Control Strategy and Experiments for Robot Assisted Craniomaxillofacial Surgery System

Cui, Tengfei; Wang, Yonggui; Duan, Xingguang; Ma, Xiaodong

doi:10.1155/2019/4853046

Cited by 1 publication

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, the system recognized teeth restoration materials with 5% of error. In [25], Tengfei Cui et al, demonstrated experiments for analyzing the robot assisted based craniomaxillo facial surgery process. The effective robot assisted system was developed to predict the exact position to inject the needle while doing surgical operations.…”

Section: Related Workmentioning

confidence: 99%

Improving the Efficiency of Dental Implantation Process Using Guided Local Search Models and Continuous Time Neural Networks With Robotic Assistance

Hashem

Youssef

2020

IEEE Access

View full text Add to dashboard Cite

for funding this work through research group no. RG-1441-369" ABSTRACT Nowadays, robotics plays a vital role in medical applications, especially in dentistry, where robots can track oral hygiene and perform dental surgeries. Dental implant replacement is one of the most challenging issues in dental surgery; quality procedures and safety measures need to be considered during this process. Manual dental implant is usually incapable to reach the satisfactory levels of accuracy and safety. In addition, it requires well-trained dentists and consumes a long time. Therefore, robot-assisted surgery systems are of utmost importance for dental implant placement as they can maintain higher level of dental examination precision and safety. More specifically, robotic arms can be manufactured with intelligent models for drilling identified locations in teeth. These intelligent robots have a high degree of autonomy, can automatically adjust during intraoperative procedures, and can execute dental surgical tasks directly on patients without any apparent control by a surgeon. In this paper, we propose a novel approach to develop a robot-assisted intelligent system that improves the efficiency of dental implant process based on Guided Local Search with Continuous Time Neural Network (GLCTNN). Firstly, dental facts are collected from PubMed articles and Maryland school children datasets. Secondly, using the collected facts, an intelligent robotassisted model based on GLCTNN is developed. The second step comprises data preprocessing to remove unsolicited details, extracting useful features from the clean data, and utilizing the extracted features to train the GLCTNN model. The proposed system recognizes the implantation location with high accuracy and maximizes implantation rate. The efficiency of the system is evaluated using experimental analysis at lab scale. The proposed GLCTNN-based approach ensures maximum average accuracy (99.5%) and minimum average deviation error (0.323) compared to W-J48, Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighboring (KNN), Nearest Neighbors with Structural Risk Minimization (NNSRM) and Generalized Regression Neural Network (GRNN) approaches.INDEX TERMS Robotics, robot arm manufacturing, dental implant, manual implant process, guided local search with continuous time neural network (GLCTNN), implant information.

show abstract

Section: Related Workmentioning

confidence: 99%