Objective:To evaluate the emergency iliosacral screw fixation assisted by TiRobot for unstable posterior pelvic ring fracture.Methods:The 26 patients with unstable pelvic fracture were analyzed retrospectively who had undergone emergency iliosacral screw fixation at Department of Orthopedics & Traumatology, Beijing Jishuitan Hospital from June 2018 to December 2020. They were divided into 2 groups depending on whether orthopaedic TiRobot was used to assist screw insertion. In the observation group of 14 cases subjected to TiRobot-assisted insertion of iliosacral screws, there were 10 males and 4 females with an age of (45.9 ± 10.1) years; in the control group of 12 cases subjected to conventional manual insertion of iliosacral screws, there were 9 males and 3 females with an age of (49.2 ± 11.3) years. All the surgeries were conducted within 24 hours after injury. The 2 groups were compared in terms of screw insertion time, pin insertion, intraoperative blood loss, fluoroscopy time, postoperative screw position, fracture reduction and Harris hip score at the final follow-up.Results:The 2 groups were comparable because there was no significant difference between them in their preoperative general clinical data or follow-up time ( P>0.05). The screw insertion time [(16.1 ± 3.4) min] and fluoroscopy time [(8.1 ± 3.3) s] in the observation group were significantly shorter than those in the control group [(26.4 ± 5.4) min and (25.2 ± 7.4) s], and the pin insertions [1 (1, 2) times] and intraoperative blood loss [(10.5 ± 6.4) mL] in the former were significantly less than those in the latter [6 (3, 8) times and (24.8 ± 6.7) mL] (all P<0.05). Postoperatively, the sacroiliac screw position was excellent in 18 cases and good in 2 in the observation group while excellent in 14 cases, good in 2 and poor in 2 in the control group; the fracture reduction was excellent in 12 cases, good in one and fair in one in the observation group while excellent in 10 cases, good in one and fair in one in the control group, showing insignificant differences in the above comparisons ( P>0.05). There was no significant difference either in the Harris hip score at the final follow-up between the 2 groups ( P>0.05). Conclusion:Compared with conventional manual insertion of iliosacral screws, emergency iliosacral screw fixation assisted by TiRobot can effectively decrease surgical time and reduce operative invasion due to a higher accuracy rate of screw insertion.

Objective:This study aims to compare the application value of Autoregressive Integrated Moving Average model (ARIMA ) and deep learning model inprediction and analysis of liposuction operation data.Methods:The patients who met inclusion criteria and underwent liposuction surgery in the Plastic Surgery Hospital of Chinese Academy of Medical Sciences from January 2019 to September 2019 were enrolled in this study. For each patient, 250~400 s operation data including kinematics and mechanical data were collected by a senior plastic surgeon, usingthe liposuction operation recording system which consists of optical tracking and force sensing equipment. After pretreatment, the collected data were divided into one liposuction reciprocating cycle as one set of data. ARIMA model and deep learning model were used to analyze the collected datafor establishing prediction models of liposuction operation. Using Matlab2017, 30 couplesofliposuction data setwereextractedby simple random sampling, andthe DTW valueofeachcoupleofdatasetswascalculated as test standard. Then, the DTW values between 30 sets of predicted data and actual data based on the ARIMA model and the deep learning model were calculated respectively and compared with the test standard to verify the prediction result of the two models. Matlab2017 was used for statistical analysis. Independent sample t-test was used to compare the two groups, and P<0. 05 indicates that the difference is statistically significant.Results:18 patients were enrolled. All patients were females at 23-49 years old, with the mean age of 36. 6 years old. Liposuction was performed in the abdomen, thighs, and waist. A total of 16 800 sets of liposuction cycle data were obtained. The mean DTW value of test standard was 0. 048±0. 028. The meanDTW value between the ARIMA model predicted data and the actual data was 0. 660±0. 577, which was statistically significant compared with the test standard ( P< 0. 05) . The meanDTW value between the deep learning model predicted data and the actual data was 0. 052±0. 030, which was no significant difference compared with the test standard ( P> 0. 05 ). Conclusions:Compared with ARIMA model, deep learning model can predict liposuction operation data more accurately, and has better adaptability and real-time performance.

Objective:This study aims to compare the applicability value of autoregressive integrated moving average model(ARIMA) and deep learning model inprediction and analysis of liposuction operation data.Methods:The patients who met inclusion criteria and underwent liposuction surgery in the Plastic Surgery Hospital of Chinese Academy of Medical Sciences from January 2019 to September 2019 were enrolled in this study. For each patient, 250-400 s operation data including kinematics and mechanical data were collected by a senior plastic surgeon, using the liposuction operation recording system which consists of optical tracking and force sensing equipment. After pretreatment, the collected data were divided into one liposuction reciprocating cycle as one set of data. ARIMA model and deep learning model were used to analyze the collected data for establishing prediction models of liposuction operation. Using Matlab 2017, 30 couples of liposuction data set were extracted by simple random sampling, and the dynamic time warping (DTW) value of each couple of data sets was calculated as test standard. Then, the DTW values between 30 sets of predicted data and actual data based on the ARIMA model and the deep learning model were calculated respectively and compared with the test standard to verify the prediction results of the two models. Matlab 2017 was used for statistical analysis. Independent sample t-test was used to compare the two groups, and P<0.05 indicated statistically significant difference. Results:Eighteen patients were enrolled. All patients were females at 23-49 years old, with the mean age of 36.6 years old. Liposuction was performed in the abdomen, thighs, and waist. A total of 16 800 sets of liposuction cycle data were obtained. The mean DTW value of test standard was 0.048±0.028. The mean DTW value between the ARIMA model predicted data and the actual data was 0.660±0.577, which was statistically significant compared with the test standard ( P<0.05). The mean DTW value between the deep learning model predicted data and the actual data was 0.052±0.030, which was not significantly different compared to the test standard ( P>0.05). Conclusions:Compared with ARIMA model, deep learning model can predict liposuction operation data more accurately, and has better adaptability and real-time performance.

Objective:To introduce an innovative simulation training system of liposuction and compare the effect of the traditional training method with this system in the liposuction training for medical students.Methods:Thirty medical postgraduates (18 males and 12 females, aged 22 to 30 years) at Peking Union Medical College without liposuction experiences were selected. All the participants were randomly divided into two groups. In the traditional training group, the trainees were trained on the phantom, while the teachers gave explanations and demonstrations. In the simulation training system group, the trainees were trained by themselves on the simulation training system. Before and after the training, the two groups were required to perform a liposuction simulation test on the simulated training system. The resistance of liposuction cannula, the acceleration of liposuction cannula and the uniformity degree of operation of the two groups were recorded, and the differences in the training effects between the two groups were compared. R 3.5 and Python 3.7 were used for analysis. Application of the t test for measurement data was in accordance with normal distribution, and the results were expressed as Mean±SD deviation. Application of Wilcoxon signed-rank test or Wilcoxon rank sum test for the measurement data did not conform to the normal distribution. The results were expressed as M( P25, P75). P< 0.05 indicated statistical differences. Results:After the training, the area of liposuction in the traditional training group was more moderate than that before the training [skewness: -0.22(-0.38, -0.14) vs. -0.07(-0.24, 0.02)( V=20, P=0.022); kurtosis: 2.32(2.09, 2.58) vs. 1.96(1.90, 2.00)( V=112, P=0.002)]. After training, the number of times of lateral resistance[7.0(3.5, 13.5) vs. 0(0, 0)( V=111.5, P=0.004)] and acceleration [7.0(5.0, 17.5) vs. 3.0(2.0, 12.5)( V=102, P=0.002)] over-threshold were significantly reduced, the angle of liposuction coverage [131.18°(117.71°, 137.88°) vs. 169.89°(162.96°, 180.00°)( V=0, P<0.001)] was significantly improved, and the area of liposuction [skewness: -0.17(-0.33, 0.03) vs. -0.01(-0.13, 0.06)( V=21, P=0.026); kurtosis: 2.35(2.08, 2.50) vs. 1.94(1.83, 2.00)( V=118, P<0.001)] was more evenly distributed. The differences before and after training were analyzed between the simulation training system group and the traditional training group. The simulation training system group was superior to the traditional training group in the number of times of lateral resistance[-7.5±7.4 vs.-1.4±9.0 ( t=111.5, P=0.026)], the number of times of acceleration [-3.0(-6.5, -2.0) vs. -1.0(-4.0, 2.0)( W=156.5, P=0.035)] and the angle of coverage[(-40.24±18.88)° vs. (-11.10±25.54)° ( t=3.553, P<0.001)]. Conclusions:Simulation training system is an effective method in liposuction training to enhance the skills of trainees.

Objective:This study aims to compare the application value of Autoregressive Integrated Moving Average model (ARIMA ) and deep learning model inprediction and analysis of liposuction operation data.Methods:The patients who met inclusion criteria and underwent liposuction surgery in the Plastic Surgery Hospital of Chinese Academy of Medical Sciences from January 2019 to September 2019 were enrolled in this study. For each patient, 250~400 s operation data including kinematics and mechanical data were collected by a senior plastic surgeon, usingthe liposuction operation recording system which consists of optical tracking and force sensing equipment. After pretreatment, the collected data were divided into one liposuction reciprocating cycle as one set of data. ARIMA model and deep learning model were used to analyze the collected datafor establishing prediction models of liposuction operation. Using Matlab2017, 30 couplesofliposuction data setwereextractedby simple random sampling, andthe DTW valueofeachcoupleofdatasetswascalculated as test standard. Then, the DTW values between 30 sets of predicted data and actual data based on the ARIMA model and the deep learning model were calculated respectively and compared with the test standard to verify the prediction result of the two models. Matlab2017 was used for statistical analysis. Independent sample t-test was used to compare the two groups, and P<0. 05 indicates that the difference is statistically significant.Results:18 patients were enrolled. All patients were females at 23-49 years old, with the mean age of 36. 6 years old. Liposuction was performed in the abdomen, thighs, and waist. A total of 16 800 sets of liposuction cycle data were obtained. The mean DTW value of test standard was 0. 048±0. 028. The meanDTW value between the ARIMA model predicted data and the actual data was 0. 660±0. 577, which was statistically significant compared with the test standard ( P< 0. 05) . The meanDTW value between the deep learning model predicted data and the actual data was 0. 052±0. 030, which was no significant difference compared with the test standard ( P> 0. 05 ). Conclusions:Compared with ARIMA model, deep learning model can predict liposuction operation data more accurately, and has better adaptability and real-time performance.

Objective:This study aims to compare the applicability value of autoregressive integrated moving average model(ARIMA) and deep learning model inprediction and analysis of liposuction operation data.Methods:The patients who met inclusion criteria and underwent liposuction surgery in the Plastic Surgery Hospital of Chinese Academy of Medical Sciences from January 2019 to September 2019 were enrolled in this study. For each patient, 250-400 s operation data including kinematics and mechanical data were collected by a senior plastic surgeon, using the liposuction operation recording system which consists of optical tracking and force sensing equipment. After pretreatment, the collected data were divided into one liposuction reciprocating cycle as one set of data. ARIMA model and deep learning model were used to analyze the collected data for establishing prediction models of liposuction operation. Using Matlab 2017, 30 couples of liposuction data set were extracted by simple random sampling, and the dynamic time warping (DTW) value of each couple of data sets was calculated as test standard. Then, the DTW values between 30 sets of predicted data and actual data based on the ARIMA model and the deep learning model were calculated respectively and compared with the test standard to verify the prediction results of the two models. Matlab 2017 was used for statistical analysis. Independent sample t-test was used to compare the two groups, and P<0.05 indicated statistically significant difference. Results:Eighteen patients were enrolled. All patients were females at 23-49 years old, with the mean age of 36.6 years old. Liposuction was performed in the abdomen, thighs, and waist. A total of 16 800 sets of liposuction cycle data were obtained. The mean DTW value of test standard was 0.048±0.028. The mean DTW value between the ARIMA model predicted data and the actual data was 0.660±0.577, which was statistically significant compared with the test standard ( P<0.05). The mean DTW value between the deep learning model predicted data and the actual data was 0.052±0.030, which was not significantly different compared to the test standard ( P>0.05). Conclusions:Compared with ARIMA model, deep learning model can predict liposuction operation data more accurately, and has better adaptability and real-time performance.

Objective:To introduce an innovative simulation training system of liposuction and compare the effect of the traditional training method with this system in the liposuction training for medical students.Methods:Thirty medical postgraduates (18 males and 12 females, aged 22 to 30 years) at Peking Union Medical College without liposuction experiences were selected. All the participants were randomly divided into two groups. In the traditional training group, the trainees were trained on the phantom, while the teachers gave explanations and demonstrations. In the simulation training system group, the trainees were trained by themselves on the simulation training system. Before and after the training, the two groups were required to perform a liposuction simulation test on the simulated training system. The resistance of liposuction cannula, the acceleration of liposuction cannula and the uniformity degree of operation of the two groups were recorded, and the differences in the training effects between the two groups were compared. R 3.5 and Python 3.7 were used for analysis. Application of the t test for measurement data was in accordance with normal distribution, and the results were expressed as Mean±SD deviation. Application of Wilcoxon signed-rank test or Wilcoxon rank sum test for the measurement data did not conform to the normal distribution. The results were expressed as M( P25, P75). P< 0.05 indicated statistical differences. Results:After the training, the area of liposuction in the traditional training group was more moderate than that before the training [skewness: -0.22(-0.38, -0.14) vs. -0.07(-0.24, 0.02)( V=20, P=0.022); kurtosis: 2.32(2.09, 2.58) vs. 1.96(1.90, 2.00)( V=112, P=0.002)]. After training, the number of times of lateral resistance[7.0(3.5, 13.5) vs. 0(0, 0)( V=111.5, P=0.004)] and acceleration [7.0(5.0, 17.5) vs. 3.0(2.0, 12.5)( V=102, P=0.002)] over-threshold were significantly reduced, the angle of liposuction coverage [131.18°(117.71°, 137.88°) vs. 169.89°(162.96°, 180.00°)( V=0, P<0.001)] was significantly improved, and the area of liposuction [skewness: -0.17(-0.33, 0.03) vs. -0.01(-0.13, 0.06)( V=21, P=0.026); kurtosis: 2.35(2.08, 2.50) vs. 1.94(1.83, 2.00)( V=118, P<0.001)] was more evenly distributed. The differences before and after training were analyzed between the simulation training system group and the traditional training group. The simulation training system group was superior to the traditional training group in the number of times of lateral resistance[-7.5±7.4 vs.-1.4±9.0 ( t=111.5, P=0.026)], the number of times of acceleration [-3.0(-6.5, -2.0) vs. -1.0(-4.0, 2.0)( W=156.5, P=0.035)] and the angle of coverage[(-40.24±18.88)° vs. (-11.10±25.54)° ( t=3.553, P<0.001)]. Conclusions:Simulation training system is an effective method in liposuction training to enhance the skills of trainees.

Objective:This study aims to discuss the detection effect of a novel liposuction cannula movement recording system based on optical tracking technique and force sensor during liposuction and analyze the relationship between recorded data and liposuction efficiency.Methods:The patients who met inclusion criteria underwent liposuction from January 2019 to September 2019 were enrolled in this study and operated on by two surgeons with extensive liposuction experience. During a given procedure, one surgeon performed liposuction on one side of the targeted liposuction area, another surgeon performed liposuction on the contralateral side. The trajectory and force of liposuction cannula were recorded for approximately 300 seconds, and the volume of upper layer fat in liposuction aspirate was measured during experiment. The average amplitude, average frequency, and average forward resistance of the liposuction needle movement of the two surgeons were compared, and the liposuction efficiency was calculated and compared. The data were analyzed by paired t-test and signed rank sum test. Results:Eighteen patients were enrolled. All patients were females at 23-49 years old, with the mean age of 37 years old. Liposuction was performed in the abdomen, thighs, and waist. The movement amplitude and forward resistance of the liposuction cannula of surgeon A were (11.43±1.23) cm and (9.35±2.24) N, which were higher than those of surgeon B (10.00±2.33) cm and (8.20±3.05) N, and the differences were significant ( t=2.780, P=0.013; t=2.328, P=0.033). The frequency of liposuction cannula movement of surgeon A was (2.14±0.19) Hz, and that of surgeon B was (2.19±0.55) Hz. There was no significant difference between the two surgeons ( t=-0.366, P=0.719). The liposuction efficiency of operator A was (19.20±9.36) ml/min, and the efficiency of operator B was (15.27±8.05) ml/min. A was 3.93 ml/min higher than B. The difference was statistically significant ( t=3.736, P=0.002). Conclusions:The liposuction cannula movement recording system based on optical tracking and force sensing technology established in this study can record and analyze the operation trajectory and resistance of the liposuction cannula. When the movement amplitude and forward resistance of the cannula are larger, the liposuction efficiency is higher.

Objective:This study aims to discuss the detection effect of a novel liposuction cannula movement recording system based on optical tracking technique and force sensor during liposuction and analyze the relationship between recorded data and liposuction efficiency.Methods:The patients who met inclusion criteria underwent liposuction from January 2019 to September 2019 were enrolled in this study and operated on by two surgeons with extensive liposuction experience. During a given procedure, one surgeon performed liposuction on one side of the targeted liposuction area, another surgeon performed liposuction on the contralateral side. The trajectory and force of liposuction cannula were recorded for approximately 300 seconds, and the volume of upper layer fat in liposuction aspirate was measured during experiment. The average amplitude, average frequency, and average forward resistance of the liposuction needle movement of the two surgeons were compared, and the liposuction efficiency was calculated and compared. The data were analyzed by paired t-test and signed rank sum test. Results:Eighteen patients were enrolled. All patients were females at 23-49 years old, with the mean age of 37 years old. Liposuction was performed in the abdomen, thighs, and waist. The movement amplitude and forward resistance of the liposuction cannula of surgeon A were (11.43±1.23) cm and (9.35±2.24) N, which were higher than those of surgeon B (10.00±2.33) cm and (8.20±3.05) N, and the differences were significant ( t=2.780, P=0.013; t=2.328, P=0.033). The frequency of liposuction cannula movement of surgeon A was (2.14±0.19) Hz, and that of surgeon B was (2.19±0.55) Hz. There was no significant difference between the two surgeons ( t=-0.366, P=0.719). The liposuction efficiency of operator A was (19.20±9.36) ml/min, and the efficiency of operator B was (15.27±8.05) ml/min. A was 3.93 ml/min higher than B. The difference was statistically significant ( t=3.736, P=0.002). Conclusions:The liposuction cannula movement recording system based on optical tracking and force sensing technology established in this study can record and analyze the operation trajectory and resistance of the liposuction cannula. When the movement amplitude and forward resistance of the cannula are larger, the liposuction efficiency is higher.

Objective: Report the experience of autologous fat grafting forfacial contouring and rejuvenation patients. Methods: Retrospective analysis the treatment of 450 facial contouring and rejuvenation patients with global optimization autologous fat grafting in Plastic Surgery Hospital of Chinese Academy of Medical Sciences from January 2011 to December 2017. Using low negative pressure liposuction technical and blunt cannula (diameter 2.5 mm and 2.0 mm), different fat particle was harvested and concentrated by a cotton pad. The one-hole blunt cannula was used for fat grafting with multi tunnel and multi-point and two layer(deep and superficial)injection ways, regularly following up and taking picture for imaging evaluation. Results: All patients were successfully performed with fat grafting. Grafted fat volume was 15-65 ml. There were 132 cases of local skin bruising after operation, which gradually subsided about 2 weeks, no severe complications occurred. 450 patients were followed up for 6-12 months. Facial contour and skin texture were improved without subcutaneous nodules and irregularity. The outcome of all patients were satisfactory. Of a total of 450 patients, 319 (70.89%) were evaluated as very good result, 94 (20.89%) as good result, and 37(8.22%) asregular result. Conclusions Optimization autologous fat grafting can improve the facial contouring and rejuvenation with little complications and satisfactory results.