Automatic detection of abnormalities on frontal Chest X-Ray
Principal Investigator
Name
Eldad Elnekave
Degrees
MD
Institution
Zebra Medical Vision
Position Title
Chief Medical Officer
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-232
Initial CDAS Request Approval
Sep 30, 2016
Title
Automatic detection of abnormalities on frontal Chest X-Ray
Summary
This project aims to generate a completely automatic algorithm which identifies abnormal opacities and lucencies on frontal chest radiographs. The opacities may represent a spectrum of clinical diagnoses including but not limited to pneumonia, granuloma, mass, atalectasis, pneumothorax, pneumomediastium and pulmonary edema. We aim to have at least 10,000 samples of each relevant pathology and approximately 40,000 normal CXR examples. The images will be used in an iterative machine learning process based upon convolutional neural network technique.
Aims
To automatically detect acute and chronic pathology on frontal chest radiographs with high sensitivity and specificity.
Collaborators
The project is at entirely internal to Zebra-medical vision.