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Exploration of deep learning-based model's lung cancer screening capabilities.

Principal Investigator
Name
Seungwook Yang
Degrees
Ph.D
Institution
Samsung Electronics
Position Title
Research Scientist
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-579
Initial CDAS Request Approval
Oct 3, 2019
Title
Exploration of deep learning-based model's lung cancer screening capabilities.
Summary
Although chest radiography has proven inferiority in terms of screening accuracy compared to computed tomography, augmenting human readers with deep learning-based computer-aided detection (CAD) model for pulmonary nodules may provide added benefits in screening. This project will first utilize radiographs from the NLST study as a standardized external validation set and compare the detection accuracies between a deep learning-based model and human readers, then the investigators will develop a new lung cancer prediction model involving the detection results.
Aims

- Exploration / validation of deep learning-based nodule detection system and comparison with human readers.
- Development of a novel lung cancer prediction model incorporating deep learning-based nodule detection system's results.

Collaborators

Clinical Research Group, Health & Medical Equipment Business, Samsung Electronics