Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT

Abstract

Automated quantification of abnormalities associated with COVID-19 from chest CT could help clinicians evaluate the disease and assess its severity and progression. This study proposes measures of disease severity and a deep learning and deep reinforcement-based method to compute them.

Modality alignment contrastive learning for severity assessment of COVID-19 from lung ultrasound and clinical information

“…A human-in-the-loop strategy was used to accelerate the manual delineation. Chaganti et al. (2020) proposed a deep learning method that can detect and quantify the abnormal tomographic patterns present in COVID-19, which include ground glass opacities (GGO) and consolidations.…”

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence

“… FC-Densenet103, Unet, DenseNet, and DenseNet121-FPN. References References [ 137 ] [ 138 ] [ 139 ] [ 140 ] [ 141 ] [ 142 ] [ 143 ] [ 129 ] [ 144 ] [ 145 ] [ 146 ] [ 147 ] [ 148 ] [ 149 ] [ 150 ] [ 151 ] [ 152 ] [ 153 ] [ 154 ] [ 155 ] [ 156 ] [ 157 ] [ 158 ] [ 159 ] [ 160 ] [ 161 ] [ 162 ] [ 163 ] [ 164 ] [ 165 ] Classification Characteristics Characteristics Gray scale feature extraction and ML classifier, and model-based techniques. Resnet-50, CNN, SVM, ResNet101, VGG16, and VGG19.…”

“… Resnet-50, CNN, SVM, ResNet101, VGG16, and VGG19. References References [ 166 ] [ 167 ] [ 168 ] [ 169 ] [ 170 ] [ 148 ] [ 150 ] [ 151 ] [ 171 ] [ 152 ] [ 153 ] [ 157 ] [ 158 ] [ 159 ] [ 172 ] [ 173 ] [ 174 ] [ 175 ] Joint Segmentation and Classification Characteristics Characteristics They use the same characteristics as adapted by segmentation and classification domain for AI-based Non-ARDS. They use the same characteristics as adapted by segmentation and classification domain for AI-based ARDS.…”

Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs

“…They transformed their study into an online platform to provide fast COVID-19 diagnostic tools that are accessible worldwide [ 20 ]. Another group of researchers created a DL and “deep reinforcement learning” model that can automatically quantify COVID-19-related lung abnormalities such as ground-glass opacities and consolidations [ 21 ]. Their proposed architecture produces two metrics that can accurately quantify the spread of COVID-19.…”