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
maclean.john@gmail.com

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