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Статті в журналах з теми "Autonomous surgery"

Автор: Grafiati

Опубліковано: 11 березня 2023

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1

Gumbs, Andrew A., Isabella Frigerio, Gaya Spolverato, Roland Croner, Alfredo Illanes, Elie Chouillard, and Eyad Elyan. "Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?" Sensors 21, no. 16 (August 17, 2021): 5526. http://dx.doi.org/10.3390/s21165526.

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Анотація:

Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). All of these facets of AI will be fundamental to the development of more autonomous actions in surgery, unfortunately, only a limited number of surgeons have or seek expertise in this rapidly evolving field. As opposed to AI in medicine, AI surgery (AIS) involves autonomous movements. Fortuitously, as the field of robotics in surgery has improved, more surgeons are becoming interested in technology and the potential of autonomous actions in procedures such as interventional radiology, endoscopy and surgery. The lack of haptics, or the sensation of touch, has hindered the wider adoption of robotics by many surgeons; however, now that the true potential of robotics can be comprehended, the embracing of AI by the surgical community is more important than ever before. Although current complete surgical systems are mainly only examples of tele-manipulation, for surgeons to get to more autonomously functioning robots, haptics is perhaps not the most important aspect. If the goal is for robots to ultimately become more and more independent, perhaps research should not focus on the concept of haptics as it is perceived by humans, and the focus should be on haptics as it is perceived by robots/computers. This article will discuss aspects of ML, DL, CV and NLP as they pertain to the modern practice of surgery, with a focus on current AI issues and advances that will enable us to get to more autonomous actions in surgery. Ultimately, there may be a paradigm shift that needs to occur in the surgical community as more surgeons with expertise in AI may be needed to fully unlock the potential of AIS in a safe, efficacious and timely manner.

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2

Rivas-Blanco, Irene, Carlos Perez-del-Pulgar, Carmen López-Casado, Enrique Bauzano, and Víctor Muñoz. "Transferring Know-How for an Autonomous Camera Robotic Assistant." Electronics 8, no. 2 (February 18, 2019): 224. http://dx.doi.org/10.3390/electronics8020224.

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Robotic platforms are taking their place in the operating room because they provide more stability and accuracy during surgery. Although most of these platforms are teleoperated, a lot of research is currently being carried out to design collaborative platforms. The objective is to reduce the surgeon workload through the automation of secondary or auxiliary tasks, which would benefit both surgeons and patients by facilitating the surgery and reducing the operation time. One of the most important secondary tasks is the endoscopic camera guidance, whose automation would allow the surgeon to be concentrated on handling the surgical instruments. This paper proposes a novel autonomous camera guidance approach for laparoscopic surgery. It is based on learning from demonstration (LfD), which has demonstrated its feasibility to transfer knowledge from humans to robots by means of multiple expert showings. The proposed approach has been validated using an experimental surgical robotic platform to perform peg transferring, a typical task that is used to train human skills in laparoscopic surgery. The results show that camera guidance can be easily trained by a surgeon for a particular task. Later, it can be autonomously reproduced in a similar way to one carried out by a human. Therefore, the results demonstrate that the use of learning from demonstration is a suitable method to perform autonomous camera guidance in collaborative surgical robotic platforms.

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3

Abdelaal, Alaa Eldin, Jordan Liu, Nancy Hong, Gregory D. Hager, and Septimiu E. Salcudean. "Parallelism in Autonomous Robotic Surgery." IEEE Robotics and Automation Letters 6, no. 2 (April 2021): 1824–31. http://dx.doi.org/10.1109/lra.2021.3060402.

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4

Ray, Katrina. "Autonomous robotic laparoscopic gastrointestinal surgery." Nature Reviews Gastroenterology & Hepatology 19, no. 3 (February 1, 2022): 148. http://dx.doi.org/10.1038/s41575-022-00584-z.

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5

Loftus, Tyler J., Amanda C. Filiberto, Jeremy Balch, Alexander L. Ayzengart, Patrick J. Tighe, Parisa Rashidi, Azra Bihorac, and Gilbert R. Upchurch. "Intelligent, Autonomous Machines in Surgery." Journal of Surgical Research 253 (September 2020): 92–99. http://dx.doi.org/10.1016/j.jss.2020.03.046.

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6

Rodriguez y Baena, Ferdinando, and Brian Davies. "Robotic surgery: from autonomous systems to intelligent tools." Robotica 28, no. 2 (August 27, 2009): 163–70. http://dx.doi.org/10.1017/s0263574709990427.

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SUMMARYA brief history of robotic surgery is provided, which describes the transition from autonomous robots to hands-on systems that are under the direct control of the surgeon. An example of the latter is the Acrobot (for active-constraint robot) system used in orthopaedics, whilst soft-tissue surgery is illustrated by the daVinci telemanipulator system. Non-technological aspects of robotic surgery have often been a major impediment to their widespread clinical use. These are discussed in detail, together with the role of navigation systems, which are considered a major competitor to surgical robots. A detailed description is then given of a registration method for robots to achieve improved accuracy. Registration is a major source of error in robotic surgery, particularly in orthopaedics. The paper describes the design and clinical implementation of a novel method, coined the bounded registration method, applied to minimally invasive registration of the femur. Results of simulations which compare the performance of bounded registration with a standard implementation of the iterative closest point algorithm are also presented, alongside a description of their application in the Acrobot hands-on robot, used clinically for uni-condylar knee arthroplasty.

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7

Madhu Mohan, R., C. Dr Grisha, M. S. Kunal, V. Lokanatha Reddy, M. Mahendra, and N. Pawan. "VISION ASSIT FOR AUTONOMOUS SURGERY ROBOT." IOP Conference Series: Materials Science and Engineering 1189, no. 1 (October 1, 2021): 012040. http://dx.doi.org/10.1088/1757-899x/1189/1/012040.

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8

Shademan, Azad, Ryan S. Decker, Justin D. Opfermann, Simon Leonard, Axel Krieger, and Peter C. W. Kim. "Supervised autonomous robotic soft tissue surgery." Science Translational Medicine 8, no. 337 (May 4, 2016): 337ra64. http://dx.doi.org/10.1126/scitranslmed.aad9398.

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9

Sandhu, Gurjit, Nicholas R. Teman, and Rebecca M. Minter. "Training Autonomous Surgeons." Annals of Surgery 261, no. 5 (May 2015): 843–45. http://dx.doi.org/10.1097/sla.0000000000001058.

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10

Nagyné Elek, Renáta, and Tamás Haidegger. "Next in Surgical Data Science: Autonomous Non-Technical Skill Assessment in Minimally Invasive Surgery Training." Journal of Clinical Medicine 11, no. 24 (December 19, 2022): 7533. http://dx.doi.org/10.3390/jcm11247533.

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Анотація:

Background: It is well understood that surgical skills largely define patient outcomes both in Minimally Invasive Surgery (MIS) and Robot-Assisted MIS (RAMIS). Non-technical surgical skills, including stress and distraction resilience, decision-making and situation awareness also contribute significantly. Autonomous, technologically supported objective skill assessment can be efficient tools to improve patient outcomes without the need to involve expert surgeon reviewers. However, autonomous non-technical skill assessments are unstandardized and open for more research. Recently, Surgical Data Science (SDS) has become able to improve the quality of interventional healthcare with big data and data processing techniques (capture, organization, analysis and modeling of data). SDS techniques can also help to achieve autonomous non-technical surgical skill assessments. Methods: An MIS training experiment is introduced to autonomously assess non-technical skills and to analyse the workload based on sensory data (video image and force) and a self-rating questionnaire (SURG-TLX). A sensorized surgical skill training phantom and adjacent training workflow were designed to simulate a complicated Laparoscopic Cholecystectomy task; the dissection of the cholecyst’s peritonial layer and the safe clip application on the cystic artery in an uncomfortable environment. A total of 20 training sessions were recorded from 7 subjects (3 non-medicals, 2 residents, 1 expert surgeon and 1 expert MIS surgeon). Workload and learning curves were studied via SURG-TLX. For autonomous non-technical skill assessment, video image data with tracked instruments based on Channel and Spatial Reliability Tracker (CSRT) and force data were utilized. An autonomous time series classification was achieved by a Fully Convolutional Neural Network (FCN), where the class labels were provided by SURG-TLX. Results: With unpaired t-tests, significant differences were found between the two groups (medical professionals and control) in certain workload components (mental demands, physical demands, and situational stress, p<0.0001, 95% confidence interval, p<0.05 for task complexity). With paired t-tests, the learning curves of the trials were also studied; the task complexity resulted in a significant difference between the first and the second trials. Autonomous non-technical skill classification was based on the FCN by applying the tool trajectories and force data as input. This resulted in a high accuracy (85%) on temporal demands classification based on the z component of the used forces and 75% accuracy for classifying mental demands/situational stress with the x component of the used forces validated with Leave One Out Cross-Validation. Conclusions: Non-technical skills and workload components can be classified autonomously based on measured training data. SDS can be effective via automated non-technical skill assessment.

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11

Yang, Shuo, Jiahao Chen, An Li, Ping Li, and Shulan Xu. "Autonomous Robotic Surgery for Immediately Loaded Implant-Supported Maxillary Full-Arch Prosthesis: A Case Report." Journal of Clinical Medicine 11, no. 21 (November 7, 2022): 6594. http://dx.doi.org/10.3390/jcm11216594.

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Robotic systems have emerged in dental implant surgery due to their accuracy. Autonomous robotic surgery may offer unprecedented advantages over conventional alternatives. This clinical protocol was used to show the feasibility of autonomous robotic surgery for immediately loaded implant-supported full-arch prostheses in the maxilla. This case report demonstrated the surgical protocol and outcomes in detail, highlighting the pros and cons of the autonomous robotic system. Within the limitations of this study, autonomous robotic surgery could be a feasible alternative to computer-assisted guided implant surgery.

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12

Rhatomy,MD, Sholahuddin, Krisna Yuarno Phatama, Asep Santoso, Kukuh Dwiputra Hernugrahanto, and Nicolaas Budhiparama. "Robot-Assisted in Hip and Knee Surgery: Are we ready?" Hip and Knee Journal 2, no. 2 (August 25, 2021): 54–56. http://dx.doi.org/10.46355/hipknee.v2i2.111.

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The word 'robot' is derived from the Polish word "robota," which means forced labor. It describes a machine that carries out various tasks either automatically or with minimal external input, especially one that is programmable. There are two main types of robotic surgery systems: haptic and autonomous. Haptic or tactile systems allow the surgeon to use or drive the robot to perform a surgical procedure. This technology requires constant input by the surgeon for the procedure to proceed. In contrast, autonomous robotic systems require the surgeon to perform the approach and set up the machine, but once engaged, the robot completes the surgery without the surgeon's help. The use of robotic technology has, in some cases, facilitated minimally invasive surgery, which has gained popularity with some patients. In spinal surgery, robotic technology has been successfully used to increase the accuracy of implant placement. Furthermore, robotic technology can improve the radiological alignment of implants following the pre-operative plan.1,2

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13

Feldkamp, J., and W. A. Scherbaum. "Substitution therapy after surgery for autonomous adenomas." Experimental and Clinical Endocrinology & Diabetes 106, S 04 (July 14, 2009): S85—S87. http://dx.doi.org/10.1055/s-0029-1212065.

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14

Champault, G., A. Rogeau, V. Garnier, and L. Pennanech. "Autonomous ambulatory surgery unit: A successful model." Journal of Visceral Surgery 153, no. 5 (November 2016): 399. http://dx.doi.org/10.1016/j.jviscsurg.2016.07.004.

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15

Abs, Roger, Achilles Stevenaert, and Albert Beckers. "Autonomously functioning thyroid nodules in a patient with a thyrotropin-secreting pituitary adenoma: possible cause–effect relationship." European Journal of Endocrinology 131, no. 4 (October 1994): 355–58. http://dx.doi.org/10.1530/eje.0.1310355.

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Abs R, Stevenaert A, Beckers A. Autonomously functioning thyroid nodules in a patient with a thyrotropin-secreting pituitary adenoma: possible cause–effect relationship. Eur J Endocrinol 1994;131:355–8. ISSN 0804–4643 A 51-year-old female patient with long-standing hyperthyroidism due to a thyrotropin-secreting pituitary adenoma is reported, who became thyrotoxic again shortly after successful pituitary surgery. Functional testing and scintigraphy suggested the diagnosis of autonomous functioning thyroid nodules, which was confirmed by pathological examination of the resected thyroid tissue. This is the first report revealing the transition from a pituitary-dependent to a thyroid-dependent hyperthyroidism. Autonomous functioning thyroid nodules are, however, considered an intrinsic thyroid defect. In similarity with other disorders, in which trophic hormones may induce an autonomous secretion by the target gland, this report opens the possibility that a humoral factor may play a role in the development of autonomous functioning thyroid nodules. A Beckers, CHU-B35, Department of Endocrinology, Sart-Tilman, B-4000 Liège, Belgium

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16

Rosen, Jacob, and Ji Ma. "Autonomous Operation in Surgical Robotics." Mechanical Engineering 137, no. 09 (September 1, 2015): S15—S18. http://dx.doi.org/10.1115/1.2015-sep-9.

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The article focuses on developing an algorithm for automation based on stereo computer vision and dynamic registration in a surgical robotic context. The performance of the algorithm was further tested experimentally utilizing the block transfer task which corresponds to tissue manipulation as designed by Fundamentals of Laparoscopic Surgery (FLS). The surgical robotics field as a whole progresses towards the reduction of invasiveness limiting the trauma at the periphery of the surgical site and increase of semi-autonomous operation while positioning the surgeon as a decision maker rather than as an operator. The autonomous FLS task is implemented successfully and tested experimentally with the Raven II surgical robot system. The data indicate that the autonomous operational mode has better overall performance and limited tool-environment interaction compared with the human teleoperation mode. Surgeon’s intention may also be extracted from a database that may lead to seamless switching between the human operator and the autonomous system and in that sense, it may allow the autonomous algorithm to cope with more complex surgical environments.

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17

Poves-Álvarez, Rodrigo, Esther Gómez-Sánchez, Beatriz Martínez-Rafael, Cecilia Bartolomé, Elisa Alvarez-Fuente, María Fe Muñoz-Moreno, José María Eiros, Eduardo Tamayo, and Estefanía Gómez-Pesquera. "Parental Satisfaction With Autonomous Pediatric Ambulatory Surgery Units." Quality Management in Health Care 30, no. 3 (June 2, 2021): 145–52. http://dx.doi.org/10.1097/qmh.0000000000000301.

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18

Battaglia, Edoardo, Jacob Boehm, Yi Zheng, Andrew R. Jamieson, Jeffrey Gahan, and Ann Majewicz Fey. "Rethinking Autonomous Surgery: Focusing on Enhancement over Autonomy." European Urology Focus 7, no. 4 (July 2021): 696–705. http://dx.doi.org/10.1016/j.euf.2021.06.009.

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19

CONRADIE, JEAN-PIERRE, CORNIE SCHEFFER, KRISTIAAN SCHREVE, and AMIR ZARRABI. "SEMI-AUTONOMOUS NEEDLE-POSITIONING DEVICE FOR PERCUTANEOUS NEPHROLITHOTOMY PROCEDURES." Journal of Mechanics in Medicine and Biology 11, no. 01 (March 2011): 177–203. http://dx.doi.org/10.1142/s0219519410003708.

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At present, manual needle-positioning techniques known as "triangulation" and "keyhole surgery" are implemented during percutaneous nephrolithotomy (PCNL) to gain initial kidney access. These techniques do not ensure correct needle placement inside the kidney, resulting in multiple needle punctures, unnecessary hemorrhage, excessive radiation exposure to all involved and increased surgery time. A cost-effective fluoroscopy-guided needle-positioning system is proposed for aiding urologists in gaining accurate and repeatable kidney calyx access. Guidance is realized by modeling a C-arm fluoroscopic system as an adapted pinhole camera model and utilizing stereovision principles on an image pair. Targeting is realized with the aid of a graphical user interface operated by the surgeon. An average target registration error of 2.5 mm (SD = 0.8 mm) was achieved in a simulated environment. Similar results were achieved in the operating room environment with successful needle access in two in-vitro porcine kidneys.

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20

Yang, Xiao-Chun, Neng-Qin Luo, Hong-Yan Wei, and Tian-Xiang Liu. "Autonomous irrigation system improvement and extension." Asian Journal of Surgery 45, no. 2 (February 2022): 828–29. http://dx.doi.org/10.1016/j.asjsur.2021.12.019.

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21

Bani, Mehrdad J. "Autonomous Camera Movement for Robotic-Assisted Surgery: A Survey." International Journal of Advanced Engineering, Management and Science 3, no. 8 (2017): 829–36. http://dx.doi.org/10.24001/ijaems.3.8.2.

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22

Prendergast, J. Micah, and Mark E. Rentschler. "Towards autonomous motion control in minimally invasive robotic surgery." Expert Review of Medical Devices 13, no. 8 (July 11, 2016): 741–48. http://dx.doi.org/10.1080/17434440.2016.1205482.

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23

Wahl, R. A., K. Joseph, E. Bögner, Ch Ohmann, P. Goretzki, and H. D. Röher. "Thyroid function after surgery for autonomous and non-autonomous nodular endemic goitre — effect of iodide-substitution." Klinische Wochenschrift 63, no. 17 (September 1985): 812–20. http://dx.doi.org/10.1007/bf01732286.

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24

Wang, Tengteng, Xiude Chen, and Huawei Qu. "Economical and efficient autonomous ureteroscopic irrigation systems." Asian Journal of Surgery 44, no. 1 (January 2021): 409–11. http://dx.doi.org/10.1016/j.asjsur.2020.10.013.

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25

Altman, Keith. "CCHDS and the Faculty of Dental Surgery." Bulletin of the Royal College of Surgeons of England 92, no. 6 (June 1, 2010): 189. http://dx.doi.org/10.1308/147363510x506784.

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The Central Committee for Hospital Dental Services (CCHDS) is an autonomous committee of the British Dental Association (BDA). It represents dental staff working in hospitals and dental public health on wider issues affecting NHS staff. An executive subcommittee supports the CCHDS.

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26

King, Brady W., Luke A. Reisner, Abhilash K. Pandya, Anthony M. Composto, R. Darin Ellis, and Michael D. Klein. "Towards an Autonomous Robot for Camera Control During Laparoscopic Surgery." Journal of Laparoendoscopic & Advanced Surgical Techniques 23, no. 12 (December 2013): 1027–30. http://dx.doi.org/10.1089/lap.2013.0304.

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27

Ma, Xin, Chengzhi Song, Philip Waiyan Chiu, and Zheng Li. "Autonomous Flexible Endoscope for Minimally Invasive Surgery With Enhanced Safety." IEEE Robotics and Automation Letters 4, no. 3 (July 2019): 2607–13. http://dx.doi.org/10.1109/lra.2019.2895273.

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28

Scaglioni, B., A. Attanasio, M. Leonetti, A. F. Frangi, W. Cross, C. S. Byiani, and P. Valdastri. "Toward autonomous tissue retraction in robotic assisted minimally invasive surgery." European Urology Open Science 19 (July 2020): e2018-e2019. http://dx.doi.org/10.1016/s2666-1683(20)33958-6.

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29

Johannigman, Jay A., Peter Muskat, Stephen Barnes, Kenneth Davis, George Beck, and Richard D. Branson. "Autonomous Control of Oxygenation." Journal of Trauma: Injury, Infection, and Critical Care 64, Supplement (April 2008): S295—S301. http://dx.doi.org/10.1097/ta.0b013e31816bce54.

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30

Johannigman, Jay A., Peter Muskat, Stephen Barnes, Kenneth Davis, and Richard D. Branson. "Autonomous Control of Ventilation." Journal of Trauma: Injury, Infection, and Critical Care 64, Supplement (April 2008): S302—S320. http://dx.doi.org/10.1097/ta.0b013e31816bf4e2.

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31

Gumbs, Andrew A., Frank Alexander, Konrad Karcz, Elie Chouillard, Roland Croner, Jasamine Coles-Black, Belinda de Simone, et al. "White paper: definitions of artificial intelligence and autonomous actions in clinical surgery." Artificial Intelligence Surgery 2, no. 2 (2022): 93–100. http://dx.doi.org/10.20517/ais.2022.10.

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This white paper documents the consensus opinion of the expert members of the Editorial Board of Artificial Intelligence Surgery regarding the definitions of artificial intelligence and autonomy in regards to surgery and how the digital evolution of surgery is interrelated with the various forms of robotic-assisted surgery. It was derived from a series of video conference discussions, and the survey and results were subsequently revised and approved by all authors.

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32

Gumbs, Andrew A., Vincent Grasso, Nicolas Bourdel, Roland Croner, Gaya Spolverato, Isabella Frigerio, Alfredo Illanes, Mohammad Abu Hilal, Adrian Park, and Eyad Elyan. "The Advances in Computer Vision That Are Enabling More Autonomous Actions in Surgery: A Systematic Review of the Literature." Sensors 22, no. 13 (June 29, 2022): 4918. http://dx.doi.org/10.3390/s22134918.

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This is a review focused on advances and current limitations of computer vision (CV) and how CV can help us obtain to more autonomous actions in surgery. It is a follow-up article to one that we previously published in Sensors entitled, “Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?” As opposed to that article that also discussed issues of machine learning, deep learning and natural language processing, this review will delve deeper into the field of CV. Additionally, non-visual forms of data that can aid computerized robots in the performance of more autonomous actions, such as instrument priors and audio haptics, will also be highlighted. Furthermore, the current existential crisis for surgeons, endoscopists and interventional radiologists regarding more autonomy during procedures will be discussed. In summary, this paper will discuss how to harness the power of CV to keep doctors who do interventions in the loop.

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33

Chamorro, C., M. Aparicio, G. Marmisa, and J. L. Martinez-Urrialde. "Organ Transplantation in the Madrid Autonomous Region." Transplantation Proceedings 41, no. 6 (July 2009): 2302–3. http://dx.doi.org/10.1016/j.transproceed.2009.06.102.

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34

Sinha, Kavya, Peter Legeza, Pooja Tekula, Busra Tok Cekmecelioglu, Zsolt Garami, and Alan Lumsden. "Autonomous Perioperative Transcranial Doppler Monitoring." Journal of Vascular Surgery 72, no. 1 (July 2020): e236. http://dx.doi.org/10.1016/j.jvs.2020.04.390.

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35

Moustris, G. P., S. C. Hiridis, K. M. Deliparaschos, and K. M. Konstantinidis. "Evolution of autonomous and semi-autonomous robotic surgical systems: a review of the literature." International Journal of Medical Robotics and Computer Assisted Surgery 7, no. 4 (August 3, 2011): 375–92. http://dx.doi.org/10.1002/rcs.408.

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36

Team, Editorial. "Professor Ivan Ilyich Klyuev." Kazan medical journal 66, no. 6 (December 15, 1985): 468. http://dx.doi.org/10.17816/kazmj62271.

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On April 28, 1985, after a serious and prolonged illness at the age of 66, a famous scientist and surgeon, Honored Doctor of the RSFSR and the Mordovian Autonomous Soviet Socialist Republic, Doctor of Medical Sciences, Head of the Department of Hospital Surgery of the Mordovian Order of Friendship of Peoples of the University named after I.I. NP Ogareva professor Ivan Ilyich Klyuev.

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37

Balasubramanian, Shivaramakrishnan, Jayakumar Chenniah, Gurumurthy Balasubramanian, and Venkataram Vellaipandi. "The era of robotics: dexterity for surgery and medical care: narrative review." International Surgery Journal 7, no. 4 (March 26, 2020): 1317. http://dx.doi.org/10.18203/2349-2902.isj20201057.

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Robots are man-made machines; created to increase the performance of an action. They are either autonomous or semi-autonomous in the hands of the user. The medical field has evolved and revolutionized over the decades. It is the hour of the robot-assisted medical care to successfully change the clinical scenario of patient care. Employment of robotics in diverse fields of medical care has increased the effectiveness of the treatment and in return the effectiveness of the healthcare professionals. Our aim is to emphasize the advances in robot-assisted procedures over their comparable facets and highlight the unresolved challenges of robotics in medical care for the near future.

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Zhang, Lin, Menglong Ye, Petros Giataganas, Michael Hughes, Adrian Bradu, Adrian Podoleanu, and Guang-Zhong Yang. "From Macro to Micro: Autonomous Multiscale Image Fusion for Robotic Surgery." IEEE Robotics & Automation Magazine 24, no. 2 (June 2017): 63–72. http://dx.doi.org/10.1109/mra.2017.2680543.

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39

Takahashi, Yosuke, Atsushi Nakazawa, Kyoichi Deie, Kanako Harada, Jun Fujishiro, and Mamoru Mitsuishi. "A Study on Autonomous Surgical Skill Assessment for Pediatric Endoscopic Surgery." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2018 (2018): 1P1—J06. http://dx.doi.org/10.1299/jsmermd.2018.1p1-j06.

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40

Woo, Sang-Yoon, Sang-Jeong Lee, Ji-Yong Yoo, Jung-Joon Han, Soon-Jung Hwang, Kyung-Hoe Huh, Sam-Sun Lee, Min-Suk Heo, Soon-Chul Choi, and Won-Jin Yi. "Autonomous bone reposition around anatomical landmark for robot-assisted orthognathic surgery." Journal of Cranio-Maxillofacial Surgery 45, no. 12 (December 2017): 1980–88. http://dx.doi.org/10.1016/j.jcms.2017.09.001.

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41

Colovic, Radoje, Marjan Micev, Vladimir Radak, Nikica Grubor, Natasa Colovic, and Stojan Latincic. "Stromal tumor of duodenal autonomous nerves (plexosarcoma)." Srpski arhiv za celokupno lekarstvo 135, no. 5-6 (2007): 330–34. http://dx.doi.org/10.2298/sarh0706330c.

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Gastrointestinal tumors arising from autonomous nerves of Meisner?s or Auerbach?s plexus (plexomas and plexosarcomas) are rare tumors in only 87 cases described in the literature up to 2001. We present a very rare case of gastrointestinal stromal tumor (plexosarcoma) of the third and fourth portion of the duodenum, 130x98x87 mm in diameter, arising from its back wall, with central necrosis of the well circumscribed tumor, which communicated with the duodenum through an ulceration of 15x7mm in diameter, spreading towards the great vessels of the retroperitoneum. It was gradually and carefully removed, together with 17 cm of the duodenum and few centimeters of the jejunum with end-to-end duodenojejunostomy below the Vater?s papilla. During the removal of the tumor, the superior mesenteric artery, being within the tumor?s capsule, was accidentally ligated but not transsected. In spite of the removal of the ligature, the artery became thrombosed due to damage of the intima by ligature so that it had to be resected and reanastomosed. After otherwise uneventful recovery, except for a mild pus discharge through the drain, not far from the arterial anastomosis, the patient suddenly started bleeding on the 13th day after surgery. At emergency reoperation, a rupture of the mesenteric artery above the thrombosed anastomosis was found. In spite of absence of the arterial pulsation within the mesentery, the bowel looked vital and the back flow from the artery was satisfactory. The arterial rereconstruction was not possible, so the artery was ligated. The postoperative recovery was surprisingly uneventful. The patient was discharged ten days after surgery and has stayed symptom-free so far. .

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42

Wagner, Martin, Sebastian Bodenstedt, Marie Daum, Andre Schulze, Rayan Younis, Johanna Brandenburg, Fiona R. Kolbinger, et al. "The importance of machine learning in autonomous actions for surgical decision making." Artificial Intelligence Surgery 2, no. 2 (2022): 64–79. http://dx.doi.org/10.20517/ais.2022.02.

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Surgery faces a paradigm shift since it has developed rapidly in recent decades, becoming a high-tech discipline. Increasingly powerful technological developments such as modern operating rooms, featuring digital and interconnected equipment and novel imaging as well as robotic procedures, provide several data sources resulting in a huge potential to improve patient therapy and surgical outcome by means of Surgical Data Science. The emerging field of Surgical Data Science aims to improve the quality of surgery through acquisition, organization, analysis, and modeling of data, in particular using machine learning (ML). An integral part of surgical data science is to analyze the available data along the surgical treatment path and provide a context-aware autonomous action by means of ML methods. Autonomous actions related to surgical decision-making include preoperative decision support, intraoperative assistance functions, as well as robot-assisted actions. The goal is to democratize surgical skills and enhance the collaboration between surgeons and cyber-physical systems by quantifying surgical experience and making it accessible to machines, thereby improving patient therapy and outcome. The article introduces basic ML concepts as enablers for autonomous actions in surgery, highlighting examples for such actions along the surgical treatment path.

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Shu, Jianfeng, Rongtao Ding, Aixiang Jin, Hui Zhu, and Shu Chen. "Acupoint Selection for Autonomous Massage Based on Infrared Thermography." Traitement du Signal 39, no. 1 (February 28, 2022): 355–62. http://dx.doi.org/10.18280/ts.390137.

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After breast cancer surgery, the patient has difficulty in selecting the acupoints for autonomous massage, due to the changes in muscles and nerves on the affected side. To overcome the difficulty, this paper proposes an acupoint selection method for autonomous massage after breast cancer surgery, based on infrared thermography. Firstly, the contrast of the infrared thermogram was enhanced by image differencing, and the chest region and upper limbs were segmented through global thresholding. Next, temperature specific detection and edge detection algorithms were combined to automatically locate the main chest organs, and the horizontal and vertical axes of the upper limbs, according to the temperature specificity and temperature variation induced by the physiological structure of chest and upper limbs. Furthermore, the main acupoints for autonomous massage were determined, based on their positions relative to the axes of main organs. Experimental results show that our method achieved a positioning accuracy greater than 90.12%. The excellent positioning effect improves the compliance of patients in active rehabilitation.

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Triponez, F., D. Dosseh, M. Hazzan, C. Noel, P. Vanhille, and C. A. G. Proye. "Subtotal parathyroidectomy with thymectomy for autonomous hyperparathyroidism after renal transplantation." British Journal of Surgery 92, no. 10 (2005): 1282–87. http://dx.doi.org/10.1002/bjs.5080.

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Rutherford, John C., Wendy L. Taylor, Michael Stowasser, and Richard D. Gordon. "Success of Surgery for Primary Aldosteronism Judged by Residual Autonomous Aldosterone Production." World Journal of Surgery 22, no. 12 (December 1998): 1243–45. http://dx.doi.org/10.1007/s002689900552.

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46

Rouweyha, Rajy M., Alice Z. Chuang, Shrabanee Mitra, Chris B. Phillips, and Richard W. Yee. "Laser Epithelial Keratomileusis for Myopia With the Autonomous Laser." Journal of Refractive Surgery 18, no. 3 (May 2, 2002): 217–24. http://dx.doi.org/10.3928/1081-597x-20020501-02.

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47

Herr, Yeek. "Autonomous or heteronomous: the need for role models." Journal of Periodontal & Implant Science 51, no. 1 (2021): 1. http://dx.doi.org/10.5051/jpis.215101edi01.

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48

Koyama, Yuki, Murilo M. Marinho, Mamoru Mitsuishi, and Kanako Harada. "Autonomous Coordinated Control of the Light Guide for Positioning in Vitreoretinal Surgery." IEEE Transactions on Medical Robotics and Bionics 4, no. 1 (February 2022): 156–71. http://dx.doi.org/10.1109/tmrb.2022.3147033.

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Attanasio, Aleks, Bruno Scaglioni, Matteo Leonetti, Alejandro F. Frangi, William Cross, Chandra Shekhar Biyani, and Pietro Valdastri. "Autonomous Tissue Retraction in Robotic Assisted Minimally Invasive Surgery – A Feasibility Study." IEEE Robotics and Automation Letters 5, no. 4 (October 2020): 6528–35. http://dx.doi.org/10.1109/lra.2020.3013914.

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50

Tayama, Takashi, Yusuke Kurose, Tatsuya Nitta, Kanako Harada, Yusei Someya, Seiji Omata, Fumihito Arai, et al. "Image Processing for Autonomous Positioning of Eye Surgery Robot in Micro-Cannulation." Procedia CIRP 65 (2017): 105–9. http://dx.doi.org/10.1016/j.procir.2017.04.036.

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