Ziyi Wang, Chi Li, Anyi Guo, Yan Liu, Yajun Liu, Wenyong Liu
{"title":"预测因素对桡骨体外冲击波治疗延迟愈合疗效的影响","authors":"Ziyi Wang, Chi Li, Anyi Guo, Yan Liu, Yajun Liu, Wenyong Liu","doi":"10.1109/WRCSARA57040.2022.9903993","DOIUrl":null,"url":null,"abstract":"As a widely adopted non-surgical treatment approach in orthopedics, extracorporeal shock wave therapy (ESWT) sill has limitations such as the high operation intensity of physician, the subjective selection of treatment parameters by physicians based on their clinical experience and patient’s medical conditions. In order to automate the planning of treatment parameters in the robot-assisted ESWT, a prerequisite is to clarity the influence of all predictive factors (not only treatment parameters, but also demographic and clinical factors) on treatment effect. However, it is hard to mathematically model the influence of all these predictive factors on treatment effect. Aiming at the radial ESWT (rESWT) of delayed union, this paper analyzes the ranking of importance (degree of explanation) of each predictive factor to the treatment effect of rESWT using the multi-layer perceptron and verifies it using the random forest for consistency. The results indicate that, of all the predictive factors of patient, only the course of disease and BMI have a strong correlation with the successful rESWT treatment of delayed union, while other factors have no significant linear correlation. This conclusion provides a reference for parameter selection in the automatic planning of treatment parameters in robot-assisted orthopedic rESWT.","PeriodicalId":106730,"journal":{"name":"2022 WRC Symposium on Advanced Robotics and Automation (WRC SARA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Influences of Predictive Factors on Treatment Effect of Delayed Union with Radial Extracorporeal Shock Wave Therapy\",\"authors\":\"Ziyi Wang, Chi Li, Anyi Guo, Yan Liu, Yajun Liu, Wenyong Liu\",\"doi\":\"10.1109/WRCSARA57040.2022.9903993\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As a widely adopted non-surgical treatment approach in orthopedics, extracorporeal shock wave therapy (ESWT) sill has limitations such as the high operation intensity of physician, the subjective selection of treatment parameters by physicians based on their clinical experience and patient’s medical conditions. In order to automate the planning of treatment parameters in the robot-assisted ESWT, a prerequisite is to clarity the influence of all predictive factors (not only treatment parameters, but also demographic and clinical factors) on treatment effect. However, it is hard to mathematically model the influence of all these predictive factors on treatment effect. Aiming at the radial ESWT (rESWT) of delayed union, this paper analyzes the ranking of importance (degree of explanation) of each predictive factor to the treatment effect of rESWT using the multi-layer perceptron and verifies it using the random forest for consistency. The results indicate that, of all the predictive factors of patient, only the course of disease and BMI have a strong correlation with the successful rESWT treatment of delayed union, while other factors have no significant linear correlation. This conclusion provides a reference for parameter selection in the automatic planning of treatment parameters in robot-assisted orthopedic rESWT.\",\"PeriodicalId\":106730,\"journal\":{\"name\":\"2022 WRC Symposium on Advanced Robotics and Automation (WRC SARA)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 WRC Symposium on Advanced Robotics and Automation (WRC SARA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WRCSARA57040.2022.9903993\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 WRC Symposium on Advanced Robotics and Automation (WRC SARA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WRCSARA57040.2022.9903993","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Influences of Predictive Factors on Treatment Effect of Delayed Union with Radial Extracorporeal Shock Wave Therapy
As a widely adopted non-surgical treatment approach in orthopedics, extracorporeal shock wave therapy (ESWT) sill has limitations such as the high operation intensity of physician, the subjective selection of treatment parameters by physicians based on their clinical experience and patient’s medical conditions. In order to automate the planning of treatment parameters in the robot-assisted ESWT, a prerequisite is to clarity the influence of all predictive factors (not only treatment parameters, but also demographic and clinical factors) on treatment effect. However, it is hard to mathematically model the influence of all these predictive factors on treatment effect. Aiming at the radial ESWT (rESWT) of delayed union, this paper analyzes the ranking of importance (degree of explanation) of each predictive factor to the treatment effect of rESWT using the multi-layer perceptron and verifies it using the random forest for consistency. The results indicate that, of all the predictive factors of patient, only the course of disease and BMI have a strong correlation with the successful rESWT treatment of delayed union, while other factors have no significant linear correlation. This conclusion provides a reference for parameter selection in the automatic planning of treatment parameters in robot-assisted orthopedic rESWT.