An objective approach to evaluate novice robotic surgeons using a combination of kinematics and stepwise cumulative sum (CUSUM) analyses

Lyman, William B.; Passeri, Michael; Murphy, Keith J.; Siddiqui, Imran; Khan, Adeel; Iannitti, David A.; Martinie, John B.; Baker, E.; Vrochides, Dionisios

doi:10.1007/s00464-020-07708-z

Cited by 14 publications

(24 citation statements)

References 26 publications

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“…Moreover, the comparison of studies is impaired by the different scales and scores used to measure technical skill. Some studies use ordinal scales with different numbers of skill levels (good vs. bad 20,26,[35][36][37]41,42,44,48,51,53 , novice vs. intermediate vs. expert 21,25,[28][29][30][31]38,40,43,45,49,52,[54][55][56] ) others use different interval scales (OSATS scores 28,40,50 , GEARS scores 24,54 , or Likert scales 42 ). This finding represents the general difficulty to define and measure technical surgical skills.…”

Section: Discussionmentioning

confidence: 99%

“…Technical surgical skills can be assessed using expert levels (ordinal scale) or proficiency scores (interval scale) (Figure 1). In 27 of the studies (73%) an ordinal scale was applied 20,21,[25][26][27][28][29][30][31][35][36][37][38][40][41][42][43][44][45]48,49,[51][52][53][54][55][56] . In twelve studies (32%) participants were categorized in two different skill levels 20,26,30,[35][36][37]41,42,44,48,51,53 and in 14 studies (38%) into three different expert levels (novice, intermediate, expert) 21,25,28,29,31,38,40,43,…”

Section: Assessmentmentioning

confidence: 99%

“…All AI models in this review are either ML-or DL-based. ML was applied in 12 studies (32%) 24,26,29,30,35,36,40,42,47,48,50,51 and DL in 27 studies (73%) [20][21][22][23]25,27,28,[31][32][33][34][37][38][39][40][41][42][43][44][45][46]49,[52][53][54][55][56] . Two studies (5%) used a combination of ML and DL models 42,44 .…”

Section: Ai Modelsmentioning

confidence: 99%

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Technical Skill Assessment in Minimally Invasive Surgery Using Artificial Intelligence: A Systematic Review

Pedrett

Mascagni

Beldi

et al. 2022

Preprint

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Objective: To review artificial intelligence (AI) based applications for the assessment of technical skills in minimally invasive surgery. Background: As technical skill assessment in surgery relies on expert opinion, it is time-consuming, costly, and often lacks objectivity. Analysis of routinely generated data by AI methods has the potential for automatic technical skill assessment in minimally invasive surgery. Methods: A systematic search of Medline, Embase, Web of Science and IEEE Xplore was performed to identify original articles reporting the use of AI in the assessment of technical skill in minimally invasive surgery. Risk of bias (RoB) and quality of included studies were analyzed according to Quality Assessment of Diagnostic Accuracy Studies criteria and the modified Joanna Briggs Institute checklists, respectively. Findings were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Results: In total, 1467 articles were identified, 37 articles met eligibility criteria and were analyzed. Motion data extracted from surgical videos (49%) or kinematic data from robotic systems or sensors (46%) were the most frequent input data for AI. Most studies used deep learning (73%) and predicted technical skills using an ordinal assessment scale (73%) with good accuracies in simulated settings. However, all proposed models were in development stage, only 8% were externally validated and 16% showed a low RoB. Conclusion: AI is promising to automate technical skill assessment in minimally invasive surgery. However, models should be benchmarked on representative datasets using predefined performance metrics and tested in clinical implementation studies.

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Section: Discussionmentioning

confidence: 99%

Section: Assessmentmentioning

confidence: 99%

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Technical Skill Assessment in Minimally Invasive Surgery Using Artificial Intelligence: A Systematic Review

Pedrett

Mascagni

Beldi

et al. 2022

Preprint

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“…Team building simulation Use of simulated scenarios to improve team building exercises and procedures Zheng et al (2008) [ 19 ] 18. Cumulative sum analytics Use of kinematic data to objectively evaluate surgical performance Lyman et al (2020) [ 20 ] 19. Lean principles Identification and elimination of unnecessary resource utilization in ERAS® pathways to decrease time, resources, and cost Collar et al (2012) [ 21 ] 20.…”

mentioning

confidence: 99%

“…Other disruptive technologies encountered in conjunction with ERAS® include but are not limited to simulation for team building, cumulative sum analytics (CUSUM), and prescriptive analytics [ 8 , 20 , 26 ]. While each of these and the presented disruptive technologies begin through, yes, disruption, and perhaps, yes, significant frustration, each offers benefit and improvement if given the chance.…”

mentioning

confidence: 99%

The role of disruptive technologies and approaches in ERAS®: erupting change through disruptive means

Baimas-George

Demartines

Vrochides

2022

Langenbecks Arch Surg

Self Cite

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Current progress in robotic hepatobiliary and pancreatic surgery at a high‐volume center

McCarron,

Vrochides,

Martinie

2023

Annals of Gastroent Surgery

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There has been steady growth in the adoption of robotic HPB procedures world‐wide over the past 20 years, but most of this increase has occurred only recently. Not surprisingly, the vast majority of robotics has been in the United States, with very few, select centers of adoption in Italy, South Korea, and Brazil, to name a few. We began our robotic HPB program in 2008, well before almost all other centers in the world, with the most notable exception of Giullianotti and colleagues. Our program began gradually, with smaller cases carefully selected to optimize the strengths of the original robotic platform and included complex biliary and pancreatic resections. We performed the first reported series of choledochojejunostomy for benign biliary strictures and first series of completion cholecystectomies. We began performing robotic distal pancreatectomies and longitudinal pancreaticojejunostomies, reporting our early experience for each of these procedures. Over time we progressed to robotic pancreaticoduodenectomies. Initially, these were performed with planned conversions until we were able to optimize efficiency. Now we have performed over 200 robotic whipples, reaching a 100% robotic completion rate by 2020. Finally, we have added robotic major hepatectomies, including resections for hilar cholangiocarcinoma to our repertoire. Since the program began, we have performed over 1600 robotic HPB cases. Outcomes from our program have shown superior lymph node harvest, lower DGE rates, shorter hospitalizations, and fewer rehab admissions with similar overall complications to open and laparoscopic procedures, signifying that over time a robotic HPB program is not only feasible but advantageous as well.

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An objective approach to evaluate novice robotic surgeons using a combination of kinematics and stepwise cumulative sum (CUSUM) analyses

Cited by 14 publications

References 26 publications

Technical Skill Assessment in Minimally Invasive Surgery Using Artificial Intelligence: A Systematic Review

Technical Skill Assessment in Minimally Invasive Surgery Using Artificial Intelligence: A Systematic Review

The role of disruptive technologies and approaches in ERAS®: erupting change through disruptive means

Current progress in robotic hepatobiliary and pancreatic surgery at a high‐volume center

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