AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia

• URL: http://arxiv.org/abs/2004.12852v1
• Date: Mon, 20 Apr 2020 12:24:08 GMT
• Title: AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia
• Authors: Guillaume Chassagnon, Maria Vakalopoulou, Enzo Battistella, Stergios Christodoulidis, Trieu-Nghi Hoang-Thi, Severine Dangeard, Eric Deutsch, Fabrice Andre, Enora Guillo, Nara Halm, Stefany El Hajj, Florian Bompard, Sophie Neveu, Chahinez Hani, Ines Saab, Alienor Campredon, Hasmik Koulakian, Souhail Bennani, Gael Freche, Aurelien Lombard, Laure Fournier, Hippolyte Monnier, Teodor Grand, Jules Gregory, Antoine Khalil, Elyas Mahdjoub, Pierre-Yves Brillet, Stephane Tran Ba, Valerie Bousson, Marie-Pierre Revel, Nikos Paragios
• Abstract summary: Chest computed tomography (CT) is widely used for the management of Coronavirus disease 2019 (COVID-19) pneumonia. CT has a prognostic role by allowing visually evaluating the extent of COVID-19 lung abnormalities.
• Score: 3.672093204122992
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chest computed tomography (CT) is widely used for the management of Coronavirus disease 2019 (COVID-19) pneumonia because of its availability and rapidity. The standard of reference for confirming COVID-19 relies on microbiological tests but these tests might not be available in an emergency setting and their results are not immediately available, contrary to CT. In addition to its role for early diagnosis, CT has a prognostic role by allowing visually evaluating the extent of COVID-19 lung abnormalities. The objective of this study is to address prediction of short-term outcomes, especially need for mechanical ventilation. In this multi-centric study, we propose an end-to-end artificial intelligence solution for automatic quantification and prognosis assessment by combining automatic CT delineation of lung disease meeting performance of experts and data-driven identification of biomarkers for its prognosis. AI-driven combination of variables with CT-based biomarkers offers perspectives for optimal patient management given the shortage of intensive care beds and ventilators.

Related papers

• A Joint Representation Using Continuous and Discrete Features for Cardiovascular Diseases Risk Prediction on Chest CT Scans [12.652540031719571]
  We propose a novel joint representation that integrates discrete quantitative biomarkers and continuous deep features extracted from chest CT scans. Our method substantially improves CVD risk predictive performance and offers individual contribution analysis of each biomarker.
  arXiv  Detail & Related papers  (2024-10-24T10:06:45Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning [133.87426554801252]
  We adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage.
  arXiv  Detail & Related papers  (2021-05-25T12:06:55Z)
• Dual-Attention Residual Network for Automatic Diagnosis of COVID-19 [6.941255691176647]
  We propose a novel residual network to automatically identify COVID-19 from other common pneumonia and normal people using CT images. Our method can differentiate COVID-19 from the other two classes with 94.7% accuracy, 93.73% sensitivity, 98.28% specificity, 95.26% F1-score, and an area under the receiver operating characteristic curve (AUC) of 0.99.
  arXiv  Detail & Related papers  (2021-05-14T11:59:47Z)
• Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade oftwo U-nets: training and assessment on multipledatasets using different annotation criteria [83.83783947027392]
  This study aims at exploiting Artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The accuracy in predicting the CT-Severity Score (CT-SS) of the LungQuant system has been also evaluated.
  arXiv  Detail & Related papers  (2021-05-06T10:21:28Z)
• An Automated Approach for Timely Diagnosis and Prognosis of Coronavirus Disease [1.52292571922932]
  Since the outbreak of Coronavirus Disease 2019 (COVID-19), most of the impacted patients have been diagnosed with high fever, dry cough, and soar throat leading to severe pneumonia. To date, the diagnosis of COVID-19 from lung imaging is proved to be a major evidence for early diagnosis of the disease. The proposed approach focuses on the automated diagnosis and prognosis of the disease from a non-contrast chest computed tomography (CT)scan.
  arXiv  Detail & Related papers  (2021-04-29T05:26:30Z)
• Fibrosis-Net: A Tailored Deep Convolutional Neural Network Design for Prediction of Pulmonary Fibrosis Progression from Chest CT Images [59.622239796473885]
  Pulmonary fibrosis is a chronic lung disease that causes irreparable lung tissue scarring and damage, resulting in progressive loss in lung capacity and no known cure. We introduce Fibrosis-Net, a deep convolutional neural network design tailored for the prediction of pulmonary fibrosis progression from chest CT images.
  arXiv  Detail & Related papers  (2021-03-06T02:16:41Z)
• Decision and Feature Level Fusion of Deep Features Extracted from Public COVID-19 Data-sets [6.445605125467574]
  The usage of chest radiography has been increasing in the early diagnosis of COVID-19. A computer-aided diagnosis system for X-ray images based on convolutional neural networks (CNNs) has been proposed. The experimental results show that the proposed approach has attained high COVID-19 detection performance.
  arXiv  Detail & Related papers  (2020-11-17T09:36:21Z)
• Pinball-OCSVM for early-stage COVID-19 diagnosis with limited posteroanterior chest X-ray images [3.4935179780034247]
  This research proposes a novel pinball loss function based one-class support vector machine (PB-OCSVM) that can work in presence of limited COVID-19 positive CXR samples. The performance of the proposed model is compared with conventional OCSVM and existing deep learning models, and the experimental results prove that the proposed model outperformed over state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-10-16T02:34:15Z)
• COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest CT Images [75.74756992992147]
  We introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images. We also introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation.
  arXiv  Detail & Related papers  (2020-09-08T15:49:55Z)
• Integrative Analysis for COVID-19 Patient Outcome Prediction [53.11258640541513]
  We combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit admission. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.
  arXiv  Detail & Related papers  (2020-07-20T19:08:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.