic surgery and how best to integrate RAS into practice. Objectives: To (1) contribute to reporting of the ROLARR (RObotic versus LAparoscopic Resection for Rectal cancer) trial, by investigating how variations in the implementation of RAS and the context impact outcomes; (2) produce guidance on factors likely to facilitate successful implementation; (3) produce guidance on how to ensure effective teamwork; and (4) provide data to inform the development of tools for RAS. Design: Realist process evaluation alongside ROLARR. Phase 1 – a literature review identified theories concerning how RAS becomes embedded into practice and impacts on teamwork and decision-making. These were refined through interviews across nine NHS trusts with theatre teams. Phase 2 – a multisite case study was conducted across four trusts to test the theories. Data were collected using observation, video recording, interviews and questionnaires. Phase 3 – interviews were conducted in other surgical disciplines to assess the generalisability of the findings. Findings: The introduction of RAS is surgeon led but dependent on support at multiple levels. There is significant variation in the training provided to theatre teams. Contextual factors supporting the integration of RAS include the provision of whole-team training, the presence of handpicked dedicated teams and the availability of suitably sized operating theatres. RAS introduces challenges for teamwork that can impact operation duration, but, over time, teams develop strategies to overcome these challenges. Working with an experienced assistant supports teamwork, but experience of the procedure is insufficient for competence in RAS and experienced scrub practitioners are important in supporting inexperienced assistants. RAS can result in reduced distraction and increased concentration for the surgeon when he or she is supported by an experienced assistant or scrub practitioner. Conclusions: Our research suggests a need to pay greater attention to the training and skill mix of the team. To support effective teamwork, our research suggests that it is beneficial for surgeons to (1) encourage the team to communicate actions and concerns; (2) alert the attention of the assistant before issuing a request; and (3) acknowledge the scrub practitioner’s role in supporting inexperienced assistants. It is beneficial for the team to provide oral responses to the surgeon’s requests. Limitations: This study started after the trial, limiting impact on analysis of the trial. The small number of operations observed may mean that less frequent impacts of RAS were missed. Future work: Future research should include (1) exploring the transferability of guidance for effective teamwork to other surgical domains in which technology leads to the physical or perceptual separation of surgeon and team; (2) exploring the benefits and challenges of including realist methods in feasibility and pilot studies; (3) assessing the feasibility of using routine data to understand the impact of RAS on rare end points associated with patient safety; (4) developing and evaluating methods for whole-team training; and (5) evaluating the impact of different physical configurations of the robotic console and team members on teamwork. / National Inst for Health Research (NIHR)

La robotique a fait progresser les interventions chirurgicales, avec des interventions moins invasives, une manipulation d’instruments plus précise et une meilleure dextérité. Néanmoins, le manque de retour haptique sur les plates-formes chirurgicales existantes aujourd’hui rend délicat l’accomplissement des gestes chirurgicaux et par conséquent augmente le risque de ces procédures. Avec l’introduction d’un retour haptique, les robots chirurgicaux sont conçus avec une approche de télé-opération bilatérale. Le retard, inhérent à cette approche, est crucial car même un petit retard pourrait déstabiliser le système. En pratique, le retard est inévitable, notamment pour les robots miniaturisés avec communication sans fils. Pour résoudre les problèmes liés à l’instabilité induite par le retard et rendre passif le canal de communication, l’approche de wave variable transformation (WVT) a été proposée. Néanmoins, les performances de suivi sont compromises à cause de la conservation de la condition de passivité. Dans cette thèse, une nouvelle approche de compensation basée sur la structure de wave variable, et considérant moins de condition de conservation est proposée afin d’améliorer les performances de suivi en position, en vitesse et en force. Pour garantir la passivité du système global, une approche énergétique (energy reservoir based regulators) est développée pour ajuster les termes de WVT avec une analyse rigoureuse. La méthode proposée permet d’améliorer les performances de suivi avec uniquement un retard de transmission dans un seul sens. Pour faciliter davantage les procédures chirurgicales, notamment les microchirurgies, deux facteurs d’échelle ont été rajoutés à l’approche de compensation. Une analyse de passivité a été par ailleurs menée en considérant la transparence du système. Les performances de suivi peuvent être obtenues si et seulement si les conditions de passivité et de transparence sont satisfaites. Les approches de compensation, avec et sans mise à l’échelle, ont été vérifiées à travers des simulations et des évaluations expérimentales. / Robotic technology has advanced the surgical procedures in terms of reduced trauma, more accurate manipulation and enhanced dexterity. However, the lack of haptic feedback on existing surgical robotic platforms makes it impossible for the surgeon to feel the operative site,and thus increases the risks of surgical procedures. With the introduction of haptic feedback, the surgical robots are design in bilateral teleoperation way. Time delay in bilateral teleoperation is crucial because even small time delay may destabilize the system. In practice, time delay is unavoidable, e.g. wireless communication miniaturized surgical robots, internet based robotic-assisted telesurgery and transmission of big amount of information, etc. In order to solve the instability caused by time delay in bilateral teleoperation, wave variable transformation (WVT) method has been proposed to passivate the delayed communication channel. However, the tracking performances are compromised due to the conservative passivity condition. In this thesis, a new wave variable compensation (WVC) structure with less conservative condition is proposed to enhance the velocity/position and force tracking performances. In order to guarantee the passivity of the whole system, energy reservoir based regulators are designed to adjust the WVC terms in the proposed structure with rigorous analysis. The WVC is able to achieve tracking performance with only single trip time delay. To better facilitate the surgical procedures, e.g. the microsurgeries, a scaled WVC structure is also developed by adding two scaling factors to the WVC structure. Passivity analysis on the scaled WVC is conducted with consideration of system transparency. Scaled tracking performance can be obtained as long as the two obtained passivity and transparency conditions are satisfied. The proposed WVC and scaled WVC have been verified through simulation and experimental studies.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第24608号 / 工博第5114号 / 新制||工||1978(附属図書館) / 京都大学大学院工学研究科機械理工学専攻 / (主査)教授 松野 文俊, 教授 小森 雅晴, 教授 森本 淳 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

Introduction: In recent years, robot-assisted surgery has taken over as a first option in rectal cancer treatment. The overall perception is that robot-assisted surgery is a method with good surgical outcomes. Many current studies have focused on comparing robot-assisted surgery to conventional laparoscopy. To our knowledge, few studies have been conducted to compare surgical outcomes in rectal cancer over time in robot-assisted surgery as training and knowledge increases in the field. Aim: To examine the two most commonly used robot-assisted surgical procedures in rectal cancer, to compare surgical outcomes of each procedure over a ten-year period. Method: A retrospective comparative study design was used. The national Swedish Colorectal Cancer Registry (SCRCR) was used to identify patients who underwent robot-assisted rectal cancer surgery at Örebro University Hospital between 2013 and 2022. Two surgical procedures were assessed: anterior resection and abdomino-perineal resection. Studied outcomes included: console-time, operation time, blood loss, hospital stay and conversion rate. Group comparisons were performed. Results: In total 202 patients were included and grouped into two periods (2013-2017; 2018-2022). A statistically significant reduction was observed in both procedures regarding blood loss in the later period. No other statistically significant differences were identified. Patients operated with APR in the later period were less fit. Conclusion: The surgical procedures showed comparable clinical outcomes in both periods. Our study showed that more complex cases in the group operated with APR were selected in the second period, which could imply that a higher degree of surgical proficiency was obtained over time.

Yes / Safe and successful surgery depends on effective teamwork between professional groups, each playing their part in a complex division of labour. This article reports the first empirical examination of how introduction of robot-assisted surgery changes the division of labour within surgical teams and impacts teamwork and patient safety. Data collection and analysis was informed by realist principles. Interviews were conducted with surgical teams across nine UK hospitals and, in a multi-site case study across four hospitals, data were collected using a range of methods, including ethnographic observation, video recording and semi-structured interviews. Our findings reveal that as the robot enables the surgeon to do more, the surgical assistant's role becomes less clearly defined. Robot-assisted surgery also introduces new tasks for the surgical assistant and scrub practitioner, in terms of communicating information to the surgeon. However, the use of robot-assisted surgery does not redistribute work in a uniform way; contextual factors of individual experience and team relationships shape changes to the division of labour. For instance, in some situations, scrub practitioners take on the role of supporting inexperienced surgical assistants. These changes in the division of labour do not persist when team members return to operations that are not robot-assisted. This study contributes to wider literature on divisions of labour in healthcare and how this is impacted by the introduction of new technologies. In particular, we emphasise the need to pay attention to often neglected micro-level contextual factors. This can highlight behaviours that can be promoted to benefit patient care.

Das Kolorektale Karzinom zählt zu den häufigsten malignen Erkrankungen und ist verbunden mit einer hohen Letalität. Die Therapie des KRK beinhaltet häufig chirurgische Herangehensweisen, welche sich in den letzten Jahren zunehmend in Richtung minimalinvasiver und roboterassistierter Verfahren entwickeln. Die operative Versorgung bringt, auch aufgrund der engen anatomischen Lagebeziehungen, ein Verletzungsrisiko für verschiedene anatomische Strukturen mit sich. Hierzu zählen neben seltenen Organverletzungen auch Schädigungen der Nerven, welche in die Lebensqualität oft stark beeinträchtigenden urogenitalen Dysfunktionen resultieren können. Daraus können Komplikationen folgen, deren Vermeidung stets angestrebt wird. Maschinelle Lernverfahren erleben in den letzten Jahren eine zunehmende Popularität und werden auf eine wachsende Vielzahl von Arbeitsbereichen mit Erfolg angewendet. Darunter findet sich auch die Medizin. Da die mangelhafte Erkennung von abdominellen Strukturen einen relevanten Risikofaktor verschiedener Operationen darstellt, der einen großen Einfluss auf die Prognose und Lebensqualität der Patient*innen hat, bietet sich hier großes Potential. An dieser Stelle soll durch eine KI-basierte Assistenz eine Lücke geschlossen werden, deren Relevanz bisher noch nicht ausreichend untersucht wurde. Auch wenn sich bereits zeigte, dass maschinelle Lernverfahren das Potential haben, optisch differenzierbare Strukturen im chirurgischen Kontext zu erkennen, bleibt die klinische Bedeutung dessen bislang unklar. In dieser Arbeit wird untersucht, inwiefern die Erkennung anatomischer Strukturen durch Bilderkennungsalgorithmen auf intraoperativem Bildmaterial möglich ist. Dies ist eine notwendige Grundlage für die Entwicklung weiterführender Technologien für die Erleichterung von operativen Eingriffen, die Vermeidung von Komplikationen oder die Erkennung morphologisch sichtbarer Pathologien. Hierbei soll sich künftig nicht nur auf die Anwendung in roboterassistierten Rektum- bzw. Sigmaresektionen beschränkt werden, sondern eine Anwendung für alle minimalinvasiven OPs ermöglicht werden. Damit können Kosten und Dauer von Eingriffen ebenso sinken wie die kognitiven Anforderungen an die Operateur*innen und eine Verbesserung der postoperativen Ergebnisse und Lebensqualität für die Patient*innen erreicht werden. Es wurden 43 zwischen Februar 2019 und März 2021 an der Klinik für Viszeral-, Thorax- und Gefäßchirurgie des Universitätsklinikums Carl Gustav Carus in Dresden durchgeführte roboterassistierte Sigma- bzw. Rektumresektionen und -exstirpationen einbezogen. Diese wurden hinsichtlich der Sichtbarkeit 6 verschiedener anatomischer Strukturen untersucht. Für die Kategorien Leber, Magen, Milz, Nerven, Pankreas und Ureter wurden jeweils zwischen 1023 und 1754 Einzelbilder aus 18 bis 23 OPs verwendet. Damit wurden sowohl anatomische Strukturen geringerer als auch höchster Komplexität betrachtet. Auf den Schritt der temporalen Annotation folgte nach der Einzelbildextraktion die semantische Segmentierung eines jeden Bildes. Dabei wurden alle Bereiche, in denen die jeweilige Struktur zu sehen ist, in ihren exakten Grenzen markiert. Diese segmentierten Bilder stellten die Grundlage für den anschließenden deep learning Prozess mittels eines CNNs dar. Das Resultat dessen war für jede Struktur ein Bilderkennungsalgorithmus, der sie automatisiert erkennen und semantische Segmentierungen anfertigen kann. Die Evaluation der Erkennungsleistung erfolgte mittels Intersection over Union, F1- Score, Precision Score, Recall Score, Specificity und Accuracy. Des Weiteren wurde die Performance des Algorithmus am Beispiel von 35 Bildern, von denen 16 das Pankreas zeigten, hinsichtlich der IoU für dieses Segment mit derjenigen von 28 Proband*innen verschiedener Wissens- und Ausbildungsstände verglichen. Dabei wurde das Konzept der Bounding box–Annotation verwendet. Die Ergebnisse der erarbeiteten Bilderkennungsalgorithmen bewegten sich für die 6 untersuchten Strukturen bei einer durchschnittlichen IoU, welche den Grad an Überlappung zweier Segmente beschreibt, zwischen 0,744 ± 0,275 und 0,255 ± 0,147. In der klinischen Evaluation wurden durch den auf das Pankreas trainierten Algorithmus Ergebnisse erzielt, die sich im Vergleich mit den 28 Proband*innen mit 0,31 an zweiter Stelle hinter einer Person von höchster Expertise eingliederten. Die von den Teilnehmenden erzielte durchschnittliche IoU betrug 0,100 ± 0,097. Die in dieser Arbeit erreichten Ergebnisse stellen einen guten Ausgangspunkt für die Weiterentwicklung KI-basierter Assistenzfunktionalitäten für den chirurgischen Alltag dar. Trotz einiger Limitationen hinsichtlich der Generalisierbarkeit einer eher kleinen und monozentrischen Untersuchung und Verbesserungspotential bezüglich der Generierung der Segmentierungen konnte ein hochwertiger Datensatz erarbeitet und publiziert werden. Insgesamt betrachtet lässt sich aus den Ergebnissen der darauf basierenden Bilderkennungsalgorithmen schließen, dass Methoden künstlicher Intelligenz bereits jetzt das Potential haben, viele Organe sehr gut und zuverlässig zu erkennen und abzugrenzen. Dadurch besitzen sie das Potential einer relevanten Unterstützung im chirurgischen Alltag. Bis zum Erreichen des Ziels einer klinischen Anwendung sind jedoch noch einige Schritte zu gehen, insbesondere die Übertragung des Erreichten auf Bewegtbildmaterial steht hierbei im Vordergrund. Zudem zeigte sich, dass die etablierten Metriken nicht ideal geeignet sind, um die klinische Relevanz der Prädiktionen abzubilden. Damit ist es notwendig, weiterhin über geeignete Metriken für die praktische Anwendung zu diskutieren und an der Entwicklung neuer Maßzahlen zu arbeiten. Allerdings kann bereits jetzt eine wertvolle Hilfestellung, besonders für Personen ohne langjährige chirurgische Erfahrung, geleistet werden. Dies stellt eine hervorragende Grundlage für die Weiterentwicklung der Algorithmen und deren Implementierung als Grundlage weiterführender Technologien im Bereich der intraoperativen Assistenzsysteme dar. / Colorectal cancer (CRC) is one of the most common malignancies and is associated with a high mortality rate. The treatment of CRC often involves surgical approaches, which in recent years have increasingly evolved towards minimally invasive and robotic-assisted procedures. Surgical treatment entails a risk of injury to various anatomical structures, in part due to the narrow surgical field and close anatomical positioning. In addition to rare organ injuries these include the more common nerve lesions which often result in urogenital dysfunction affecting patients' quality of life tremendously. Consequently, complications can occur and the aim is always to avoid them. In recent years, machine learning techniques have become increasingly popular and are being successfully applied in a growing variety of fields. One such area is medical applications. Since the inadequate detection of anatomical structures represents a relevant risk factor in various surgical procedures with a high impact on patient prognosis and quality of life, there is great potential here. At this point, AI-based assistance aims to fill a gap whose practical relevance has not yet been sufficiently investigated. Although machine learning has already been shown to identify optically differentiable structures in a surgical context, its clinical significance remains unclear to date. This work examines the possibility of using image recognition algorithms to identify anatomical structures on intraoperative image material. This research is fundamental to the development of future technologies that facilitate surgical interventions, reduce the likelihood of complications or identify morphologically visible organ pathologies. This technique is intended to be used in the widespread field of minimally invasive surgery, rather than being limited to robot-assisted rectal and sigmoid resections. This could result in a decrease in the expenses and duration of surgery, as well as a reduction in the cognitive demands on the surgeons. Additionally, it could lead to improvements in post-operative outcomes and quality of life for patients. Between February 2019 and March 2021, 43 robot-assisted rectal and sigmoid resections and extirpations performed at the Clinic for Visceral, Thoracic and Vascular Surgery of the University Hospital Carl Gustav Carus in Dresden were included in this work. The surgery recordings were examined with regard to the visibility of 6 different anatomical structures. For the categories liver, stomach, spleen, nerves, pancreas and ureter, between 1023 and 1754 individual frames from 18 to 23 surgeries were used in each case. Thus, both anatomical structures of lower as well as highest complexity were considered. After performing temporal annotation and extracting single frames, each frame underwent semantic segmentation. In this step, all areas displaying the respective structure were marked with their exact boundaries. The resulting segmented images provided as input to the subsequent deep learning process using a CNN. As a result, we have obtained an image recognition algorithm for each structure considered capable of automatic detection and semantic segmentation. The recognition performance was evaluated using metrics including Intersection over Union, F1- Score, Precision Score, Recall Score, Specificity and Accuracy. Additionally, the algorithm's performance was compared with the organ recognition skills of 28 individuals with varying levels of medical knowledge and training. A clinical evaluation was performed using the example of the pancreas, which had to be highlighted by bounding boxes. For this purpose, a sample of 35 images, 16 of which included the pancreas, was used to examine the IoU for this segment. The developed image recognition algorithms produced results regarding the average IoU, which describes the degree of overlap between two segments, ranging from 0.744 ± 0.275 to 0.255 ± 0.147 for the 6 anatomical structures investigated. During the clinical validation, the algorithm's results for the generation of bounding boxes for the segment pancreas achieved the second-highest score, at an IoU of 0.31. This ranking placed it second among the 28 individuals, surpassed only by a single person with the highest level of expertise. The average IoU obtained by the participants was 0.100 ± 0.097. The results of this work provide a good starting point for the development of further AI-based assistance functionalities for everyday surgical practice. Despite some limitations regarding the generalisability of a rather small and monocentric study and potential for improvement in the generation of the segmentations, a high quality dataset has been compiled and published. Overall, the resulting image recognition algorithms' outcomes indicate that AI techniques already have the potential to detect and differentiate a lot of organs very well and dependably. As a consequence, they have the capacity to offer significant assistance to the surgical routine. Before moving to clinical application, several additional steps need to be taken, with particular emphasis on the crucial process of transferring what has been achieved to moving image material. Furthermore, it has become evident that the established metrics are not entirely capable of representing the clinical value of the predictions. Thus, it is necessary to have further discussions regarding appropriate metrics for practical implementation and to focus on the development of new measures. However, even now they can provide a precious assistance, particularly for individuals without extensive surgical training. This signifies an excellent foundation for advancing the algorithms and implementing them as the basis for future technologies in the field of intra-operative assistance systems.