Comparison of autoregressive integrated moving average model and deep learning model in prediction and analysis of liposuction operation data
10.3760/cma.j.cn114453-20201201-00603
- VernacularTitle:差分整合移动平均自回归模型与深度学习模型在吸脂操作数据预测分析中的应用比较
- Author:
Zhibin SUN
1
;
Gang ZHOU
;
Sijie CHEN
;
Yuneng WANG
;
Yu WANG
;
Facheng LI
;
Haiyue JIANG
Author Information
1. 北京航空航天大学生物与医学工程学院 100083
- Keywords:
Lipectomy;
Forecasting;
Artificial intelligence;
Machine learning;
Liposuction;
Deep learning
- From:
Chinese Journal of Plastic Surgery
2021;37(10):1102-1108
- CountryChina
- Language:Chinese
-
Abstract:
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.
