Learning Curves for Robotic Surgery: a Review of the Recent Literature

Mazzon, Giorgio; Sridhar, Ashwin; Busuttil, Gerald; Thompson, James; Nathan, Senthil; Briggs, T.; Kelly, John D.; Shaw, Greg

doi:10.1007/s11934-017-0738-z

Cited by 55 publications

(34 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The learning process for any new technique is difficult to define and harder still to quantify [5]. We have demonstrated this centre's LC objectively, with expectations to reach expert TCT at 33 cases, and equivalence with the open procedure at 26.…”

Section: Discussionmentioning

confidence: 82%

See 1 more Smart Citation

Urology robotic journey: demonstrating the learning curve in robot-assisted pyeloplasties (RALP)

et al. 2019

View full text Add to dashboard Cite

Introduction The advantages of robot-assisted pyeloplasties (RALP) have been widely documented and increasingly familiar. A 'learning curve' (LC) is difficult to define and its objective review remains problematic. We aimed to evaluate the LC, and outcomes, of RALP performed at our newly established robotic centre. Method and materials After the appropriate training, robot-assisted surgery (RAS) commenced at our centre. A prospective database was maintained regarding demographics and intra-operative timings of all cases, post-operative stay and complications. The data were statistically interrogated and linear regression analysis was performed. Results Between April 2014 and September 2018, 29 urological RAS were performed including 23 RALP. The total operating and total console times were significantly decreased over time, with no significant differences seen in length of stay or complication rates. Regression analysis predicted achievement of open pyeloplasty time (148 min) at 26 cases and 'expert' console time (58 min) by case 34. Median length of stay was 2 days and operative success rate 96%. Discussion RAS is proliferating across the globe. LC is difficult to define objectively. We suggest that the points described here in this 'learning journey' can be applied to other nascent departments and that RALP is safe and feasible within a developing urology unit.

show abstract

Section: Discussionmentioning

confidence: 82%

“…A 'learning curve' (LC) is difficult to define. In a surgical context, it is commonly described as "the time it takes for a surgeon to achieve the accepted levels of competence in a certain procedure [5]." The LC applies to all team members, not just the primary surgeon, and many factors govern its progress.…”

Section: Introductionmentioning

confidence: 99%

Urology robotic journey: demonstrating the learning curve in robot-assisted pyeloplasties (RALP)

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Evaluating the learning curve is one way of describing the changes observed in surgical outcomes. Learning curves should increase with surgeon experience and would therefore be useful in the planning of training programs [25]. Providing neurobehavioral performance details to explain different learning curves in surgical training would also be useful to improve training programs.…”

Section: Discussionmentioning

confidence: 99%

Training Skills in Minimally Invasive, Robotic and Open Surgery: Brain Activation as an Opportunity for Learning

et al. 2020

View full text Add to dashboard Cite

Introduction: The advantages of the robotic approach in surgery are undisputed. However, during surgical training, how this technique influences the learning curve has not been described. We provide a tentative model for analyzing the learning curves associated with observation and active participation in learning different surgical techniques, using functional imaging. Methods: Forty medical students were enrolled and assigned to 4 groups who underwent training in robotic (ROB), laparoscopic (LAP), or open (OPEN) surgery, and a control group that performed motor training without surgical instruments. Surgical/motor training included six 1-h sessions completed over 6 days of the same week. All subjects underwent functional magnetic resonance imaging (fMRI) scanning sessions, before and after surgical training during. Results: Twenty-three participants completed the study. The 3 surgical groups exhibited different learning curves during training. The main effects of the day of training

show abstract

“…The laparoscopic learning curve however, has a higher suture completion time at the start with a steeper curve than in robot assisted suturing. A 'steep' learning curve is often mistaken for a period of learning where the progress is slow, however, it refers to the rapid improvement in outcomes due to learning [2]. This indicates fast learning in laparoscopic complex suturing and more intuitive abilities with less improvement in robot assisted surgery.…”

Section: Discussionmentioning

confidence: 99%

“…The learning curve is the amount of procedural training required for a surgeon to achieve competence in a new procedure. The duration of the learning curve to reach a particular outcome depends on the specific outcomes being investigated [2]. During the early phase of learning, the novice can expect to find longer task duration and poorer overall outcomes with the chance of a higher rate of complications [1,[3][4][5].…”

mentioning

confidence: 99%

Robot assisted versus laparoscopic suturing learning curve in a simulated setting

Leijte¹,

Blaauw²,

Workum

et al. 2019

Surg Endosc

View full text Add to dashboard Cite

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.

show abstract

Learning Curves for Robotic Surgery: a Review of the Recent Literature

Cited by 55 publications

References 36 publications

Urology robotic journey: demonstrating the learning curve in robot-assisted pyeloplasties (RALP)

Urology robotic journey: demonstrating the learning curve in robot-assisted pyeloplasties (RALP)

Training Skills in Minimally Invasive, Robotic and Open Surgery: Brain Activation as an Opportunity for Learning

Robot assisted versus laparoscopic suturing learning curve in a simulated setting

Contact Info

Product

Resources

About