Validation of Deep Learning Algorithm on Nodule Detection using Chest Radiographs in the National Lung Screening Trial

Principal Investigator

Name
Hao Wu

Degrees
Ph.D.

Institution
Infervision Medical Technology

Position Title
Research Scientist

Email
hao.wu@infervision.ai

About this CDAS Project

Study
NLST (Learn more about this study)

Project ID
NLST-744

Initial CDAS Request Approval
Dec 29, 2020

Title
Validation of Deep Learning Algorithm on Nodule Detection using Chest Radiographs in the National Lung Screening Trial

Summary
NLST participants underwent both low-dose CT and chest radiography for lung cancer screening. Although the chest radiography group is inferior to the low-dose CT group, chest radiographs provide useful features that may be useful to identify and classify suspicious pulmonary nodules, especially augmented by additional support from advanced computed-aided detection software. Deep learning algorithms have demonstrated superior performance on feature extraction and quantification from medical images. The goal of this proposal is to use deep learning algorithms to predict lung nodules from chest radiographs and compare the performance of this approach with the results of the low-dose CT group.

Aims

(1) Develop deep learning models for lung nodule detection from chest radiographs;
(2) Validate the performance of deep learning algorithms on nodule detection from chest radiographs and compare the results with that of the low-dose CT group;
(3) Investigate the possibility of deep learning models to identify nodules early and reduce mortality and morbidity in the chest radiology group;

Collaborators

Matt Deng, Infervision Medical Technology
Chen Xia, Infervision Medical Technology
Kai Liu, Infervision Medical Technology