Point of Care Image Analysis for COVID-19

  title={Point of Care Image Analysis for COVID-19},
  author={Daniel Yaron and Daphna Keidar and Elisha Goldstein and Yair Shachar and Ayelet Blass and Oz Frank and Nir Schipper and Nogah Shabshin and Ahuva Grubstein and Dror Suhami and Naama R. Bogot and Eyal Sela and Amiel A. Dror and Mordehay Vaturi and Federico Mento and Elena Torri and Riccardo Inchingolo and Andrea Smargiassi and Gino Soldati and Tiziano Perrone and Libertario Demi and Meirav Galun and Shai Bagon and Yishai M. Elyada and Yonina C. Eldar},
  journal={ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
Early detection of COVID-19 is key in containing the pandemic. Disease detection and evaluation based on imaging is fast and cheap and therefore plays an important role in COVID-19 handling. COVID-19 is easier to detect in chest CT, however, it is expensive, non-portable, and difficult to dis-infect, making it unfit as a point-of-care (POC) modality. On the other hand, chest X-ray (CXR) and lung ultrasound (LUS) are widely used, yet, COVID-19 findings in these modalities are not always very… 

Figures and Tables from this paper

COVID-Net US-X: Enhanced Deep Neural Network for Detection of COVID-19 Patient Cases from Convex Ultrasound Imaging Through Extended Linear-Convex Ultrasound Augmentation Learning

Experimental results show that the proposed extended linear-convex ultrasound augmentation learning significantly increases performance, with a gain of 5.1% in test accuracy and 13.6% in AUC.

Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic

The current development of AI for COVID-19 management and the outlook for emerging trends of combining AI-based LUS with robotics, telehealth, and other techniques are summarized.

Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray

The study found that integrating clinical data with the CXR improves diagnostic accuracy, and the proposed system can be used as a tool that can help the doctors in COVID-19 diagnosis.

Hybrid Modeling of Regional COVID-19 Transmission Dynamics in the U.S.

A hybrid modeling framework is proposed which not only accounts for such policies but also utilizes the spatial and temporal information to characterize the pattern of COVID-19 progression and provided satisfactory short-term forecasts of the number of new daily cases at regional levels by utilizing the estimated spatio-temporal covariance structures.

Integrating Domain Knowledge Into Deep Networks for Lung Ultrasound With Applications to COVID-19

A framework for training deep artificial neural networks for interpreting LUS, which allows for a unified treatment of LUS frames captured by either convex or linear probes, is proposed and evaluated on the task of COVID-19 severity assessment.

A Framework for Integrating Domain Knowledge into Deep Networks for Lung Ultrasound, and its Applications to COVID-19

A framework for training deep artificial neural networks for interpreting LUS, which allows for a unified treatment of LUS frames captured by either convex or linear probes, and finetuned simple image classification models to predict per-frame COVID-19 severity score.

The role of convolutional neural networks in scanning probe microscopy: a review

This review focuses on a subset of deep learning algorithms, that is, convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data.

Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis

This work proposes a frame-based model that correctly distinguishes COVID-19 LUS videos from healthy and bacterial pneumonia data with a sensitivity and specificity of 0.90±0.08 and demonstrates the model to recognize low-confidence situations which also improves performance.



COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images

COVID-Net is introduced, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public, and COVIDx, an open access benchmark dataset comprising of 13,975 CXR images across 13,870 patient patient cases.

Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound

A novel deep network, derived from Spatial Transformer Networks, is presented, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artefacts in a weakly-supervised way.

CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images

Diagnosis of Coronavirus Disease 2019 Pneumonia by Using Chest Radiography: Value of Artificial Intelligence

CV19-Net was able to differentiate coronavirus disease 2019–related pneumonia from other types of pneumonia, with performance exceeding that of experienced thoracic radiologists.

Lung Ultrasound for COVID‐19 Patchy Pneumonia

The aim of this study was to focus on the implications of limiting LUS examinations to specific regions of the chest.

Diagnosing COVID-19: The Disease and Tools for Detection

Diagnostic and surveillance technologies for SARS-CoV-2 and their performance characteristics are described and point-of-care diagnostics that are on the horizon are described to encourage academics to advance their technologies beyond conception.

Lung ultrasound predicts clinical course and outcomes in COVID-19 patients

Routine use of LUS may guide patients’ management strategies, as well as resource allocation in case of surge capacity, and is a strong predictor of mortality.

CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

A labeler is designed to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation, in CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients.

Chest Imaging Appearance of COVID-19 Infection

Coronavirus disease 2019 (COVID-19) (previously known as novel coronavirus [2019-nCoV]), first reported in China, has now been declared a global health emergency by the World Health Organization. As

U-Net: Convolutional Networks for Biomedical Image Segmentation

It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.