Skip to Main Content
An official website of the United States government

Government Funding Lapse

Because of a lapse in government funding, the information on this website may not be up to date, transactions submitted via the website may not be processed, and the agency may not be able to respond to inquiries until appropriations are enacted. The NIH Clinical Center (the research hospital of NIH) is open. For more details about its operating status, please visit cc.nih.gov. Updates regarding government operating status and resumption of normal operations can be found at OPM.gov.

Integrating AI into workflow

Principal Investigator

Name
akio iwase

Degrees
PhD

Institution
NucleusHealth

Position Title
VP, Eng and Data Science

Email
akio.iwase@gmail.com

About this CDAS Project

Study
PLCO (Learn more about this study)

Project ID
PLCOI-542

Initial CDAS Request Approval
Nov 4, 2019

Title
Integrating AI into workflow

Summary
Recent advance in the machine learning and specifically deep learning has been remarkably successful in various image classification problems. We propose to investigate the effectiveness of the deep learning technology for chest x-ray classification. Specifically, deep learning network will be designed and trained using the data and will be used to compare with existing data for its effectiveness. It is further proposed to quantify the effectiveness of various nodule characteristics. Correlation with database will be investigated.
Nvidia provided chest x-ray classification algorithm that PLCO data was used to train and validate.

Aims

The specific aim is to run and verify that performance of the chest x-ray classification algorithms by running with the original data of PLCO.

Collaborators

N/A