Automated triage of COVID-19 from various lung abnormalities using chest CT features

• URL: http://arxiv.org/abs/2010.12967v1
• Date: Sat, 24 Oct 2020 19:44:48 GMT
• Title: Automated triage of COVID-19 from various lung abnormalities using chest CT features
• Authors: Dor Amran, Maayan Frid-Adar, Nimrod Sagie, Jannette Nassar, Asher Kabakovitch, Hayit Greenspan
• Abstract summary: We propose a fully automated AI based system that takes as input chest CT scans and triages COVID-19 cases. We produce multiple features, including lung and infections statistics, texture, shape and location, to train a machine learning based classifier. We evaluated our system on a dataset of 2191 CT cases and demonstrated a robust solution with 90.8% sensitivity at 85.4% specificity with 94.0% ROC-AUC.
• Score: 2.4956060473718407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The outbreak of COVID-19 has lead to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 suspected patients is utilized, either as a support or replacement to reverse transcription-polymerase chain reaction (RT-PCR) test. In this paper, we propose a fully automated AI based system that takes as input chest CT scans and triages COVID-19 cases. More specifically, we produce multiple descriptive features, including lung and infections statistics, texture, shape and location, to train a machine learning based classifier that distinguishes between COVID-19 and other lung abnormalities (including community acquired pneumonia). We evaluated our system on a dataset of 2191 CT cases and demonstrated a robust solution with 90.8% sensitivity at 85.4% specificity with 94.0% ROC-AUC. In addition, we present an elaborated feature analysis and ablation study to explore the importance of each feature.

Related papers

• A Novel Implementation of Machine Learning for the Efficient, Explainable Diagnosis of COVID-19 from Chest CT [0.0]
  The aim of this study was to take a novel approach in the machine learning-based detection of COVID-19 from chest CT scans. The proposed model attained an overall accuracy of 0.927 and a sensitivity of 0.958.
  arXiv  Detail & Related papers  (2022-06-15T18:35:22Z)
• An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans [8.694504007704994]
  COVID-19 infection caused by SARS-CoV-2 pathogen is a catastrophic pandemic outbreak all over the world. We propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans.
  arXiv  Detail & Related papers  (2021-01-28T11:47:35Z)
• 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)
• Automated Detection of COVID-19 from CT Scans Using Convolutional Neural Networks [0.0]
  COVID-19 is an infectious disease that causes respiratory problems similar to those caused by SARS-CoV 2003. We build a 2D segmentation model using the U-Net architecture, which gives the output by marking out the region of infection.
  arXiv  Detail & Related papers  (2020-06-23T06:50:41Z)
• Machine Learning Automatically Detects COVID-19 using Chest CTs in a Large Multicenter Cohort [43.99203831722203]
  Our retrospective study obtained 2096 chest CTs from 16 institutions. A metric-based approach for classification of COVID-19 used interpretable features. A deep learning-based classifier differentiated COVID-19 via 3D features extracted from CT attenuation and probability distribution of airspace opacities.
  arXiv  Detail & Related papers  (2020-06-09T00:40:35Z)
• Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT [49.09507792800059]
  We propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. We evaluate our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP)
  arXiv  Detail & Related papers  (2020-05-07T06:00:02Z)
• Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia [46.521323145636906]
  We develop a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT) In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses. Our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944, accuracy of 87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%.
  arXiv  Detail & Related papers  (2020-05-06T09:56:51Z)
• 3D Tomographic Pattern Synthesis for Enhancing the Quantification of COVID-19 [13.424414148963566]
  Coronavirus Disease (COVID-19) has affected 1.8 million people and resulted in more than 110,000 deaths as of April 12, 2020. Tomographic patterns seen on chest Computed Tomography (CT), such as ground-glass opacities, consolidations, and crazy paving pattern, are correlated with the disease severity and progression. We propose to use synthetic datasets to augment an existing COVID-19 database to tackle these challenges.
  arXiv  Detail & Related papers  (2020-05-05T01:31:40Z)
• JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation [95.57532063232198]
  coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries. To control the infection, identifying and separating the infected people is the most crucial step. This paper develops a novel Joint Classification and (JCS) system to perform real-time and explainable COVID-19 chest CT diagnosis.
  arXiv  Detail & Related papers  (2020-04-15T12:30:40Z)
• Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images [54.919022945740515]
  The aim of this study is to realize automatic severity assessment (non-severe or severe) of COVID-19 based on chest CT images. A random forest (RF) model is trained to assess the severity (non-severe or severe) based on quantitative features. Several quantitative features, which have the potential to reflect the severity of COVID-19, were revealed.
  arXiv  Detail & Related papers  (2020-03-26T15:49:32Z)

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.