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Principal Investigator
Shadab Khan
Group 42
Position Title
Head of Applied Science
About this CDAS Project
NLST (Learn more about this study)
Project ID
Initial CDAS Request Approval
Apr 6, 2020
COVID-19 AI Screening Model Pretraining Using NLST Data - Is CT a clinically meaningful COVID screening tool?
Several recent studies have presented imaging features of COVID 19 subjects in CT. Guam et al (NEJM) state that 18% of symptomatic non-severe COVID subjects were asymptomatic on radiology scans. Others have similarly raised concerns about CT's ability to be used a useful screening tool for COVID 19 (ACR/RSNA expert consensus report in Radiology: Cardiothoracic Imaging). Despite this, several studies have reported features that were common and nearly unique to COVID, e.g. peripheral presentation of ground glass opacity and consolidation near the periphery.

Working further against CT is the fact that it takes much longer to clean a CT scanner post COVID-scan than it does to clean an X-ray unit. While no COVID-specific distinguishing features have been reported X-rays, it motivates us to assess whether any radiology tools, CT or X-ray, offer anything meaningful in screening or managing care of a COVID-19 subject.

To this end, we intend to assess the utility of CT imaging. We will use the NLST data to train an AI model that will be fine tuned using COVID data. The volume of NLST images will allow us to try sophisticated modeling approaches in order to address our questions.

1) Train a lung nodule detection and classification model using NLST data.
2) Fine tune the model using COVID-19 scans as well as bacterial and viral pneumonia scans, in addition to scans from other morbidities that result in radiology features similar to pneumonia (e.g. diffuse alveolar hemorrhage).
3) Assess the specificity and sensitivity of the model in correctly diagnosing COVID-19 from a hold out set comprising scans from bacterial and viral pneumonia.


This is an internal project, no external collaborators are expected to participate.