Optimizing radiological workflow with automated diagnostics
Principal Investigator
Name
Mark-Jan Harte
Degrees
MSc
Institution
Aidence
Position Title
Researcher
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-181
Initial CDAS Request Approval
Nov 25, 2015
Title
Optimizing radiological workflow with automated diagnostics
Summary
Radiologists currently must manually read every image; by introducing software tools to support them the quality of readings and thus the diagnostic reports is expected to increase. In order to create such software a large body of annotated images is required to train the algorithm the distinction between normal and pathological cases. Therefore the lung CT image set from the NLST is an important element in creating this software as well as for validating its clinical accuracy.
Aims
1) To create a machine learning algorithm that is able to detect lung nodules and determine their malignancy
2) To achieve a specificity and sensitivity that are better than existing CAD solutions
Collaborators
N/A