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Machine learning approach for patient specific finite element modelling in the human body dummy model: A study with Clinical Injuries and EuroNCAP Whiplash Protocol

Implementing Organization

Vellore Institute of Technology
Principal Investigator
Dr. D Davidson Jebaseelan
Vellore Institute Of Technology Chennai, Tamil Nadu
davidson.jd@vit.ac.in
CO-Principal Investigator
Dr. PAVITHRA LATHAKUMARESAN
Vellore Institute Of Technology Chennai, Vandalur - Kelambakkam Road, Chennai,Tamil Nadu,Chennai-600127
CO-Principal Investigator
Dr. K Annamalai
Vellore Institute Of Technology Chennai,Vandalur - Kelambakkam Road, Chennai,Tamil Nadu,Chennai-600127

Project Overview

Injuries and fatalities due to road accidents are more common across the country and has made all the automotive researchers to concentrate on automotive safety. Majority of these are neck and head injuries. Clinicians treating these injuries need tools to plan their treatment protocols, surgical planning and also need data to understand the post-surgery implications. The team has access too finite element human body model, BioRID-II FE dummy. Although Finite element model needs high computational power, they have good responses compared with experimental dummies and post-mortem human subjects (PMHS) which gives hope in wide variety of researches in crash safety. Patient specific Head and Neck spine finite element modelling (PS-HNM) is of recent interest to researchers and integrating PS-HNM using CAD morphing algorithms with dummies would help clinicians to understand the nature of injuries due to automotive crash, the current study is looking at whiplash injury. Efforts of PS-HNM with spinal cord modelling has been reported by this team collaborators for cervical spine. Adding these modelling efforts into a dummy will make a detailed FE model for intrinsic responses like spinal cord stresses, cornerstone trabecular stresses etc for an automotive rear crash signal as an input . PS-HNM need deep learning techniques for automatic detection of spine components contours from MRI images, to classify degeneration and will be a supportive tool to aid in prediction of the clinical conditions due to injury.
Funding Organization
Funding Organization
Anusandhan National Research Foundation (ANRF)
Quick Information
Area of Research
Engineering Sciences
Focus Area
Mechanical & Manufacturing Engineering & Robotics
Start Date
26 Jul 2024
End Date
25 Jul 2026
Status
ongoing
Output
No. of Research Paper
00
Technologies (If Any)
00
No. of PhD Produced
00
Publications
01
No. of Patents
Filed : 00
Grant : 00
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