Chest Radiograph and Computed Tomography Phenotyping For Longevity Prediction Using Deep Learning – Training and Validation in Chinese Patients
Principal Investigator
Name
Varut Vardhanabhuti
Degrees
M.D., Ph.D
Institution
The University of Hong Kong
Position Title
Clinical Assistant Professor
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-865
Initial CDAS Request Approval
Jan 5, 2022
Title
Chest Radiograph and Computed Tomography Phenotyping For Longevity Prediction Using Deep Learning – Training and Validation in Chinese Patients
Summary
A deep learning approach as applied to medical imaging (i.e. radiological images) has huge potential for diagnostic and prognostic prediction of diseases. In this study, we will use the NLST datasets for training separate deep learning models using chest radiographs and CT scans to predict biological age, and time to death and validate on a local dataset of Chinese patients in Hong Kong. This proof of concept will be applied to the screening population in the NLST cohorts.
Aims
1. To develop a deep learning model using chest radiograph for the prediction of biological age and time to death.
2. To develop a deep learning model using chest computed tomography for the prediction of biological age and time to death.
3. To explore generalizability in Chinese patients using local validation datasets in Hong Kong.
Collaborators
N/A