Medical Robotics

Medical Robotics

Medical Robotics

Edited by Vanja Bozovic

I-Tech

IV

Published by I-Tech Education and Publishing I-Tech Education and Publishing Vienna Austria Abstracting and non-profit use of the material is permitted with credit to the source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside. After this work has been published by the I-Tech Education and Publishing, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. © 2008 I-Tech Education and Publishing www.i-techonline.com Additional copies can be obtained from: [email protected] First published January 2008 Printed in Croatia

A catalogue record for this book is available from the Austrian Library. Medical Robotics, Edited by Vanja Bozovic p. cm. ISBN 978-3-902613-18-9 1. Medical Robotics. 2. Robotic Surgery. I. Vanja Bozovic

V

Preface The first generation of surgical robots are already being installed in a number of operating rooms around the world. Robotics is being introduced to medicine because it allows for unprecedented control and precision of surgical instruments in minimally invasive procedures. So far, robots have been used to position an endoscope, perform gallbladder surgery and correct gastroesophogeal reflux and heartburn. The ultimate goal of the robotic surgery field is to design a robot that can be used to perform closed-chest, beating-heart surgery. The use of robotics in surgery will expand over the next decades without any doubt. Minimally Invasive Surgery (MIS) is a revolutionary approach in surgery. In MIS, the operation is performed with instruments and viewing equipment inserted into the body through small incisions created by the surgeon, in contrast to open surgery with large incisions. This minimizes surgical trauma and damage to healthy tissue, resulting in shorter patient recovery time. The aim of this book is to provide an overview of the state-of-art, to present new ideas, original results and practical experiences in this expanding area. Nevertheless, many chapters in the book concern advanced research on this growing area. The book provides critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies. This book is certainly a small sample of the research activity on Medical Robotics going on around the globe as you read it, but it surely covers a good deal of what has been done in the field recently, and as such it works as a valuable source for researchers interested in the involved subjects, whether they are currently “medical roboticists” or not.

Editor Dr. Vanja Bozovic E-mail: [email protected]

VII

Contents Preface

V

1. The learning curve of robot-assisted laparoscopic surgery E. Olthof, D. Nio and W.A. Bemelman

001

2. The Must-Have in Robotic Heart Surgery: Haptic Feedback Eva U. Braun, Hermann Mayer, Alois Knoll, Ruediger Lange and Robert Bauernschmitt

009

3. Robot-Assisted Epicardial Ultrasound for Coronary Artery Localization and Anastomosis Quality Assessment in Totally Endoscopic Coronary Bypass Surgery Ricardo P.J. Budde, Cornelius Borst, Patricia F.A. Bakker and Paul F. Gründeman

021

4. Image Guided Robotic Systems for Focal Ultrasound Based Surgical Applications Sunita Chauhan

029

5. Robotic Applications in Neurosurgery M. Sam Eljamel

041

6. Autonomous Virtual Mobile Robot for the Exploration of 3D Medical Images 065 Luca Ferrarini, Hans Olofsen, Johan H.C. Reiber and Faiza Admiraal-Behloul 7. Robotic Foregut Surgery Daniel T. McKenna, M.D. and Jon C. Gould

075

8. Robotic Long Bone Fracture Reduction A. E. Graham, S. Q. Xie, K. C. Aw, W. L. Xu and S. Mukherjee

085

9. Non-Invasive Estimates of Local Field Potentials for Brain-Computer Interfaces: Theoretical derivation and comparison with direct intracranial recordings Rolando Grave de Peralta Menendez and Sara L. Gonzalez Andino

103

10. Motion Tracking for Minimally Invasive Robotic Surgery Martin Groeger, Klaus Arbter and Gerd Hirzinger

117

11. Surgical Skills Training For Robotic Assisted Surgery Juan D. Hernandez R., Fernando Bello and Ara Darzi

149

12. Medical Robotics in Cardiac Surgery Stephan Jacobs and Volkmar Falk

167

13. Robotic Neurosurgery Karas, CS and Baig, MN

175

VIII

14. The Use of Software Systems for Visualized Treatment Objectives in Orthognatic Surgery Zafer Ozgur Pektas, Beyza Hancioglu Kircelli and Ulkem Cilasun

181

15. Intelligent Laparoscopic Assistant Robot through Surgery Task Model: How to Give Intelligence to Medical Robots Dong-Soo Kwon, Seong-Young Ko and Jonathan Kim

197

16. Miniature robotic guidance for spine surgery Y. Barzilay, L. Kaplan and M. Liebergall

219

17. Nerve Sparing Axillary Dissection using the da Vinci Surgical System Susan M L Lim, Cheng-Kiong Kum and Foong-Lian Lam

233

18. Robotic-assisted Laparoscopic Renal and Adrenal Surgery Yves Caumartin, Jeffrey Warren and Patrick P.W. Luke

239

19. Robin Heart - Perspectives of application of mini invasive tools in cardiac surgery Zbigniew Nawrat and Pawel Kostka

265

20. Robot Assisted Laparoscopic Surgery for Aortoiliac Disease; a systematic review Jeroen Diks, Denise Nio, Willem Wisselink and Willem Bemelman

291

21. Basic Study of Appropriate Knot-tying Force in the Gastrointestinal Tract for Development of Haptic Surgical Robot Junya Oguma, Soji Ozawa, Yasuhide Morikawa, Toshiharu Furukawa, Yuko Kitagawa, Masakazu Ueda, Kouhei Ohnishi and Masaki Kitajima

299

22. Robotic Gastrectomy with Lymphadenectomy for Gastric Cancer Casandra Anderson, Joshua Ellenhorn and Alessio Pigazzi

305

23. Robotic Rectal Cancer Surgery Minia Hellan, Joshua Ellenhorn and Alessio Pigazzi

315

24. Efficient Non-Invasive Registration with A-mode Ultrasound in Skull Surgery Aleksandra Popovic, Stefan Heger, Axel Follmann, Ting Wu, Martin Engelhardt, Kirsten Schmieder and Klaus Radermacher

323

25. Camera Holding Robotic Devices in Urology Sashi S Kommu and Abhay Rané

341

26. Telerobotic Surgery for Right and SigmoidColectomy Arthur Rawlings, M.D., M.Div. and David Crawford

351

27. Robotic Assisted Microsurgery (RAMS):Application in Plastic Surgery Sanjay Saraf

363

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28. Prototypic force feedback instrument for minimally invasive robotic surgery Ulrich Seibold, Bernhard Kuebler, and Gerd Hirzinger

377

29. “RoboLase”: Internet-accessible robotic laser scissors and laser tweezers microscope systems Linda Z. Shi, Michael W. Berns and Elliot Botvinick

401

30. Robot Attack on Vascular Surgery Petr Stádler

421

31. Mechanical error analysis of compact forceps manipulator for laparoscopic surgery Takashi Suzuki, Yoichi Katayama, Etsuko Kobayashi and Ichiro Sakuma

433

32. Dental Patient Robot Hideaki Takanobu

443

33. Neuro- and Fascial Anatomy in the Male Pelvis for Robotic Radical Prosta- 457 tectomy Atsushi Takenaka, Ashutosh K. Tewari and Masato Fujisawa 34. Robotic assistance in microvascular surgery M.G.W. van den Heuvel and R.R.W.J. van der Hulst

471

35. Cooperative robotic system to support surgicalinterventions Raúl A. Castillo Cruces, H. Christian Schneider and Jürgen Wahrburg

481

36. Robotics in General Surgery James Wall, MD, Venita Chandra, MD and Thomas Krummel

491

37. Telemanipulated Long Bone Fracture Reduction Ralf Westphal, Simon Winkelbach, Thomas Gösling, Markus Oszwald, Tobias Hüfner, Christian Krettek and Friedrich Wahl

507

1 The learning curve of robot-assisted laparoscopic surgery E. Olthof1, D. Nio1 and W.A. Bemelman2

Department of Surgery, Spaarne Hospital, Hoofddorp, Department of Surgery, Academic Medical Center, Amsterdam The Netherlands 1

2

1. Introduction Endoscopic surgery has proven to be beneficial to the patient with regard to reduction of hospital stay, postoperative pain and earlier return to daily activities. After its introduction, development of new instrumentation improved and facilitated endoscopic performance (Yohannes et al, 2002). Despite this development, laparoscopic procedures have been limited by fixed distances, restricted freedom of motion of the surgical instruments, impaired visualization and small working space (Sarle et al, 2004). With the introduction of surgical robotic systems an attempt was made to overcome these technical difficulties. Many conventional laparoscopic procedures have been duplicated with assistance of a surgical robotic system. Endoscopic cardiac procedures, that were not feasible before applying conventional laparoscopic techniques, are currently performed robotically-assisted. Several advantages of robotic surgery compared to conventional laparoscopy have been identified: additional degrees of freedom of motion, downscaling of movements, enhanced stability (both of visualization and surgical instrumentation), restoration of the eye-hand target axis, elimination of the fulcrum effect and improved ergonomics for the surgeon. These features are supposed to enhance surgical performance by improved accuracy, dexterity and visualization. Consequently, it can be expected that endoscopic surgical skills are more easily mastered and the learning curve is shortened. The learning curve can be defined as the amount of practice (in time or number of repetitions) necessary to achieve a consistency of a specified parameter. A time-action analysis, the time to complete a task, the number of actions required and the number of errors made, are parameters used to evaluate the learning curve for a specific task. In daily practice, other parameters viz. conversion rate, operating time, blood loss, morbidity and hospital stay are used to assess the learning curve for a specific procedure. Most advanced endoscopic procedures are characterized by a long learning curve. Learning curves are associated with prolonged operative times, increased patient morbidity and higher costs. These difficulties might delay further implementation of advanced endoscopic techniques. Although a surgical robotic system might impose as the ideal endoscopic instrument, most clinical studies have not shown benefit with regard to operative time compared to conventional endoscopy. The objective of this study is to systematically review the available literature to evaluate the impact of a surgical robotic system on the learning curve of endoscopic procedures compared to conventional endoscopy.

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2. Methods A computer-assisted search was performed in the medical databases Medline (from January 1966 to June 2007), Embase (from January 1988 to June 2007) and the Cochrane Database of Systematic Reviews, using a combination of the keywords “Learning curve, robot, telemanipulation and computer-assisted surgery”. After identifying relevant titles, the abstracts of these studies were read to decide if the study was suitable. Two authors (EO and DN) independently read the abstracts. A manual search of reference lists of studies thus obtained was conducted for any relevant articles not found in the computerized search. 2.1 Criteria for inclusion Clinical and experimental studies eligible for inclusion had to describe a learning curve for robot-assisted procedures. Articles in languages other than English, German or French were excluded. Articles, in which a summary of different procedures executed with the aid of a robotic surgical system was described, were excluded.

3. Results In total 21 studies were identified. Most excluded studies were case reports, small series or reports of the author’s experience with a variety of surgical procedures using a robotic system without evaluation of a learning curve. The search resulted in 10 experimental studies on laparoscopic skills. In general surgery, articles reporting a learning curve were mostly those describing robot-assisted cholecystectomy and robot-assisted antireflux surgery (fundoplication), respectively 7 and 4 studies. There were some incidental reports of other surgical procedures viz. gastric bypass (3) and aortoiliac surgery (1). Reports on other fields than general surgery as urology and cardiac surgery were not included for evaluation. As a result this review concentrates on the learning curve of robot–assisted standard experimental exercises, laparoscopic cholecystectomy and laparoscopic fundoplication.

4. Robot-assisted laparoscopic skills Ten experimental studies described standardized exercises with either Zeus (4/10) or Da Vinci (6/10) robotic system. In all studies basic endoscopic tasks such as transferring beads/ rings, rope passing, knot tying and suturing were reported. The drills were predominantly performed by laparoscopic novice participants sometimes compared with laparoscopic experienced surgeons. In 6 studies (Prasad et al, 2001; Yohannes et al, 2002; Maniar et al, 2004; Nio et al, 2005 ; Blavier et al, 2006; Heemskerk et al, 2007) the robotic learning curve (RLC) was compared to the manual learning curve (MLC). In most studies the learning curve was defined on the basis of 2 parameters; completion time and amount of errors, often given as a combined score. Results are shown in table 1. In most studies the initial performance using the robotic system was inferior to the conventional laparoscopy. Although a rapid improvement of robotic performance was observed, conventional laparoscopic performance was rarely equalled. In all but one study a significant improvement of outcome parameter after time was shown, which suggested a significant learning curve. Only one study (Heemskerk et al, 2007) showed a flat RLC from the beginning. Most novice participants showed an initial inferior performance in comparison to laparoscopic experienced participants. This resulted in a steeper early phase of their RLC.

The learning curve of robot-assisted laparoscopic surgery

3

When RLC and MLC were compared results were not conclusive. When steeper learning curves were described in either the robotic of conventional laparoscopic group, they were attributed to an initially worse performance. Robotic system

Skill

Yohannes

Dexterity task Da Suturing/KnotVinci tying

Prasad

Zeus

Bead transfer Rope pass

Bead transfer Rope pass Bead Da drop/transfer Heemskerk Vinci Needle cap Suturing Maniar

Zeus

No. of Participants repetitions LN/LE 5

5 15

Parameter

4LN/4LE Time Calculated score 17LN/11LE (time + error) Precision score Calculated score 20LN % improvement

3

8LN

Time Accuracy

Learning curve Yes

Yes Yes

No

Performance score Da Passing needle Blavier 6 10LN Error score Yes Vinci through rings Ambidexterity score Number of Suturing Nio Zeus 20 1LN/1LE actions / stitch Yes Knot-tying or knot > 5 -14 h Time Chang Zeus Knot-tying 8? Yes training Composite score Da OSATS Hernandez Suturing 5 7N/6E Yes Vinci Time Pick and place Time Da 4 weeks Narazaki Needle passing 7LN (Travelling Yes Vinci training* Suturing distance) Performance Da LN :Yes Ro 5 drills 5-6 17 LN/2LE score Vinci LE : No (time+error) LN: laparoscopic naive, LE: laparoscopic experienced, OSATS: objective structured assessment of surgical skills * 6 sessions of training Table 1. Learning curve. Results of robotic skill studies

5. Robot-assisted laparoscopic cholecystectomy Seven robot-assisted cholecystectomy studies describing the learning curve were identified. Four series were comparative studies (Perez et al, 2003; Guilianotti et al, 2003; Caratozollo et al, 2005; Heemskerk et al, 2005) and 3 series consisted of consecutive patient series

Medical Robotics

4

(Chitwood et al, 2001; Ruurda et al, 2002; Vidovszky et al, 2006). In 6 studies the Da Vinci was used, in only one the Zeus-AESOP robotic system. Laparoscopic experienced surgeons performed the laparoscopic cholecystectomy. The set-up time and operative time were used as the parameters for the learning curve. Four studies (Caratozollo et al, 2005, Vidovszky et al, 2006, Ruurda et al, 2002, Chitwood et al, 2001) showed a decrease of the robotic set-up time, but in only 2 studies this decrease was significant (Chitwood et al, 2001;Vidovszky et al, 2006). In one study robotic set-up time did not change in time (Heemskerk et al, 2005) and 2 studies did not report on the robotic set-up time (Perez et al, 2003, Giulianotti et al, 2003). The operating time decreased in 3/7 studies (Perez et al, 2003, Giulianotti et al, 2003; Caratozzolo et al, 2005), of which 2 studies showed a significant decrease. All 3 studies reported that the mean robotic operative time at the end of the series was equal compared to manual laparoscopic cholecystectomy. One study mentioned that 20 operations were needed to complete the learning phase. (Guilianotti et al, 2003). No major intra-operative complications occurred. Conversion was necessary 7/219 times as a result of severe cholecystitis, poor visualization or obscure anatomy. The conversion rate was not higher in the robotic laparoscopic cholecystectomy. No study mentioned at which moment of the learning curve conversion was necessary. Three studies mentioned mechanical problems such as a malfunctioning/interfering of the robotic arms, which necessitated repositioning of the robotic arms and exchange of instruments (Caratozzolo et al, 2005; Vidovszky et al, 2006) and in one case detachment of the robotic instrument resulted in a minilaparotomy (Ruurda et al, 2002). Robotic system Caratozzolo Zeus

No. of Surgeon patients (LN/LE) 29

2LE

Parameter

Learning curve

Conversion

Set-up time Yes Operative time Yes

2/29 0/12

Heemskerk

Da Vinci N=12*

1LE

Set-up time No Operative time No

Vidovsky

Da Vinci N=51

NR

Set-up time Yes, significant 3/51 Operative time No

Ruurda

Da Vinci N=35

3LE

Set-up time Yes Operative time No

Chitwood

Da Vinci N=20

LE

Set-up time Yes, significant 0/20 Operative time No Combined time Yes, significant

Perez

Da Vinci N=20

3 LE

Operative time Yes, significant 0/20

1/35

Giulianotti Da Vinci N=52** NR Operative time Yes, significant 1/52 LN: laparoscopic naive, LE: laparoscopic experienced, NR not reported * Vs. historical robotic series;** 14 procedures were combined procedures (with fundoplication, hepatic and gastric resection) Table 2. Learning curve of robot-assisted laparoscopic cholecystectomy

The learning curve of robot-assisted laparoscopic surgery

5

6. Robot-assisted anti-reflux surgery Four fundoplication studies showed the learning curve of experienced surgeons all performed with Da Vinci. Only one study compared the RLC with the MLC (Morino et al, 2006). The decrease in set-up time and operative time was used to compare the mean results of the first and second part of the series to assess the learning curve. The set-up time, reported in 2 studies (Chitwood et al, 2001; Wykypiel et al 2003), decreased but not significantly. The operative time decreased in 3 studies. One study reported equivalence in operating time when compared with conventional laparoscopic fundoplication already after 2 procedures (Wykypiel et al 2003). Another study reported that 20 robotic procedures were necessary to complete the learning phase (Guilianotti et al, 2003). Two conversions due to operative complications, not related to the robotic system were reported. No mechanical problems were described. Results are shown in table 3. Robotic No. of Surgeon system patients LE/LN Giulianotti Da Vinci N=49

NR

Chitwood

Da Vinci N=14

LE

Wykypiel

Da Vinci N=10

LE

Morino

Da Vinci N=25

LE

Parameter

Learning curve

Operative time Yes, significant Set-up time Operative time Set-up time Operative time

Conversion 2/49

Yes, NS NR Yes, significant Yes, significance nr 0/10 Yes, significance nr

Operative time No, significance nr

0/10*

LN: laparoscopic naive, LE: laparoscopic experienced, NR not reported, NS not significant *1 conversion to manual laparoscopy Table 3. Learning curve robot-assisted laparoscopic fundoplication

7. Discussion Few reports on the learning curve of robotic surgery are available. Studies to compare robotic with conventional laparoscopic learning curves were even scarcer. To measure the learning curve of robotic surgical performance a diversity of parameters was used throughout most studies. These parameters were not always well defined. Furthermore, a great variety of practice/time was used to define an early or late experience phase of the learning curve. An experience bias was expected in most clinical series, because of prior laparoscopic experience of the participating surgeons. All these issues limit an objective evaluation of the learning curve of robotic surgery. However, although robotic systems are supposed to be “intuitive” in use, this technique showed to have a learning curve. This was most clearly demonstrated for laparoscopically inexperienced persons. A long learning curve might prevent implementation of a new technology, but the most important feature of a new technology, despite its learning curve, should be its advantage for the patient or the surgeon. Does it result in a reduction of morbidity or mortality? Does it facilitate and enhance laparoscopic surgical performance? These important questions remain unanswered with the current data. Furthermore, the financial cost-benefit should also be considered (Heemskerk et al, 2005).

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Medical Robotics

A learning curve consists of an initial steep phase in which performance increases rapidly. When the change in improvement slows down, the learning curve reaches a plateau phase, in which variability in performance is small. The number of repetitions in most reported experimental series are to low to reach the consistency which characterizes the end of the learning curve. Only the first and steepest part is evaluated, in which the most improvement is expected. However, in 9/10 studies a learning curve was described, with the majority of participants being laparoscopically naïve. When compared to laparoscopic experienced participants the RLC of the laparoscopic naïve persons was steeper, due to inferior performance at the beginning. This suggests more impact of a robot on laparoscopic naïve persons, whereas a laparoscopic experienced person quickly adapts to the advantages of operating robotically (fulcrum effect) and benefits of his prior laparoscopic experience. In the clinical series more “repetitions” are performed. As for the robot-assisted laparoscopic cholecystectomy, in 3/7 studies no learning curve was described for the robotic operative time, although a learning curve for the robotic set-up time was seen in all reported studies. All 4 comparative studies described equal operative times for robotic assisted cholecystectomy with conventional laparoscopic cholecystectomy after 20-50 procedures. All procedures were done by experienced laparoscopic surgeons. Proficiency for a conventional laparoscopic cholecystectomy is reached after 30 procedures (Dagash et al, 2002). Only in one out of the four studies no learning curve was observed for robot-assisted fundoplication operative time. Set-up time showed a learning curve in all studies. One study, which compared RLC and MLC of fundoplication reported equal operative times after only two robotic procedures. Proficiency of a conventional laparoscopic fundoplication is said to be reached after 28 procedures. The variability in operating time remains high for this procedure even in the late phase of surgical experience (Dagash et al. 2003). Most advantage of a robotic system is expected in advanced or complicated operative procedures. A laparoscopic cholecystectomy might be too simple, since it does not necessitate fine and complex movements. It might not be the appropriate procedure to evaluate the robotic learning curve. Although a laparoscopic fundoplication asks for more skill, most advantage of a robotic system is expected only during suturing of the wrap, which is a small part of the total procedure. Total operative time is not an accurate parameter to evaluate performance and learning curve. An expected learning curve for the robotic set-up time was found, but not quantified in most studies. However, the clinical importance of a small increment in total operative time due to robotic set-up time is low, especially when operative times are long. The use of robotic systems in laparoscopic surgery does not obviate the learning curve. Application of this technology has its own learning curve with respect to set-up of the system and getting accustomed to the specific features of the robotic systems. The limited data suggest that this learning curve is comparable with conventional laparoscopic surgery. Laparoscopically naive surgeons might benefit more from the advantages of a robotic system such as 3-D visualization and the absence of the fulcrum effect. This results in a steeper first phase of the robotic learning curve. However, experienced laparoscopic surgeons benefit from their prior laparoscopic experience shortening the robotic learning curve when compared to novice surgeons.

The learning curve of robot-assisted laparoscopic surgery

7

8. Referenties Ballantyne GH: Robotic surgery, telerobotic surgery, telepresence and telementoring; review of early clinical results. Surg Endosc 2002 July;16:1389-1402 Blavier A, Gaudissart Q, Cadiere G et al: Comparison of learning curves and skill transfer between classical and robotic laparoscopy according to the viewing conditions: implications for training. Am J Surg 2007, July; 194: 115-121 Caratozzolo E, Recordare A, Massani M, et al: Telerobotic-assited laparoscopic cholecystectomy: our experience on 29 patients; J Hepatobiliary Pancreatic Surg 2005; 12:163-166 Chang L, Satava RM, Pellegrini CA et al: Robotic surgery: identifying the learning curve through objective measurement of skill. Surg Endosc 2003, November; 17: 1744-1748 Chitwood WR, Nifong LW, Chapman W.H., et al: Robotic surgical training in an academic institution. Ann Surg 2001, October; 234:475-484; discussion 484-486 Dagash H, Chowdhury M, Pierro A. When can I be proficient in laparoscopic surgery? A systematic review of the evidence. J Pediatr Surg 2003, May;38:720-724 Giulianotti PC, Coratti A, Angelini M, et al: Robotics in general surgery:personal experience in a large community hospital. Arch Surg 2003, July; 138:777-784 Heemskerk J, van Dam R, van Gemert WG, et al: First results after introduction of the fourarmed da Vinci surgical system in fully robotic laparoscopic cholecystectomy. Dig Surg 2005, February; 22: 426-431 Heemskerk J, van Gemert WG, de Vries J et al: Learning curves of robot-assisted laparoscopic surgery compared with conventional laparoscopic surgery: An experimental study evaluating skill acquisition of robot-assisted laparoscopic tasks compared with conventional laparoscopic tasks in inexperienced users. Surg Laparosc EndoscPercutan Tech 2007, June; 17 : 171-174 Hernandez JD, Bann SD, Munz Y, et al: Qualitative and quantitative analysis of the learning curve of a simulated task on the da Vinci system. Surg Endosc 2004, March; 18: 372378 Maniar HS, Council ML, Prasad SM et al: Comparison of skill training with robotic systems and traditional endoscopy: Implications on training and adoption. J Surg Res 2005, May; 125: 23-29 Morino M, Peegrino L, Giaccone C, et al: Randomized clinical trial of robot-assisted versus laparoscopic Nissen fundoplication. Br J Surg 2006,May; 93:553-558 Narazaki K, Oleynikov D, Stergiou, et al: Robotic surgery training and performance: Identifying objective variables for quantifying the extent of proficiency. Surg Endosc 2006, January; 20: 96-103 Nio D, Bemelman WA, Balm R, et al: Laparoscopic vascular anastomoses: Does robotic (Zeus-Aesop) assistance help to overcome the learning curve? Surg Endosc 2005, August; 19: 1071-1076 Perez A, Zinner MJ, Ashley SW, et al: What is the value of telerobotic technology in gastrointestinal surgery? Surg Endosc 2003,May; 17:811-813 Prasad SM, Maniar HS, Soper NJ et al: The effect of robotic assistance on learning curves for basic laparoscopic skills. Am J Surg 2002, June; 183: 702-707 Ro CY, Toumpoulis IK, Ashton RC, et al: A novel drill set for the enhancement and assessment of robotic surgical performance. Stud Health Technol Inform 2005; 111:418-21

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Ruurda JP, Broeders IA, Simmermacher RP, et al: Feasibility of robot-assisted laparoscopic surgery: an evaluation of 35 robot assisted laparoscopic cholecystectomies. Surg Laparosc Endosc Percutan Tech 2002, February; 12:41-45 Sarle R, Tewari A, Shirvastava A et al: Surgical Robotics and laparoscopic training drills. J Endourol 2004; 18: 63-67 Vidovszky TJ, Smith W, Ghosh J, et al: Robotic cholecystectomy: learning curve, advantages, and limitations. J Surg Res 2006,December; 136, 172-178 Wykypiel H, Wetscher GJ, Klaus A, et al: Robotic-assisted laparoscopic partial posterior fundolplication with the Da Vinci system: initial experiences and technical aspects. Langenbecks Arch Surg 2003, February; 387:411-416 Yohannes P, Rotariu P, Pinto P, et al: Comparison of robotic versus laparoscopic skills: is there a difference in the learning curve? Urology 2002, July; 60: 39-45

2 The Must-Have in Robotic Heart Surgery: Haptic Feedback Eva U. Braun1, Hermann Mayer1, 2, Alois Knoll2, Ruediger Lange 1 and Robert Bauernschmitt 1

Technische Universität München Heart Center Munich, Clinic for Cardiovascular Surgery, 2Fakultät für Informatik, Robotics and Embedded Systems, Technische Universität München Germany 1German

1. Introduction The minimally invasive endoscopic surgery was introduced in the late 1980s in the abdominal surgery as revolutionary surgical technique (Voges et al., 1997). Surgeons no longer needed to physically place their hands within the body to perform an operation. In minimal invasive surgery, instruments and viewing equipments are inserted into the body through small incisions. Long manipulators are used to perform operations under manual guidance. This does not only minimize the collateral surgical trauma of an access incision but results also in quicker recovery. In heart surgery the introduction of endoscopic techniques were promising, but not satisfying like the application of robots in other surgical disciplines (Bholat et al., 1999; Gutt et al., 2004; Mitsuishi et al., 2000). Complex cardiac surgery had to be performed by long instruments without tremor filter or adequate freedom of movement, so satisfactory results were missing. In heart surgery pure endoscopic techniques have not established since the demanded high precision in this speciality did not reached with endoscopic instruments only. The promise of telemanipulated endoscopic assistance was to eliminate many of the beginning impediments, with the concurrent enhancements of motion scaling, tremor filtration, 3-dimensional vision and fulcrum effect. The surgeon could now operate with a surgical mechatronic assist system in a comfortable, dextrous and intuitive manner. The solution for the initial problems was the implementation of telemanipulators that offer with the endoscopic instruments as much degrees of freedom in movement as the hand of the surgeon in conventional open surgery performing 6 degrees of freedom instead of four in conventional endoscopic instruments. Furthermore the telemanipulator had to dispose of 3D-optic and a filter of tremor (Suematsu & Del Nido, 2004). The new system has been a telesystem controlled remotely by the surgeon. The implementation of totally endoscopic heart surgery was realised ten years later with the telemanipulator Da Vinci® (Intuitive Surgical, Inc., Sunnyvale, CA, USA) after introducing endoscopic surgery in abdominal surgery. Nevertheless technical limitations still exist that limit the application in special heart diseases and surgical indications in expert medical centres only.

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Medical Robotics

This telemanipulated technology is available for a minimal part of heart surgical patients only since the technical inconvenience of the system and the clumsy system is considerably limited in valve surgery, congenital heart surgery and a bigger part of bypass surgery. The necessity of haptic feedback is discussed controversially by robotically working surgeons and haptic engineers (Bethea et al., 2004; Fager, 2004; Hu et al., 2004). The postulate, that the integration of a supplementary haptic channel in addition to the visual channel improves the quality of surgical work and enhances the immersion for the surgeon in a remote system, is not yet demonstrated and evidenced. For virtual and artificial scenarios tactile sense and haptic feedback is an essential part (Darggahi & Najarian, 2004; Van Beers et al., 1999), but in the research of surgical telepresence for remote real scenarios the necessity of haptic feedback is still discussed very intensely. Several microsurgical telerobot systems are implemented by research groups all over the world (Cavusoglu et al., 2003; Garcia-Ruiz et al., 1997; Kwon 1998), but important questions and problems arising while operating are not answered and solved sufficiently. The breaking of surgical suture material and the damage of tissue are basic and unsolved problems in telemanipulated surgery. A further hypothesis is not yet explored: The especially high fatigue of the surgeon while and after robotic operations is caused in visual compensation of the skills and movements (Thompson et al., 1999). The basic solution seems to be the implementation of force feedback. In our study haptic feedback is built up in the experimental setup of a surgical telemanipulator system (fig. 1) as technical modification (Schirmbeck et al., 2004 a; Bauernschmitt et al., 2005 a; Mayer et al, 2005), the application on surgical skills is analysed and evaluated ( Schirmbeck et al., 2005 ; Bauernschmitt et al., 2005 b; Freyberger et al., 2005; Mayer et al., 2005).

Figure 1. Surgical telemanipulator system: Two instruments and one 3-D camera

The Must-Have in Robotic Heart Surgery: Haptic Feedback

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2. Methods 2.1 Robotic system for endoscopic heart surgery We built up a telemanipulated surgical experimental platform with commercially available equivalent surgical instruments to present comparable conditions for the surgeons. The difference and advancement is our implementation of haptic feedback in the instruments and the new robotic system. The setup comprises like typical systems for robotic surgery an operator-side master console for in-output and a patient-side robotic manipulator that directly interacts with the operating environment. A bi-manual telemanipulator is built up not only capable performing delicate operations, but also capable of applying real-time image processing tools like coronary artery detection (Nagy et al., 2004), navigation features (Mayer et al., 2004) and autonomous procedures (Schirmbeck et al., 2004 b). Telemanipulator system The robotic system consists of two surgical manipulators, which are controlled by two PHANTOM® 1.5 input devices (SensAble Technologies, Inc., Woburn, MA, USA), and a third robot, which carries a stereoscopic camera (Richard Wolf GmbH, Knittlingen, Germany). Each manipulator is composed of a KUKA KR 6/2 robot (KUKA Roboter GmbH, Augsburg, Germany), that bears a surgical instrument of Intuitive Surgical®. The KUKA robot disposes of six degrees of freedom. The surgical instruments provide three degrees of freedom. Therefore each robotic arm has eight degrees of freedom which enables free surgical manipulation via trocar kinematics. A micro-gripper at the distal end of the shaft can be rotated and the adaptation of pitch and yaw angles is possible. We developed an adapter to link the robotic arm with the instrument. For security reasons all flange adapters are equipped with magnetic security couplings. Those disengaged exercising forces exceed a certain level and might cause harm on instruments or tissue. All movable parts of the gripper are driven by steel wires. Their motion is controlled by four driving wheels at the proximal end of the instrument, one four each degree of freedom (two for yaw of the fingers). In order to control the instrument, we have flanged servos to each driving wheel by means of an Oldham coupling. This guarantees instrument movement free of jerk. The servo controllers are connected via serial lines to a multiport card. This redundancy renders the end effector possible to reach every position and orientation within the working space under restriction of trocar kinematics for surgical use. Haptic instruments and haptic interface We modified the instruments of the Da Vinci® telemanipulator system for measuring forces while executing surgical tasks (Bowersox et al., 1998). Since the shaft of the surgical instruments is made of carbon fibre, force sensors have to be very sensitive and reliable. Therefore strain gauge sensors are applied, which are employed for industrial force registration. The sensor gauges are applied at the distal end of the instrument's shaft near the gripper in order to display realistic forces during operation. The strain gauge force sensors measure forces along two translational directions of the instrument’s shaft. One full bridge of sensors is used for each direction. Forces are displayed to the user by means of two PHANTOM®, which act at the same time as input devices. The signals of the sensors are amplified and transmitted via CAN-bus to a PC system. Since reading of direct sensor is associated with noise a smoothing filter is applied in order to

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stabilize the results. Position and orientation of the manipulators are controlled by the two PHANTOM® input devices. The working space of approximately 20x25x40 cm provides enough space to perform surgical procedures. The user controls a stylus pen equipped with a switch that can be used to open and close the micro-grippers. The basic idea of minimally invasive surgery is that only small incisions have to be made into the surface of the patient’s body. The translational movements of the instruments are essentially restricted by shifts and rotations about these fulcrums (trocar kinematics). The most interesting feature of the employed PHANTOM® devices is their capability of displaying forces to the user. Forces are fed back by small servomotors incorporated in the device. They are used to steer the stylus pen in a certain direction. This creates the impression of occurring forces, while the user is holding the pen at a certain posture. Optical system: 3D-endoscope and head mounted display To enable proper telemanipulation a 3D-display (Falk et al., 2001) is indispensable providing a distinct vision of the region of interest. An additional robot is equipped with the stereo endoscopic camera. This camera can also be moved by means of trocar kinematics as the instruments and can be actively controlled either by the operator or automatically track the instruments. Images taken from the stereo camera system can be displayed via three options, while time delay and least asynchrony in video have to be avoided (Thompson et al., 1999). One is a head mounted display (HMD) that is part of the input console. The second possibility is to alternately display left and right images on a Cathode Ray Tube- (CRT-) screen. In this case, the operator has to wear shutter glasses, which are triggered by the output on the screen. A third option is the projection of online-acquired polarized operation sequences on a silver screen with two video projectors. The projectors are equipped with polarising filters that are orthogonally arranged. Observers have to wear glasses with an appropriate polarisation for the corresponding eye. 2.2 Evaluation of force feedback Human participants The human subjects of this study included 25 surgeons divided in three groups within the Clinic for Cardiovascular Surgery in the German Heart Center Munich in different levels of surgical training and age (table 1).

6.1

young surgeons n=8 0.4

experienced surgeons n=12 11.5

robotic surgeons n=5 2.4

(±7.4) 36 (±8)

(±0.3) 29 (±7)

(±7.6) 39 (±8)

(±2.0) 39 (±5)

all participants n=25 Surgical experience (years) mean value (sd) Age (years) mean value (sd)

Table 1. Random sample of the human subjects for the evaluation of haptic feedback One group of robotically working surgeons and two groups of surgeons without experience in robotic surgery (one group with conventionally experienced surgeons and one with young surgeons) were evaluated.

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Every surgeon executed three surgical tasks three times under three different haptic levels (no haptic feedback, 1:1 real haptic feedback and 1:2 doubled force feedback). The order of the haptic conditions (type of task and haptic condition) were completely balanced to avoid learning effect and were double blinded. Training skills Before executing the main surgical tasks of the evaluation, the trainees got 15 minutes to get familiar with handling the robotic system. First, soft coloured pellets had to be transferred from one cup and in a second next right to the first one. Second, a rubber band had to be threaded through five eyelets. In addition the surgeons got time to tie several knots to be prepared for the main tasks of the evaluation. The surgical tasks The study intended basic surgical and cardiac surgical procedures. Knot tying, breaking suture material and detection of arteriosclerosis had to be performed in a defined cycle with double blinding. These tasks imply at least basic knowledge in surgical principles. The participants dealt with three different levels of haptic feedback: no haptic, actually fed back forces and enhanced force feedback. During the entire experiment, the arising forces were recorded. Breaking of suture material: The breaking of suture material represents the amount of telepresence and immersion of the robotic system for the surgeons. The surgeons had to tension the thread until the supposed breaking point and had to mention this point before breaking. The difference of force between the supposed and the real breaking of a surgical thread was measured in Newton. The used surgical suture material Prolene® 6-0 (ETHICON Inc., Somerville, NJ, USA) is a common and frequently used non-absorbable thread made from Polypropylene in heart surgery. Knot tying: The human subjects had to tie surgical knots with two surgical instruments equipped with haptic feedback. The surgeons had ten minutes to perform precisely as much knots in alternate way (left and right taught knots) as possible. The total number of knots, the applied forces and the breakage of suture material while knot tying were recorded. The speed and course of motion and the coordination of the graspers were analysed. In addition the trauma of the surrounding tissue has been rated while knot tying. Following parameters have been analysed: number of dents, holes, fissures and breaks of tissue. Furthermore, the number of outbreaks of knots and insufficient knots were counted. The finished objects with the knotted tissue were analysed in completion to the rated variables of the video recording. Detection of arteriosclerosis: The surgeons had to detect possible stenosis with one haptic instrument in realistic arteries made from polymer precisely and at the same time rapidly. The errors in detecting short, long or no stenosis in three arteries were counted. The applied forces while detecting were recorded in Newton and the time of detecting in seconds. The critical flicker fusion frequency CFF The critical flicker fusion frequency (CFF) is an individual part of the Wiener Testsystem (Schuhfried GmbH, Mödling, Austria) analysing the progression of fatigue during the evaluation (Wiemeyer, 2002). The CFF is regarded as an indicator for the central-nervous

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function capacity, the activation level and the progression of fatigue during practical tasks (Johansson & Sandström, 2003). The flicker fusion frequency has been identified between three blocks of tasks with the three different degrees of haptic levels (no haptic, 1:1 haptic and 1:2 haptic feedback).

3. Results 3.1 Surgical knot tying Force feedback influences the application of forces significantly (p0,05). Haptic feedback does not show any influence on the quality of surgical knot tying (p=0,05).

Figure 2. Forces while knot tying. With increasing haptic feedback the applied forces decrease significantly, *p