Predicting mortality risk using cardiac LDCT regions
Principal Investigator
Name
chen yingchi
Degrees
Bachelor program in digital health
Institution
National Yang Ming Chiao Tung University
Position Title
graduate student
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-1255
Initial CDAS Request Approval
May 16, 2024
Title
Predicting mortality risk using cardiac LDCT regions
Summary
This work will utlize the NLST data set to predict the death risk of CT in the heart area. The heart area will first be segmented by CNN heart detector, and then deep learning training will be performed on this area. It is expected to use CNN to extract the features. In addition to predicting the risk of death, it will also predict the level of calcification. Different grading systems such as ICD-9 will be used for grading, and the accuracy is expected to be as high as 80% or more.
Aims
This project includes different ways of predicting calcification grade and predicting the risk of death
Collaborators
CHEN YING-CHI