Learning curves of robot-assisted pedicle screw fixations based on the cumulative sum test

BACKGROUND In robot-assisted(RA)spine surgery,the relationship between the surgical outcome and the learning curve remains to be evaluated.AIM To analyze the learning curve of RA pedicle screw fixation(PSF)through fitting the operation time curve based on the cumulative summation method.METHODS RA PSFs that were initially completed by two surgeons at the Beijing Jishuitan Hospital from July 2016 to March 2019 were analyzed retrospectively.Based on the cumulative sum of the operation time,the learning curves of the two surgeons were drawn and fit to polynomial curves.The learning curve was divided into the early and late stages according to the shape of the fitted curve.The operation time and screw accuracy were compared between the stages.RESULTS The turning point of the learning curves from Surgeons A and B appeared in the 18th and 17th cases,respectively.The operation time[150(128,188)min vs 120(105,150)min,P=0.002]and the screw accuracy(87.50%vs 96.30%,P=0.026)of RA surgeries performed by Surgeon A were significantly improved after he completed 18 cases.In the case of Surgeon B,the operation time(177.35±28.18 min vs 150.00±34.64 min,P=0.024)was significantly reduced,and the screw accuracy(91.18%vs 96.15%,P=0.475)was slightly improved after the surgeon completed 17 RA surgeries.CONCLUSION After completing 17 to 18 cases of RA PSFs,surgeons can pass the learning phase of RA technology.The operation time is reduced afterward,and the screw accuracy shows a trend of improvement.

Artificial intelligence in orthopedic surgery:current state and future perspective

Artificial intelligence(AI),first proposed by Prof.John McCarthy in 1956,aims to reproduce human intelligence using computers.Machine learning(ML)is a form of AI that uses computational algorithms that learn and improve with experience.[1]The two main forms of ML are supervised and unsupervised.In supervised ML,algorithms are given labeled data,which is used to predict disease outcomes in a new patient.In contrast,unsupervised ML is used to identify patterns without training;the algorithm learns the inherent structure of the data by searching for common characteristics.[1]