Open AccessA Novel Machine Learning-derived Radiomic Signature of the Whole Lung Differentiates Stable From Progressive COVID-19 Infection
Author(s) -
Liping Fu,
Yongchou Li,
Cheng Ai-ping,
Peipei Pang,
Zhenyu Shu
Publication year - 2020
Publication title -
journal of thoracic imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 57
eISSN - 1536-0237
pISSN - 0883-5993
DOI - 10.1097/rti.0000000000000544
Subject(s) - medicine , covid-19 , signature (topology) , betacoronavirus , coronavirus infections , lung , radiomics , lung infection , pathology , virology , artificial intelligence , radiology , infectious disease (medical specialty) , computer science , disease , outbreak , geometry , mathematics
This study aimed to use the radiomics signatures of a machine learning-based tool to evaluate the prognosis of patients with coronavirus disease 2019 (COVID-19) infection.