Automating Endoscope Motion in Robotic Surgery: A Usability Study on da Vinci-Assisted Ex Vivo Neobladder Reconstruction

Col, Tommaso Da; Caccianiga, Guido; Catellani, Michele; Mariani, Andrea; Ferro, Matteo; Cordima, Giovanni; Momi, Elena De; Ferrigno, Giancarlo; Cobelli, O. De

doi:10.3389/frobt.2021.707704

“…For the experiment, this surgical procedure was reproduced on an ex vivo porcine model in a simplified version, with the aim of minimizing the waste of animal tissue. Three steps were selected to be reproduced: (i) bowel distension and measurement; (ii) detubularization; (iii) neobladder plication and suturing [ 14 ]. Each surgeon was asked to perform the simplified neobladder reconstruction twice, randomly starting with MC or AC.…”

Section: Methodsmentioning

confidence: 99%

Robot-assisted ex vivo neobladder reconstruction: preliminary results of surgical skill evaluation

Chen

¹

,

Terlizzi

²

,

Col

³

et al. 2022

Int J CARS

Self Cite

4

0

View full text Add to dashboard Cite

Purpose Advanced developments in the medical field have gradually increased the public demand for surgical skill evaluation. However, this assessment always depends on the direct observation of experienced surgeons, which is time-consuming and variable. The introduction of robot-assisted surgery provides a new possibility for this evaluation paradigm. This paper aims at evaluating surgeon performance automatically with novel evaluation metrics based on different surgical data. Methods Urologists ($$n=10$$ n = 10 ) from a hospital were requested to perform a simplified neobladder reconstruction on an ex vivo setup twice with different camera modalities ($$n=2$$ n = 2 ) randomly. They were divided into novices and experts ($$n=5$$ n = 5 , respectively) according to their experience in robot-assisted surgeries. Different performance metrics ($$n=2$$ n = 2 ) are proposed to achieve the surgical skill evaluation, considering both instruments and endoscope. Also, nonparametric tests are adopted to check if there are significant differences when evaluating surgeons performance. Results When grouping according to four stages of neobladder reconstruction, statistically significant differences can be appreciated in phase 1 ($$p=0.0284$$ p = 0.0284 ) and phase 2 ($$p=0.01953$$ p = 0.01953 ) with normalized time-related metrics and camera movement-related metrics, respectively. On the other hand, considering experience grouping shows that both metrics are able to highlight statistically significant differences between novice and expert performances in the control protocol. It also shows that the camera-related performance of experts is significantly different ($$p=0.003153$$ p = 0.003153 ) when handling the endoscope manually and when it is automatic. Conclusion Surgical skill evaluation, using the approach in this paper, can effectively measure surgical procedures of surgeons with different experience. Preliminary results demonstrate that different surgical data can be fully utilized to improve the reliability of surgical evaluation. It also demonstrates its versatility and potential in the quantitative assessment of various surgical operations.

show abstract

“…Automating these tasks enhances the autonomy of surgical assistants, reducing the workload of surgeons during procedures. Da Col et al [ 143 ] developed an autonomous camera navigation system for the da Vinci Research Suite to enhance the robustness of SRS in bladder reconstruction. This system, which integrates autonomous endoscope navigation, was assessed by ten urologists who conducted ex vivo neobladder reconstruction.…”

Section: Characteristics Of Ai‐aided Srsmentioning

confidence: 99%

Evolution of Surgical Robot Systems Enhanced by Artificial Intelligence: A Review

Liu,

Wu,

Sang

et al. 2024

Advanced Intelligent Systems

1

0

View full text Add to dashboard Cite

Surgical robot systems (SRS) represent an innovative cross‐disciplinary research field using robotic technology to assist surgeons in operations. Current bottlenecks in SRS, such as the limited ability to process complex information and make surgical decisions, have not been effectively solved. Artificial intelligence (AI) is a valuable technique for simulating and extending human intelligence. AI offers a new direction and impetus for SRS by enhancing performance in areas such as perception, navigation, surgical planning, and control strategies. This review introduces the developmental history of AI‐aided SRS, summarizes the basic SRS architecture, and analyzes how AI can improve SRS performance. Classical cases of AI‐aided SRS, the impact of evidence in clinical settings, and associated ethical and legal considerations are explored. Finally, the challenges in AI‐aided SRS are discussed, including algorithm development, surgical data science, human–robot coordination, and trust building between humans and robots.

show abstract

“…Many methods have been proposed to track instruments. [13] and [14] have used the kinematic chain of the robotic manipulator to track the position of instruments and employ it in the control of the camera. However, this method cannot be used in some traditional surgeries, such as in neurosurgery where surgeons manipulate instruments freehand without relying on robotic assistance.…”

Section: B Instrument Trackingmentioning

confidence: 99%

Towards Exoscope Automation in Neurosurgery: A Markerless Visual-Servoing Approach

Iovene

¹

,

Casella

²

,

Iordache

³

et al. 2023

IEEE Trans. Med. Robot. Bionics

6

0

View full text Add to dashboard Cite

Exoscopes are a promising tool for neurosurgeons, offering improved visualisation and ergonomics compared with traditional surgical microscopes. They consist of an external scope that projects the surgical field onto a 2D or 3D monitor, providing a wider field of view and better access to the surgical site. Despite the advantages, exoscopes present some limitations, such as the need for manual or foot joystick repositioning, which can disrupt the flow of the procedure and increase the risk of user error. In this study, a markerless visual-servoing approach for autonomous exoscope control is proposed to address these limitations and enhance the ergonomics and reduce the physical and cognitive load compared with traditional joystick control. The system uses visual information from the operating field to control the exoscope, eliminating the need for markers or additional tracking devices. The proposed approach was validated using a 7-DOF robotic manipulator with a stereo camera in an eye-in-hand configuration. Results showed that the system achieved 89 % accuracy in detecting the target and tracking its movement with a tracking error ranging from 0.50 ± 0.17 cm for low-speed movements to 1.38 ± 0.73 cm for high-speed movements. The proposed system also demonstrated improved efficiency, with a shorter execution time of 72.07 ± 19.36 s compared with 106.52±18.50 s for the foot-joystick control. Additionally, the time out of the FoV was significantly higher in the joystick control mode and the frequency of appearance of the instrument in the centre of the image was higher when using the proposed system. The NASA TLX results indicated lower physical and cognitive load compared with the joystick control-based modality.

show abstract

Automating Endoscope Motion in Robotic Surgery: A Usability Study on da Vinci-Assisted Ex Vivo Neobladder Reconstruction

Cited by 17 publications

References 19 publications

Robot-assisted ex vivo neobladder reconstruction: preliminary results of surgical skill evaluation

Robot-assisted ex vivo neobladder reconstruction: preliminary results of surgical skill evaluation

Evolution of Surgical Robot Systems Enhanced by Artificial Intelligence: A Review

Towards Exoscope Automation in Neurosurgery: A Markerless Visual-Servoing Approach

Contact Info

Product

Resources

About