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A High-Fidelity Artificial Urological System for the Quantitative Assessment of Endoscopic Skills. J Funct Biomater 2022; 13:jfb13040301. [PMID: 36547561 PMCID: PMC9784860 DOI: 10.3390/jfb13040301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/07/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Minimally-invasive surgery is rapidly growing and has become a standard approach for many operations. However, it requires intensive practice to achieve competency. The current training often relies on animal organ models or physical organ phantoms, which do not offer realistic surgical scenes or useful real-time feedback for surgeons to improve their skills. Furthermore, the objective quantitative assessment of endoscopic skills is also lacking. Here, we report a high-fidelity artificial urological system that allows realistic simulation of endourological procedures and offers a quantitative assessment of the surgical performance. The physical organ model was fabricated by 3D printing and two-step polymer molding with the use of human CT data. The system resembles the human upper urinary tract with a high-resolution anatomical shape and vascular patterns. During surgical simulation, endoscopic videos are acquired and analyzed to quantitatively evaluate performance skills by a customized computer algorithm. Experimental results show significant differences in the performance between professional surgeons and trainees. The surgical simulator offers a unique chance to train endourological procedures in a realistic and safe environment, and it may also lead to a quantitative standard to evaluate endoscopic skills.
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Da Col T, Caccianiga G, Catellani M, Mariani A, Ferro M, Cordima G, De Momi E, Ferrigno G, de Cobelli O. Automating Endoscope Motion in Robotic Surgery: A Usability Study on da Vinci-Assisted Ex Vivo Neobladder Reconstruction. Front Robot AI 2021; 8:707704. [PMID: 34901168 PMCID: PMC8656430 DOI: 10.3389/frobt.2021.707704] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 11/01/2021] [Indexed: 11/18/2022] Open
Abstract
Robots for minimally invasive surgery introduce many advantages, but still require the surgeon to alternatively control the surgical instruments and the endoscope. This work aims at providing autonomous navigation of the endoscope during a surgical procedure. The autonomous endoscope motion was based on kinematic tracking of the surgical instruments and integrated with the da Vinci Research Kit. A preclinical usability study was conducted by 10 urologists. They carried out an ex vivo orthotopic neobladder reconstruction twice, using both traditional and autonomous endoscope control. The usability of the system was tested by asking participants to fill standard system usability scales. Moreover, the effectiveness of the method was assessed by analyzing the total procedure time and the time spent with the instruments out of the field of view. The average system usability score overcame the threshold usually identified as the limit to assess good usability (average score = 73.25 > 68). The average total procedure time with the autonomous endoscope navigation was comparable with the classic control (p = 0.85 > 0.05), yet it significantly reduced the time out of the field of view (p = 0.022 < 0.05). Based on our findings, the autonomous endoscope improves the usability of the surgical system, and it has the potential to be an additional and customizable tool for the surgeon that can always take control of the endoscope or leave it to move autonomously.
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Affiliation(s)
- Tommaso Da Col
- Neuro-Engineering and Medical Robotics Laboratory (NEARLab), Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Guido Caccianiga
- Haptic Intelligence Department, Max-Planck-Institute for Intelligent Systems, Stuttgart, Germany
| | - Michele Catellani
- Division of Urology, European Institute of Oncology, IRCCS, Milan, Italy
| | - Andrea Mariani
- Excellence in Robotics and AI Department, Sant’Anna School of Advanced Studies, Pisa, Italy
| | - Matteo Ferro
- Division of Urology, European Institute of Oncology, IRCCS, Milan, Italy
| | - Giovanni Cordima
- Division of Urology, European Institute of Oncology, IRCCS, Milan, Italy
| | - Elena De Momi
- Neuro-Engineering and Medical Robotics Laboratory (NEARLab), Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Giancarlo Ferrigno
- Neuro-Engineering and Medical Robotics Laboratory (NEARLab), Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Ottavio de Cobelli
- Division of Urology, European Institute of Oncology, IRCCS, Milan, Italy
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