A systematic review of the learning curve in robotic surgery: range and heterogeneity

Kassite, I.; Bejan-Angoulvant, Théodora; Lardy, H.; Binet, Aurélien

doi:10.1007/s00464-018-6473-9

Cited by 62 publications

(47 citation statements)

References 110 publications

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“…The graphical representation of the rate of learning how to place implants with better accuracy over time or repeated experiences can be defined as the learning curve (Kassite, Bejan‐Angoulvant, Lardy, & Binet, ). Such a curve was implied by the aforementioned two previous studies (Block et al, ; Stefanelli et al, ), revealing a trend towards a lower deviation rate after 15 to 25 cases.…”

Section: Discussionmentioning

confidence: 99%

Accuracy of dental implant placement via dynamic navigation or the freehand method: A split‐mouth randomized controlled clinical trial

Aydemir

Arısan

2019

Clinical Oral Implants Res

102

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Objectives The aim of this split‐mouth randomized controlled clinical trial was to compare the deviations of planned and placed implants placed by the assistance of a micron tracker‐based dynamic navigation device or freehand methods. Material and methods A thermoplastic fiducial marker was adapted on the anterior teeth, and cone‐beam computerized tomography was used for imaging. A minimum of one implant was planned for each side of the posterior maxilla, and the dynamic navigation device or freehand method was randomly used for surgical insertion. Deviations were measured by matching the planning data with a final CBCT image. Linear deviations (mm) between the planned and placed implants were the primary outcome. The results were analysed by generalized linear mixed models (p < .05). (NCT03471208). Results A total of 92 implants were placed to 32 volunteers, and 86 implants were included in the final analysis. For the linear deviations, mean of differences (Δ) was 0.72mm (Standard deviation (SD): 0.26); (95% Confidence interval (CI): 0.39–1.02) in the shoulder of the implants (p < .001) and 0.69mm (SD: 0.36); (95% CI: 0.19–1.19) in the tip of the implants (p < .001). For the angular deviations, Δ was 5.33° (SD: 1.63); (95% CI: 7.17–3.48); (p < .001). Conclusions The navigation technique can be used to transfer virtual implant planning to the patient's jaw with increased accuracy.

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Section: Discussionmentioning

confidence: 99%

Accuracy of dental implant placement via dynamic navigation or the freehand method: A split‐mouth randomized controlled clinical trial

Aydemir

Arısan

2019

Clinical Oral Implants Res

102

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“…This method transforms the raw data into the running total of data deviations from their group mean, enabling researchers to visualize the data for trends not discernible with other approaches. An important advantage of the CUSUM analysis is that it is able to identify subtle and slow changes, which was otherwise not visualized in the robot assisted surgery learning curve [10,11]. Finally, a sub-analysis between the novice and laparoscopic experienced robot assisted participants to determine the influence of laparoscopic experience on the robot assisted learning curve outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…The main strength of this study is the randomization of the novice group, the relative high number of participants and the inclusion of multiple assessment parameters. This study was also aimed at using multiple assessment parameters for proficiency as stated by the recent review from Kassite et al [10]. These parameters are found in the correctness of the knot and precision of the needle punctures; the safety parameter 'out of view' which indicates the risk on collateral damage and therefore, is an indicator for complications; and time to finish the procedure.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

“…surgical skill level that resembles proficiency. The use of a single parameter, such as time to complete the task, is too simplistic since it does not take into account complications or patient related outcomes [10]. Therefore, multidimensional assessment is recommended, which incorporates the need of simulator assessment feedback with objective assessment parameters, including safety parameters, leading to a competency-or outcome-anchored appraisal of skill performance and progression [1,[11][12][13].…”

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confidence: 99%

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Robot assisted versus laparoscopic suturing learning curve in a simulated setting

Leijte¹,

Blaauw²,

Workum

et al. 2019

Surg Endosc

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Background Compared to conventional laparoscopy, robot assisted surgery is expected to have most potential in difficult areas and demanding technical skills like minimally invasive suturing. This study was performed to identify the differences in the learning curves of laparoscopic versus robot assisted suturing. Method Novice participants performed three suturing tasks on the EoSim laparoscopic augmented reality simulator or the RobotiX robot assisted virtual reality simulator. Each participant performed an intracorporeal suturing task, a tilted plane needle transfer task and an anastomosis needle transfer task. To complete the learning curve, all tasks were repeated up to twenty repetitions or until a time plateau was reached. Clinically relevant and comparable parameters regarding time, movements and safety were recorded. Intracorporeal suturing time and cumulative sum analysis was used to compare the learning curves and phases. Results Seventeen participants completed the learning curve laparoscopically and 30 robot assisted. Median first knot suturing time was 611 s (s) for laparoscopic versus 251 s for robot assisted (p < 0.001), and this was 324 s versus 165 (sixth knot, p < 0.001) and 257 s and 149 s (eleventh knot, p < 0.001) respectively on base of the found learning phases. The percentage of 'adequate surgical knots' was higher in the laparoscopic than in the robot assisted group. First knot: 71% versus 60%, sixth knot: 100% versus 83%, and eleventh knot: 100% versus 73%. When assessing the 'instrument out of view' parameter, the robot assisted group scored a median of 0% after repetition four. In the laparoscopic group, the instrument out of view increased from 3.1 to 3.9% (left) and from 3.0 to 4.1% (right) between the first and eleventh knot (p > 0.05). Conclusion The learning curve of minimally invasive suturing shows a shorter task time curve using robotic assistance compared to the laparoscopic curve. However, laparoscopic outcomes show good end results with rapid outcome improvement.

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“…Smith et al 68 Modern curricula for the robot specific surgical training through simulation are being developed and improved to revolutionize robotic surgeries. 70 These simulations are aimed at developing self-driven and mentor-free skills using fully immersive 3D virtual environments, which is often referred to as VR. 13 However, neither quantifiable psychomotor skills nor haptic feedback is present in these training simulations.…”

Section: Mst In Surgical Skill Training Curriculamentioning

confidence: 99%

Novel virtual reality based training system for fine motor skills: Towards developing a robotic surgery training system

Vasudevan

Isaac

Sadanand

et al. 2020

Robotics Computer Surgery

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Background: Training surgeons to use surgical robots are becoming part of surgical training curricula. We propose a novel method of training fine-motor skills such as Microscopic Selection Task (MST) for robot-assisted surgery using virtual reality (VR) with objective quantification of performance. We also introduce vibrotactile feedback (VTFB) to study its impact on training performance. Methods: We use a VR-based environment to perform MST with varying degrees of difficulties. Using a well-known human-computer interaction paradigm and incorporating VTFB, we quantify the performance: speed, precision and accuracy. Results: MST with VTFB showed statistically significant improvement in performance metrics leading to faster completion of MST with higher precision and accuracy compared to that without VTFB. Discussion: The addition of VTFB to VR-based training for robot-assisted surgeries may improve performance outcomes in real robotic surgery. VTFB, along with proposed performance metrics, can be used in training curricula for robot-assisted surgeries. K E Y W O R D S human-computer interaction, psychomotor, surgical robot, true positive rate, vibrotactile feedback 1 | INTRODUCTION Surgical robots are increasingly being used in almost every multispecialty hospital. Currently, they have multiple applications in pelvic, abdominal, select-chest and neurosurgical procedures. 1-6 The technology of surgical robots today has advanced sufficiently to provide a high degree of freedom in the robots' arm movements, control and stereoscopic vision. The improved user-interface of these technologies has led to better on-the-job performance and faster learning curves in the use of surgical robots such as da Vinci®, 7 ROSA® 8,9 and Senhance™. 10 Psychomotor skills play a vital role in surgical task performance with increased speed, accuracy, sensitivity and precision. Surgical robotic systems typically seek to help surgeons perform surgeries with higher accuracy, faster responses to intraoperative complications and increased dexterity. Quantification of psychomotor skills became essential with robotic surgical training systems that emphasize rare complex surgical scenarios, challenges and evaluations of these skills. Training and evaluation of these skills are crucial for effective analysis of the surgeons in the tasks as mentioned earlier. The evaluation of the performance is currently through cognitive, technical and non-technical skills. Technical skills (TS)

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A systematic review of the learning curve in robotic surgery: range and heterogeneity

Cited by 62 publications

References 110 publications

Accuracy of dental implant placement via dynamic navigation or the freehand method: A split‐mouth randomized controlled clinical trial

Accuracy of dental implant placement via dynamic navigation or the freehand method: A split‐mouth randomized controlled clinical trial

Robot assisted versus laparoscopic suturing learning curve in a simulated setting

Novel virtual reality based training system for fine motor skills: Towards developing a robotic surgery training system

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