Data-driven Imaging Biomarker(DIB) study for chest x-ray
Principal Investigator
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-373
Initial CDAS Request Approval
Jun 19, 2018
Title
Data-driven Imaging Biomarker(DIB) study for chest x-ray
Summary
Invented in 1900, chest radiography has been, and still is, the single most commonly performed diagnostic imaging test. It is mainly used to evaluate the lungs and heart, help diagnose and monitor treatment response for a variety of diseases such as pneumonia, tuberculosis and lung cancer. Albeit an old technology, it remains to take an integral part of clinical decisions made by physicians worldwide.
Despite a large amount of collective experience in interpreting chest x-rays, a significant number of cases are still misinterpreted and misdiagnosed on chest x-rays. Even though chest radiography is a basic part of medicine, it remains to be a challenging task to accurately interpret chest x-rays. There is room for improvement in terms of accuracy and consistency both due to the inherent limitations of the modality itself as well as limitations of the human visual system.
Our research aims to use technology to understand lesions on chest x-rays in depth and devise better models of lesion morphology in order to improve the overall diagnostic performance of chest radiograph interpretation.
Despite a large amount of collective experience in interpreting chest x-rays, a significant number of cases are still misinterpreted and misdiagnosed on chest x-rays. Even though chest radiography is a basic part of medicine, it remains to be a challenging task to accurately interpret chest x-rays. There is room for improvement in terms of accuracy and consistency both due to the inherent limitations of the modality itself as well as limitations of the human visual system.
Our research aims to use technology to understand lesions on chest x-rays in depth and devise better models of lesion morphology in order to improve the overall diagnostic performance of chest radiograph interpretation.
Aims
- Develop the state-of-the-art chest x-ray DIB model using deep learning.
- Validation of pre-trained DIB model for PLCO dataset.
- Seamlessly integrate the DIB model into clinical workflow.
Collaborators
Lunit