[HTML][HTML] Development and clinical implementation of tailored image analysis tools for COVID-19 in the midst of the pandemic: The synergetic effect of an open …
C Anastasopoulos, T Weikert, S Yang… - European journal of …, 2020 - Elsevier
Purpose During the emerging COVID-19 pandemic, radiology departments faced a
substantial increase in chest CT admissions coupled with the novel demand for …
substantial increase in chest CT admissions coupled with the novel demand for …
[HTML][HTML] Deep learning for automatic quantification of lung abnormalities in COVID-19 patients: First experience and correlation with clinical parameters
Rationale and objectives To demonstrate the first experience of a deep learning-based
algorithm for automatic quantification of lung parenchymal abnormalities in chest CT of …
algorithm for automatic quantification of lung parenchymal abnormalities in chest CT of …
[HTML][HTML] Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images
PURPOSE Chest X-ray plays a key role in diagnosis and management of COVID-19 patients
and imaging features associated with clinical elements may assist with the development or …
and imaging features associated with clinical elements may assist with the development or …
An interpretable chest CT deep learning algorithm for quantification of COVID-19 lung disease and prediction of inpatient morbidity and mortality
JH Chamberlin, G Aquino, UJ Schoepf, S Nance… - Academic …, 2022 - Elsevier
Rationale and Objectives The burden of coronavirus disease 2019 (COVID-19) airspace
opacities is time consuming and challenging to quantify on computed tomography. The …
opacities is time consuming and challenging to quantify on computed tomography. The …
Automated quantification of CT patterns associated with COVID-19 from chest CT
S Chaganti, P Grenier, A Balachandran… - Radiology: Artificial …, 2020 - pubs.rsna.org
Purpose To present a method that automatically segments and quantifies abnormal CT
patterns commonly present in COVID-19, namely ground-glass opacities and …
patterns commonly present in COVID-19, namely ground-glass opacities and …
A deep learning-based application for COVID-19 diagnosis on CT: the imaging COVID-19 AI initiative
L Topff, J Sánchez-García, R López-González… - Plos one, 2023 - journals.plos.org
Background Recently, artificial intelligence (AI)-based applications for chest imaging have
emerged as potential tools to assist clinicians in the diagnosis and management of patients …
emerged as potential tools to assist clinicians in the diagnosis and management of patients …
Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients
Recent research on COVID-19 suggests that CT imaging provides useful information to
assess disease progression and assist diagnosis, in addition to help understanding the …
assess disease progression and assist diagnosis, in addition to help understanding the …
Comparison of deep learning, radiomics and subjective assessment of chest CT findings in SARS-CoV-2 pneumonia
C Arru, S Ebrahimian, Z Falaschi, JV Hansen… - Clinical Imaging, 2021 - Elsevier
Purpose Comparison of deep learning algorithm, radiomics and subjective assessment of
chest CT for predicting outcome (death or recovery) and intensive care unit (ICU) admission …
chest CT for predicting outcome (death or recovery) and intensive care unit (ICU) admission …
Quantification of COVID-19 opacities on chest CT–evaluation of a fully automatic AI-approach to noninvasively differentiate critical versus noncritical patients
C Mader, S Bernatz, S Michalik, V Koch, SS Martin… - Academic radiology, 2021 - Elsevier
Objectives To evaluate the potential of a fully automatic artificial intelligence (AI)-driven
computed tomography (CT) software prototype to quantify severity of COVID-19 infection on …
computed tomography (CT) software prototype to quantify severity of COVID-19 infection on …
Longitudinal assessment of COVID-19 using a deep learning–based quantitative CT pipeline: illustration of two cases
Figure 1: Evolution of COVID-19 in a 48-year-old woman across 16 days of treatment. A,
Axial unenhanced chest CT images at four time points (dates annotated in each panel) show …
Axial unenhanced chest CT images at four time points (dates annotated in each panel) show …