Objective:To analyze the learning curve of TiRobot-assisted lumbar pedicle screw fixation (LPSF) by cumulative sum (CUSUM) test method.Methods:The clinical data of 50 patients who underwent TiRobot-assisted LPSF from January 2020 to December 2022 in Beijing Friendship Hospital, Capital Medical University were retrospectively analyzed. CUSUM analysis and learning curve fitting were performed with robot usage time as the main indicator with the time for each step refined (robot registration time, path planning time and guide wire placement time), to select the best learning curve fitting model with the R2 value closest to 1. Using the turning point of the learning curve as the boundary, the learning curve was divided into two stages as learning stage and maturity stage, and then the observation indexes were compared between the two stages. Results:All 50 patients successfully completed the surgery without perioperative complications, with a total of 244 pedicle screws implanted. The total robot usage time and robot registration time showed a gradually decreasing trend with the increase of case number, and the learning curves were successfully fitted and reached their peaks at the seventeenth and thirteenth cases respectively. The entire learning process was divided into learning stage (17 cases) and maturity stage (33 cases) based on the turning point of the learning curve of total robot usage time. The path planning time and guide wire placement time did not show significant changes with the increase in the case number. The total robot usage time, robot registration time and the intraoperative blood loss in the learning stage were significantly higher than those in the maturity stage: (35.35 ± 1.58) min vs. (30.61 ± 0.43) min, (20.83 ± 1.56) min vs. (14.94 ± 0.29) min and 400 (150, 500) ml vs. 200 (110, 300) ml, the guide wire placement time of per screw was significantly lower than that in the maturity stage: 2.00 (1.83, 2.34) min/screw vs. 2.33 (2.13, 2.69) min/screw, and there were statistical differences ( P<0.05 or <0.01). There were no statistical difference in the path planning time, path planning time of per screw, guide wire placement time and the accuracy of screw placement between two stages ( P>0.05). Conclusions:TiRobot-assisted LPSF is a new technology with safety and effectiveness, and it has a relatively short learning curve. To achieve technological maturity, at least 17 surgeries are required with accumulated experience, and the robot registration is the main step of the learning process. After reaching maturity stage, the robot usage time is significantly shortened and intraoperative trauma is significantly reduced while the relatively high screw placement accuracy is ensured.

Objective:To analyze the learning curve of TiRobot-assisted lumbar pedicle screw fixation (LPSF) by cumulative sum (CUSUM) test method.Methods:The clinical data of 50 patients who underwent TiRobot-assisted LPSF from January 2020 to December 2022 in Beijing Friendship Hospital, Capital Medical University were retrospectively analyzed. CUSUM analysis and learning curve fitting were performed with robot usage time as the main indicator with the time for each step refined (robot registration time, path planning time and guide wire placement time), to select the best learning curve fitting model with the R2 value closest to 1. Using the turning point of the learning curve as the boundary, the learning curve was divided into two stages as learning stage and maturity stage, and then the observation indexes were compared between the two stages. Results:All 50 patients successfully completed the surgery without perioperative complications, with a total of 244 pedicle screws implanted. The total robot usage time and robot registration time showed a gradually decreasing trend with the increase of case number, and the learning curves were successfully fitted and reached their peaks at the seventeenth and thirteenth cases respectively. The entire learning process was divided into learning stage (17 cases) and maturity stage (33 cases) based on the turning point of the learning curve of total robot usage time. The path planning time and guide wire placement time did not show significant changes with the increase in the case number. The total robot usage time, robot registration time and the intraoperative blood loss in the learning stage were significantly higher than those in the maturity stage: (35.35 ± 1.58) min vs. (30.61 ± 0.43) min, (20.83 ± 1.56) min vs. (14.94 ± 0.29) min and 400 (150, 500) ml vs. 200 (110, 300) ml, the guide wire placement time of per screw was significantly lower than that in the maturity stage: 2.00 (1.83, 2.34) min/screw vs. 2.33 (2.13, 2.69) min/screw, and there were statistical differences ( P<0.05 or <0.01). There were no statistical difference in the path planning time, path planning time of per screw, guide wire placement time and the accuracy of screw placement between two stages ( P>0.05). Conclusions:TiRobot-assisted LPSF is a new technology with safety and effectiveness, and it has a relatively short learning curve. To achieve technological maturity, at least 17 surgeries are required with accumulated experience, and the robot registration is the main step of the learning process. After reaching maturity stage, the robot usage time is significantly shortened and intraoperative trauma is significantly reduced while the relatively high screw placement accuracy is ensured.

Irreducible anteromedial radial head dislocation (IARHD) caused by transposed biceps tendon is rare. Delayed diagnosis and surgical failure often occur. A 46-year-old fisherman presented with 10 days history of painful swelling and restricted movement of his right elbow due to strangulation injury by a fishing boat cable. On examination, the images of the right elbow reveals in a "semi-extended and pronated" elastic fixation position. Radiography and 3-dimensional reconstruction CT reveals an isolated anteromedial radial head dislocation with extreme protonation of the radius and the bicipital tuberosity towards the posterior aspect of the elbow joint, and MRI shows biceps tendon wrapping around the radial neck, similar to umbilical cord wrapping seen in newborns. The Henry approach was applied for the first time to reduce the biceps tendon. The patient achieved a good functional recovery at 26 months, which represents the first reported case of IARHD without fracture caused by biceps tendon in an adult. In treatment of IARHD, attention should be paid to the phenomenon of biceps tendon transposition. Careful clinical examination, comprehensive imaging modalities, and appropriate surgical approach are the keys to successful management.

In recent years, the application of artificial intelligence technology in the field of orthodontics has gradually increased, and deep learning, as a hot direction, has also been rapidly applied in the detection, evaluation, diagnosis, prediction and effect evaluation. At present, deep learning research has the advantages of high efficiency and accuracy, but it also has limitations such as weak interpretability and insufficient data volume. This paper reviewed the proposal and development of deep learning, the application in orthodontic diagnosis and treatment, as well as the limitations and countermeasures of the popularization, and prospect of the future research.
Humans ; Artificial Intelligence ; Deep Learning ; Dental Care ; Forecasting ; Orthodontics

Objective:To determine the current situation of food safety risk monitoring ability of centers for disease control and prevention （CDCs） in a province， and provide reference for strengthening the construction of food safety monitoring ability in the future. Methods:A self-administered questionnaire survey was conducted among 14 CDCs who are responsible for food safety risk monitoring in the province in 2018. Results:The ratio of food inspection staff with senior， middle， and primary professional titles was 1∶1∶1. The educational background of food inspection staff at the provincial and municipal levels was mainly bachelor degree or above， while that of county level directly under the administration of the province was mainly college degree. The age composition of the staff at the provincial level tended to be elder； however， there were more staff with less working experience at the county level. The qualification proportion of laboratory area was 50%. The distribution proportion of food inspection equipment in the CDC at all levels was 83.3%， 79.5% and 36.3%， respectively. In total， 14 CDCs completed the monitoring task as required. They were all accredited with laboratory qualifications； however， one CDC did not conduct re-evaluation after the certification of food inspection qualification expired. Each CDC ensured the accuracy and reliability of monitoring data through the supervision in food testing. Conclusion:The food safety risk monitoring ability of the CDCs in the province is continuously improved， which can meet the needs of routine monitoring. However， the composition of professional titles of food inspection staff is not very reasonable. There is still a gap between the equipment configuration and the national requirements. The ability of food inspection needs to be improved， in which quality management and laboratory qualification should be further strengthened.