Data-driven Imaging Biomarker (DIB) study in NLST datasets
Principal Investigator
Name
Ki Hwan Kim
Degrees
M.D., Ph.D.
Institution
Lunit
Position Title
Chief Medical Officer
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-474
Initial CDAS Request Approval
Feb 1, 2019
Title
Data-driven Imaging Biomarker (DIB) study in NLST datasets
Summary
Despite a large amount of collective experience in interpreting medical images, a significant number of cases is still misinterpreted and misdiagnosed. We believe that 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 images in depth and devise better models of lesion morphology in order to improve the overall diagnostic performance.
Our research aims to use technology to understand lesions on chest images in depth and devise better models of lesion morphology in order to improve the overall diagnostic performance.
Aims
- Development of DIB model for NLST dataset.
- Seamlessly integrate the DIB model into clinical workflow.
Collaborators
Sunggyun Park, Lunit
Related Publications
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AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset.
Yoo H, Lee SH, Arru CD, Doda Khera R, Singh R, Siebert S, Kim D, Lee Y, Park JH, Eom HJ, Digumarthy SR, Kalra MK
Eur Radiol. 2021 Jun 4 PUBMED -
Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs.
Yoo H, Kim KH, Singh R, Digumarthy SR, Kalra MK
JAMA Netw Open. 2020 Sep 1; Volume 3 (Issue 9): Pages e2017135 PUBMED