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Bellos T, Manolitsis I, Katsimperis S, Juliebø-Jones P, Feretzakis G, Mitsogiannis I, Varkarakis I, Somani BK, Tzelves L. Artificial Intelligence in Urologic Robotic Oncologic Surgery: A Narrative Review. Cancers (Basel) 2024; 16:1775. [PMID: 38730727 PMCID: PMC11083167 DOI: 10.3390/cancers16091775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/29/2024] [Accepted: 05/02/2024] [Indexed: 05/13/2024] Open
Abstract
With the rapid increase in computer processing capacity over the past two decades, machine learning techniques have been applied in many sectors of daily life. Machine learning in therapeutic settings is also gaining popularity. We analysed current studies on machine learning in robotic urologic surgery. We searched PubMed/Medline and Google Scholar up to December 2023. Search terms included "urologic surgery", "artificial intelligence", "machine learning", "neural network", "automation", and "robotic surgery". Automatic preoperative imaging, intraoperative anatomy matching, and bleeding prediction has been a major focus. Early artificial intelligence (AI) therapeutic outcomes are promising. Robot-assisted surgery provides precise telemetry data and a cutting-edge viewing console to analyse and improve AI integration in surgery. Machine learning enhances surgical skill feedback, procedure effectiveness, surgical guidance, and postoperative prediction. Tension-sensors on robotic arms and augmented reality can improve surgery. This provides real-time organ motion monitoring, improving precision and accuracy. As datasets develop and electronic health records are used more and more, these technologies will become more effective and useful. AI in robotic surgery is intended to improve surgical training and experience. Both seek precision to improve surgical care. AI in ''master-slave'' robotic surgery offers the detailed, step-by-step examination of autonomous robotic treatments.
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Affiliation(s)
- Themistoklis Bellos
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| | - Ioannis Manolitsis
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| | - Stamatios Katsimperis
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| | | | - Georgios Feretzakis
- School of Science and Technology, Hellenic Open University, 26335 Patras, Greece;
| | - Iraklis Mitsogiannis
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| | - Ioannis Varkarakis
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| | - Bhaskar K. Somani
- Department of Urology, University of Southampton, Southampton SO16 6YD, UK;
| | - Lazaros Tzelves
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
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Huang X, Wang P, Chen J, Huang Y, Liao Q, Huang Y, Liu Z, Peng D. An intelligent grasper to provide real-time force feedback to shorten the learning curve in laparoscopic training. BMC Med Educ 2024; 24:161. [PMID: 38378608 PMCID: PMC10880316 DOI: 10.1186/s12909-024-05155-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 02/09/2024] [Indexed: 02/22/2024]
Abstract
BACKGROUND A lack of force feedback in laparoscopic surgery often leads to a steep learning curve to the novices and traditional training system equipped with force feedback need a high educational cost. This study aimed to use a laparoscopic grasper providing force feedback in laparoscopic training which can assist in controlling of gripping forces and improve the learning processing of the novices. METHODS Firstly, we conducted a pre-experiment to verify the role of force feedback in gripping operations and establish the safe gripping force threshold for the tasks. Following this, we proceeded with a four-week training program. Unlike the novices without feedback (Group A2), the novices receiving feedback (Group B2) underwent training that included force feedback. Finally, we completed a follow-up period without providing force feedback to assess the training effect under different conditions. Real-time force parameters were recorded and compared. RESULTS In the pre-experiment, we set the gripping force threshold for the tasks based on the experienced surgeons' performance. This is reasonable as the experienced surgeons have obtained adequate skill of handling grasper. The thresholds for task 1, 2, and 3 were set as 0.731 N, 1.203 N and 0.938 N, respectively. With force feedback, the gripping force applied by the novices with feedback (Group B1) was lower than that of the novices without feedback (Group A1) (p < 0.005). During the training period, the Group B2 takes 6 trails to achieve gripping force of 0.635 N, which is lower than the threshold line, whereas the Group A2 needs 11 trails, meaning that the learning curve of Group B2 was significantly shorter than that of Group A2. Additionally, during the follow-up period, there was no significant decline in force learning, and Group B2 demonstrated better control of gripping operations. The training with force feedback received positive evaluations. CONCLUSION Our study shows that using a grasper providing force feedback in laparoscopic training can help to control the gripping force and shorten the learning curve. It is anticipated that the laparoscopic grasper equipped with FBG sensor is promising to provide force feedback during laparoscopic training, which ultimately shows great potential in laparoscopic surgery.
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Affiliation(s)
- Xuemei Huang
- Obstetrics and Gynecology Center, Department of Gynecology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China
| | - Pingping Wang
- Obstetrics and Gynecology Center, Department of Gynecology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China
| | - Jie Chen
- Obstetrics and Gynecology Center, Department of Gynecology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China
| | - Yuxin Huang
- Obstetrics and Gynecology Center, Department of Gynecology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China
| | - Qiongxiu Liao
- Obstetrics and Gynecology Center, Department of Gynecology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China
| | - Yuting Huang
- Obstetrics and Gynecology Center, Department of Gynecology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China
| | - Zhengyong Liu
- Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems, School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou, 510275, China.
| | - Dongxian Peng
- Obstetrics and Gynecology Center, Department of Gynecology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.
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Bergholz M, Ferle M, Weber BM. The benefits of haptic feedback in robot assisted surgery and their moderators: a meta-analysis. Sci Rep 2023; 13:19215. [PMID: 37932393 PMCID: PMC10628231 DOI: 10.1038/s41598-023-46641-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 11/03/2023] [Indexed: 11/08/2023] Open
Abstract
Robot assisted surgery (RAS) provides medical practitioners with valuable tools, decreasing strain during surgery and leading to better patient outcomes. While the loss of haptic sensation is a commonly cited disadvantage of RAS, new systems aim to address this problem by providing artificial haptic feedback. N = 56 papers that compared robotic surgery systems with and without haptic feedback were analyzed to quantify the performance benefits of restoring the haptic modality. Additionally, this study identifies factors moderating the effect of restoring haptic sensation. Overall results showed haptic feedback was effective in reducing average forces (Hedges' g = 0.83) and peak forces (Hedges' g = 0.69) applied during surgery, as well as reducing the completion time (Hedges' g = 0.83). Haptic feedback has also been found to lead to higher accuracy (Hedges' g = 1.50) and success rates (Hedges' g = 0.80) during surgical tasks. Effect sizes on several measures varied between tasks, the type of provided feedback, and the subjects' levels of surgical expertise, with higher levels of expertise generally associated with smaller effect sizes. No significant differences were found between virtual fixtures and rendering contact forces. Implications for future research are discussed.
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Affiliation(s)
- Max Bergholz
- Department of Ergonomics, Technical University of Munich, 85748, Garching, Germany
- Institute of Robotics and Mechatronics, German Aerospace Center, 82234, Wessling, Germany
| | - Manuel Ferle
- Department of Ergonomics, Technical University of Munich, 85748, Garching, Germany.
| | - Bernhard M Weber
- Institute of Robotics and Mechatronics, German Aerospace Center, 82234, Wessling, Germany
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Polykandriotis E, Daenicke J, Bolat A, Grüner J, Schubert DW, Horch RE. Individualized Wound Closure-Mechanical Properties of Suture Materials. J Pers Med 2022; 12:jpm12071041. [PMID: 35887538 PMCID: PMC9316899 DOI: 10.3390/jpm12071041] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/19/2022] [Accepted: 06/22/2022] [Indexed: 11/16/2022] Open
Abstract
Wound closure is a key element of any procedure, especially aesthetic and reconstructive plastic surgery. Therefore, over the last decades, several devices have been developed in order to assist surgeons in achieving better results while saving valuable time. In this work, we give a concise review of the literature and present a biomechanical study of different suturing materials under mechanical load mimicking handling in the operating theatre. Nine different suture products, all of the same USP size (4-0), were subjected to a standardized crushing load by means of a needle holder. All materials were subjected to 0, 1, 3 and 5 crushing load cycles, respectively. The linear tensile strength was measured by means of a universal testing device. Attenuation of tensile strength was evaluated between materials and between crush cycles. In the pooled analysis, the linear tensile strength of the suture materials deteriorated significantly with every cycle (p < 0.0001). The suture materials displayed different initial tensile strengths (in descending order: polyglecaprone, polyglactin, polydioxanone, polyamid, polypropylene). In comparison, materials performed variably in terms of resistance to crush loading. The findings were statistically significant. The reconstructive surgeon has to be flexible and tailor wound closure techniques and materials to the individual patient, procedure and tissue demands; therefore, profound knowledge of the physical properties of the suture strands used is of paramount importance. The crushing load on suture materials during surgery can be detrimental for initial and long-term wound repair strength. As well as the standard wound closure methods (sutures, staples and adhesive strips), there are promising novel devices.
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Affiliation(s)
- Elias Polykandriotis
- Department of Plastic, Hand and Microsurgery, Sana Hospital Hof, 95032 Hof, Germany
- Department of Plastic and Hand Surgery, University of Erlangen Medical Center, 91054 Erlangen, Germany; (J.G.); (R.E.H.)
- Correspondence: ; Tel.: +49-15161-068069
| | - Jonas Daenicke
- Department of Materials Science and Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, 91054 Erlangen, Germany; (J.D.); (D.W.S.)
| | - Anil Bolat
- Department of Orthopedics, Theresien Hospital, 90491 Nürnberg, Germany;
| | - Jasmin Grüner
- Department of Plastic and Hand Surgery, University of Erlangen Medical Center, 91054 Erlangen, Germany; (J.G.); (R.E.H.)
| | - Dirk W. Schubert
- Department of Materials Science and Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, 91054 Erlangen, Germany; (J.D.); (D.W.S.)
| | - Raymund E. Horch
- Department of Plastic and Hand Surgery, University of Erlangen Medical Center, 91054 Erlangen, Germany; (J.G.); (R.E.H.)
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Bellini V, Valente M, Del Rio P, Bignami E. Artificial intelligence in thoracic surgery: a narrative review. J Thorac Dis 2022; 13:6963-6975. [PMID: 35070380 PMCID: PMC8743413 DOI: 10.21037/jtd-21-761] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 08/30/2021] [Indexed: 12/12/2022]
Abstract
Objective The aim of this article is to review the current applications of artificial intelligence in thoracic surgery, from diagnosis and pulmonary disease management, to preoperative risk-assessment, surgical planning, and outcomes prediction. Background Artificial intelligence implementation in healthcare settings is rapidly growing, though its widespread use in clinical practice is still limited. The employment of machine learning algorithms in thoracic surgery is wide-ranging, including all steps of the clinical pathway. Methods We performed a narrative review of the literature on Scopus, PubMed and Cochrane databases, including all the relevant studies published in the last ten years, until March 2021. Conclusion Machine learning methods are promising encouraging results throughout the key issues of thoracic surgery, both clinical, organizational, and educational. Artificial intelligence-based technologies showed remarkable efficacy to improve the perioperative evaluation of the patient, to assist the decision-making process, to enhance the surgical performance, and to optimize the operating room scheduling. Still, some concern remains about data supply, protection, and transparency, thus further studies and specific consensus guidelines are needed to validate these technologies for daily common practice. Keywords Artificial intelligence (AI); thoracic surgery; machine learning; lung resection; perioperative medicine
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Affiliation(s)
- Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Marina Valente
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Paolo Del Rio
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Elena Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
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Gómez Rivas J, Toribio Vázquez C, Ballesteros Ruiz C, Taratkin M, Marenco JL, Cacciamani GE, Checcucci E, Okhunov Z, Enikeev D, Esperto F, Grossmann R, Somani B, Veneziano D. Artificial intelligence and simulation in urology. Actas Urol Esp 2021; 45:524-529. [PMID: 34526254 DOI: 10.1016/j.acuroe.2021.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 10/27/2020] [Indexed: 10/20/2022]
Abstract
INTRODUCTION AND OBJECTIVE Artificial intelligence (AI) is in full development and its implementation in medicine has led to an improvement in clinical and surgical practice. One of its multiple applications is surgical training, with the creation of programs that allow avoiding complications and risks for the patient. The aim of this article is to analyze the advantages of AI applied to surgical training in urology. MATERIAL AND METHODS A literary research is carried out to identify articles published in English regarding AI applied to medicine, especially in surgery and the acquisition of surgical skills. RESULTS Surgical training has evolved over time thanks to AI. A model for surgical learning where skills are acquired in a progressive way while avoiding complications to the patient, has been created. The use of simulators allows a progressive learning, providing trainees with procedures that increase in number and complexity. On the other hand, AI is used in imaging tests for surgical or treatment planning. CONCLUSION Currently, the use of AI in daily clinical practice has led to progress in medicine, specifically in surgical training.
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Affiliation(s)
- J Gómez Rivas
- Departamento de Urología, Hospital Clínico San Carlos, Madrid, Spain; Young Academic Urologist-Urotechnology Working Party (ESUT-YAU), European Association of Urology, Arnhem, The Netherlands.
| | - C Toribio Vázquez
- Departamento de Urología, Hospital Universitario La Paz, Madrid, Spain
| | | | - M Taratkin
- Young Academic Urologist-Urotechnology Working Party (ESUT-YAU), European Association of Urology, Arnhem, The Netherlands; Institute for Urology and Reproductive Health, Sechenov University, Moscú, Russia
| | - J L Marenco
- Young Academic Urologist-Urotechnology Working Party (ESUT-YAU), European Association of Urology, Arnhem, The Netherlands; Departamento de Urología, Instituto Valenciano de Oncología, Valencia, Spain
| | - G E Cacciamani
- Young Academic Urologist-Urotechnology Working Party (ESUT-YAU), European Association of Urology, Arnhem, The Netherlands; Catherine and Joseph Aresty Department of Urology, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - E Checcucci
- Young Academic Urologist-Urotechnology Working Party (ESUT-YAU), European Association of Urology, Arnhem, The Netherlands; Division of Urology, Department of Oncology, School of Medicine, San Luigi Hospital, University of Turin, Orbassano, Italy
| | - Z Okhunov
- Young Academic Urologist-Urotechnology Working Party (ESUT-YAU), European Association of Urology, Arnhem, The Netherlands; Department of Urology, University of California, Irvine, CA, United States
| | - D Enikeev
- Institute for Urology and Reproductive Health, Sechenov University, Moscú, Russia
| | - F Esperto
- Department of Urology, Campus Biomedico, University of Rome, Roma, Italy
| | - R Grossmann
- Young Academic Urologist-Urotechnology Working Party (ESUT-YAU), European Association of Urology, Arnhem, The Netherlands; Eastern Maine Medical Center, Bangor, ME, United States
| | - B Somani
- Department of Urology, University Hospital Southhampton, Southampton, United Kingdom
| | - D Veneziano
- Young Academic Urologist-Urotechnology Working Party (ESUT-YAU), European Association of Urology, Arnhem, The Netherlands; Department of Urology and Kidney Transplant, Grande Ospedale Metropolitano, Reggio Calabria, Italy
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Cabibihan JJ, Alhaddad AY, Gulrez T, Yoon WJ. Influence of Visual and Haptic Feedback on the Detection of Threshold Forces in a Surgical Grasping Task. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3068934] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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8
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Gómez Rivas J, Toribio Vázquez C, Ballesteros Ruiz C, Taratkin M, Marenco JL, Cacciamani GE, Checcucci E, Okhunov Z, Enikeev D, Esperto F, Grossmann R, Somani B, Veneziano D. Artificial intelligence and simulation in urology. Actas Urol Esp 2021; 45:S0210-4806(21)00088-7. [PMID: 34127285 DOI: 10.1016/j.acuro.2020.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 10/27/2020] [Indexed: 11/28/2022]
Abstract
INTRODUCTION AND OBJECTIVE Artificial intelligence (AI) is in full development and its implementation in medicine has led to an improvement in clinical and surgical practice. One of its multiple applications is surgical training, with the creation of programs that allow avoiding complications and risks for the patient. The aim of this article is to analyze the advantages of AI applied to surgical training in urology. MATERIAL AND METHODS A literary research is carried out to identify articles published in English regarding AI applied to medicine, especially in surgery and the acquisition of surgical skills. RESULTS Surgical training has evolved over time thanks to AI. A model for surgical learning where skills are acquired in a progressive way while avoiding complications to the patient, has been created. The use of simulators allows a progressive learning, providing trainees with procedures that increase in number and complexity. On the other hand, AI is used in imaging tests for surgical or treatment planning. CONCLUSION Currently, the use of AI in daily clinical practice has led to progress in medicine, specifically in surgical training.
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Affiliation(s)
- J Gómez Rivas
- Departamento de Urología, Hospital Clínico San Carlos, Madrid, España; Young Academic Urologist-Urotechnology Working Party (ESUT-YAU), European Association of Urology, Arnhem, Países Bajos.
| | - C Toribio Vázquez
- Departamento de Urología, Hospital Universitario La Paz, Madrid, España
| | | | - M Taratkin
- Young Academic Urologist-Urotechnology Working Party (ESUT-YAU), European Association of Urology, Arnhem, Países Bajos; Institute for Urology and Reproductive Health, Sechenov University, Moscú, Rusia
| | - J L Marenco
- Young Academic Urologist-Urotechnology Working Party (ESUT-YAU), European Association of Urology, Arnhem, Países Bajos; Departamento de Urología, Instituto Valenciano de Oncología, Valencia, España
| | - G E Cacciamani
- Young Academic Urologist-Urotechnology Working Party (ESUT-YAU), European Association of Urology, Arnhem, Países Bajos; Catherine and Joseph Aresty Department of Urology, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, Estados Unidos
| | - E Checcucci
- Young Academic Urologist-Urotechnology Working Party (ESUT-YAU), European Association of Urology, Arnhem, Países Bajos; Division of Urology, Department of Oncology, School of Medicine, San Luigi Hospital, University of Turin, Orbassano, Italia
| | - Z Okhunov
- Young Academic Urologist-Urotechnology Working Party (ESUT-YAU), European Association of Urology, Arnhem, Países Bajos; Department of Urology, University of California, Irvine, California, Estados Unidos
| | - D Enikeev
- Institute for Urology and Reproductive Health, Sechenov University, Moscú, Rusia
| | - F Esperto
- Department of Urology, Campus Biomedico, University of Rome, Roma, Italia
| | - R Grossmann
- Young Academic Urologist-Urotechnology Working Party (ESUT-YAU), European Association of Urology, Arnhem, Países Bajos; Eastern Maine Medical Center, Bangor, Maine, Estados Unidos
| | - B Somani
- Department of Urology, University Hospital Southhampton, Southampton, Reino Unido
| | - D Veneziano
- Young Academic Urologist-Urotechnology Working Party (ESUT-YAU), European Association of Urology, Arnhem, Países Bajos; Department of Urology and Kidney Transplant, Grande Ospedale Metropolitano, Reggio Calabria, Italia
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Feizi N, Tavakoli M, Patel RV, Atashzar SF. Robotics and AI for Teleoperation, Tele-Assessment, and Tele-Training for Surgery in the Era of COVID-19: Existing Challenges, and Future Vision. Front Robot AI 2021; 8:610677. [PMID: 33937347 PMCID: PMC8079974 DOI: 10.3389/frobt.2021.610677] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Accepted: 01/18/2021] [Indexed: 12/18/2022] Open
Abstract
The unprecedented shock caused by the COVID-19 pandemic has severely influenced the delivery of regular healthcare services. Most non-urgent medical activities, including elective surgeries, have been paused to mitigate the risk of infection and to dedicate medical resources to managing the pandemic. In this regard, not only surgeries are substantially influenced, but also pre- and post-operative assessment of patients and training for surgical procedures have been significantly impacted due to the pandemic. Many countries are planning a phased reopening, which includes the resumption of some surgical procedures. However, it is not clear how the reopening safe-practice guidelines will impact the quality of healthcare delivery. This perspective article evaluates the use of robotics and AI in 1) robotics-assisted surgery, 2) tele-examination of patients for pre- and post-surgery, and 3) tele-training for surgical procedures. Surgeons interact with a large number of staff and patients on a daily basis. Thus, the risk of infection transmission between them raises concerns. In addition, pre- and post-operative assessment also raises concerns about increasing the risk of disease transmission, in particular, since many patients may have other underlying conditions, which can increase their chances of mortality due to the virus. The pandemic has also limited the time and access that trainee surgeons have for training in the OR and/or in the presence of an expert. In this article, we describe existing challenges and possible solutions and suggest future research directions that may be relevant for robotics and AI in addressing the three tasks mentioned above.
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Affiliation(s)
- Navid Feizi
- Canadian Surgical Technologies and Advanced Robotics (CSTAR), London Health Sciences Centre, and School of Biomedical Engineering, University of Western Ontario, London, ON, Canada
| | - Mahdi Tavakoli
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
| | - Rajni V. Patel
- Canadian Surgical Technologies and Advanced Robotics (CSTAR), London Health Sciences Centre, and School of Biomedical Engineering, University of Western Ontario, London, ON, Canada
- Department of Electrical and Computer Engineering, University of Western Ontario, London, ON, Canada
- Department of Surgery, University of Western Ontario, London, ON, Canada
| | - S. Farokh Atashzar
- Department of Electrical and Computer Engineering, New York University, New York, NY, United States
- Department of Mechanical and Aerospace Engineering, New York University, New York, NY, United States
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Wang WA, Dong P, Zhang A, Wang WJ, Guo CA, Wang J, Liu HB. Artificial intelligence: A new budding star in gastric cancer. Artif Intell Gastroenterol 2020; 1:60-70. [DOI: 10.35712/aig.v1.i4.60] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 11/01/2020] [Accepted: 11/27/2020] [Indexed: 02/06/2023] Open
Abstract
The pursuit of health has always been the driving force for the advancement of human society, and social development will be profoundly affected by every breakthrough in the medical industry. With the arrival of the information technology revolution era, artificial intelligence (AI) technology has been rapidly developed. AI has been combined with medicine but it has been less studied with gastric cancer (GC). AI is a new budding star in GC, and its contribution to GC is mainly focused on diagnosis and treatment. For early GC, AI’s impact is not only reflected in its high accuracy but also its ability to quickly train primary doctors, improve the diagnosis rate of early GC, and reduce missed cases. At the same time, it will also reduce the possibility of missed diagnosis of advanced GC in cardia. Furthermore, it is used to assist imaging doctors to determine the location of lymph nodes and, more importantly, it can more effectively judge the lymph node metastasis of GC, which is conducive to the prognosis of patients. In surgical treatment of GC, it also has great potential. Robotic surgery is the latest technology in GC surgery. It is a bright star for minimally invasive treatment of GC, and together with laparoscopic surgery, it has become a common treatment for GC. Through machine learning, robotic systems can reduce operator errors and trauma of patients, and can predict the prognosis of GC patients. Throughout the centuries of development of surgery, the history gradually changes from traumatic to minimally invasive. In the future, AI will help GC patients reduce surgical trauma and further improve the efficiency of minimally invasive treatment of GC.
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Affiliation(s)
- Wen-An Wang
- Graduate School, Gansu University of Traditional Chinese Medicine, Lanzhou 730000, Gansu Province, China
- Department of General Surgery, The 940th Hospital of Joint Logistics Support Force of Chinese People’s Liberation Army, Lanzhou 730050, Gansu Province, China
| | - Peng Dong
- Department of General Surgery, Lanzhou University Second Hospital, Lanzhou 730000, Gansu Province, China
| | - An Zhang
- Graduate School, Gansu University of Traditional Chinese Medicine, Lanzhou 730000, Gansu Province, China
- Department of General Surgery, The 940th Hospital of Joint Logistics Support Force of Chinese People’s Liberation Army, Lanzhou 730050, Gansu Province, China
| | - Wen-Jie Wang
- Department of General Surgery, Lanzhou University Second Hospital, Lanzhou 730000, Gansu Province, China
| | - Chang-An Guo
- Department of Emergency Medicine, Lanzhou University Second Hospital, Lanzhou 730000, Gansu Province, China
| | - Jing Wang
- Graduate School, Gansu University of Traditional Chinese Medicine, Lanzhou 730000, Gansu Province, China
- Department of General Surgery, The 940th Hospital of Joint Logistics Support Force of Chinese People’s Liberation Army, Lanzhou 730050, Gansu Province, China
| | - Hong-Bin Liu
- Department of General Surgery, The 940th Hospital of Joint Logistics Support Force of Chinese People’s Liberation Army, Lanzhou 730050, Gansu Province, China
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Geoghegan R, Song J, Singh A, Le T, Abiri A, Mendelsohn AH. Development of a Transoral Robotic Surgery Training Platform .. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2019:5851-5854. [PMID: 31947182 DOI: 10.1109/embc.2019.8856971] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Transoral robotic surgery (TORS) presents unique challenges due to difficulty manipulating surgical instruments within the tight confines of the oral cavity. Collisions between the end effectors and anatomical structures can be visualized through the endoscope; however, instrument shaft collisions are outside of the field-of-view. Acquiring the requisite skill set to minimize these collisions is challenging due to the lack of an appropriate training platform. In this paper, we present a TORS training platform with an integrated collision sensing system and real-time haptic feedback. Preliminary testing involved the recruitment of 10 Otolaryngology residents assigned to `feedback' (N=5) and `no feedback' (N=5) groups. Each trainee performed three mock surgical procedures involving the resection of a tumor from the base of the tongue. Superior surgical performance was observed in the feedback group suggesting that haptic feedback will enhance the acquisition of surgical skills.
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Andras I, Mazzone E, van Leeuwen FWB, De Naeyer G, van Oosterom MN, Beato S, Buckle T, O'Sullivan S, van Leeuwen PJ, Beulens A, Crisan N, D'Hondt F, Schatteman P, van Der Poel H, Dell'Oglio P, Mottrie A. Artificial intelligence and robotics: a combination that is changing the operating room. World J Urol 2019; 38:2359-2366. [PMID: 31776737 DOI: 10.1007/s00345-019-03037-6] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 11/21/2019] [Indexed: 12/12/2022] Open
Abstract
PURPOSE The aim of the current narrative review was to summarize the available evidence in the literature on artificial intelligence (AI) methods that have been applied during robotic surgery. METHODS A narrative review of the literature was performed on MEDLINE/Pubmed and Scopus database on the topics of artificial intelligence, autonomous surgery, machine learning, robotic surgery, and surgical navigation, focusing on articles published between January 2015 and June 2019. All available evidences were analyzed and summarized herein after an interactive peer-review process of the panel. LITERATURE REVIEW The preliminary results of the implementation of AI in clinical setting are encouraging. By providing a readout of the full telemetry and a sophisticated viewing console, robot-assisted surgery can be used to study and refine the application of AI in surgical practice. Machine learning approaches strengthen the feedback regarding surgical skills acquisition, efficiency of the surgical process, surgical guidance and prediction of postoperative outcomes. Tension-sensors on the robotic arms and the integration of augmented reality methods can help enhance the surgical experience and monitor organ movements. CONCLUSIONS The use of AI in robotic surgery is expected to have a significant impact on future surgical training as well as enhance the surgical experience during a procedure. Both aim to realize precision surgery and thus to increase the quality of the surgical care. Implementation of AI in master-slave robotic surgery may allow for the careful, step-by-step consideration of autonomous robotic surgery.
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Affiliation(s)
- Iulia Andras
- ORSI Academy, Melle, Belgium
- Department of Urology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Elio Mazzone
- ORSI Academy, Melle, Belgium
- Department of Urology, Onze Lieve Vrouw Hospital, Aalst, Belgium
- Department of Urology and Division of Experimental Oncology, URI, Urological Research Institute, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Fijs W B van Leeuwen
- ORSI Academy, Melle, Belgium
- Interventional Molecular Imaging Laboratory, Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands
- Department of Urology, Antoni Van Leeuwenhoek Hospital, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Geert De Naeyer
- ORSI Academy, Melle, Belgium
- Department of Urology, Onze Lieve Vrouw Hospital, Aalst, Belgium
| | - Matthias N van Oosterom
- Interventional Molecular Imaging Laboratory, Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands
- Department of Urology, Antoni Van Leeuwenhoek Hospital, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Tessa Buckle
- Interventional Molecular Imaging Laboratory, Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Shane O'Sullivan
- Department of Pathology, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Pim J van Leeuwen
- Department of Urology, Antoni Van Leeuwenhoek Hospital, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Alexander Beulens
- Department of Urology, Catharina Hospital, Eindhoven, The Netherlands
- Netherlands Institute for Health Services (NIVEL), Utrecht, The Netherlands
| | - Nicolae Crisan
- Department of Urology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Frederiek D'Hondt
- ORSI Academy, Melle, Belgium
- Department of Urology, Onze Lieve Vrouw Hospital, Aalst, Belgium
| | - Peter Schatteman
- ORSI Academy, Melle, Belgium
- Department of Urology, Onze Lieve Vrouw Hospital, Aalst, Belgium
| | - Henk van Der Poel
- Department of Urology, Antoni Van Leeuwenhoek Hospital, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Paolo Dell'Oglio
- ORSI Academy, Melle, Belgium.
- Department of Urology, Onze Lieve Vrouw Hospital, Aalst, Belgium.
- Interventional Molecular Imaging Laboratory, Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.
- Department of Urology, Antoni Van Leeuwenhoek Hospital, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
| | - Alexandre Mottrie
- ORSI Academy, Melle, Belgium
- Department of Urology, Onze Lieve Vrouw Hospital, Aalst, Belgium
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