CORADS-AI

About

Contact email:

nikolas.lessmann@radboudumc.nl

Version:

74e8c59a-e93d-46e2-a4a8-6f89f107180d

Last updated:

Jan. 8, 2021, 4:20 p.m.

Associated publication:

Lessmann N, Sánchez CI, Beenen L, et al.. Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence. Radiology. 2021;298(1):E18-E28.

Inputs:

Generic Medical Image

Outputs:

Generic Overlay (An overlay of unknown type. Legacy, please use alternative interfaces.)
Results JSON File (A collection of results of unknown type. Legacy, if possible please use alternative interfaces.)

Model Facts

Summary

This algorithm analyzes non-contrast CT scans for COVID-19. The algorithm is described in Radiology. The analysis follows the CO-RADS classification. More information on this classification is available on the Radiology Assistant website.

The algorithm includes the following features:

Lobe segmentation
CO-RADS scoring
GGO and consolidation segmentation
CT Severity Scoring per lobe and in total

This algorithm was developed by the Diagnostic Image Analysis Group, Amsterdam University Medical Center, Fraunhofer MEVIS, and Thirona.

Mechanism

See Radiology paper.

Validation and Performance

See Radiology paper.

Uses and Directions

This algorithm was developed for research purposes only.

Warnings

Left empty by the Algorithm Editors

Common Error Messages

Left empty by the Algorithm Editors

Information on this algorithm has been provided by the Algorithm Editors, following the Model Facts labels guidelines from Sendak, M.P., Gao, M., Brajer, N. et al. Presenting machine learning model information to clinical end users with model facts labels. npj Digit. Med. 3, 41 (2020). 10.1038/s41746-020-0253-3