Machine learning to identify, track, and monitor disease
Principal Investigator
Name
John MacLean
Degrees
BMBS, BSc
Institution
doclink.io
Position Title
Chief Medical Officer
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-338
Initial CDAS Request Approval
Aug 16, 2017
Title
Machine learning to identify, track, and monitor disease
Summary
We are attempting to develop a machine learning-based platform to aid clinicians in the diagnosis of lung lesions, using radiological and histological images from the NLST as a means to train our system. A minority of the data will be used to validate our system to help ensure it can meet the clinical standards required.
Aims
1. Develop a system that can aid in an initial radiological diagnosis of patients that have the clinical signs of symptoms of lung pathology.
2. Develop a system that can subsequently aid the clinicians in the followup of patients with confirmed lung pathology.
3. Develop a system that can correlate the radiological images with the histopathological images of the lung resection specimens.
4. Use a portion of the data as a means of validating our system
Collaborators
No one outside of doclink.io