Overview

Since the onset of the COVID-19 pandemic, the global scientific community has come together to rapidly advance our understanding of this disease. The overarching goal of COVID-19 research is to learn about the disease so that we can improve medical care and uncover new drug therapies.

To support the global research community, Invicro has created a secure COVID-19 iPACS® platform for hosting COVID-19 image data and associated metadata, focused on X-ray and CT imaging. The platform is a free and secure, open repository of organized and curated COVID-19 image data that is available for all to use. Invicro has partnered with Microsoft® to host the COVID-19 iPACS on their Azure® cloud platform. A key distinction of the iPACS platform is strong metadata functionality based on the CDC case report form, a function that will simplify image data analysis for the global research community.

We call on the global research and medical communities to both share their preclinical and clinical COVID-19 image data and utilize the image data repository to gain insights from the disease for the benefit of humankind.

Contact us for more information or view press release.

View COVID-19 iPACS

Imaging in COVID-19 Research

Imaging in respiratory disease, including COVID-19 and more generally acute respiratory distress syndrome (ARDS) is a key clinical assay to monitor inflammation, edema, and vascular dysfunction (e.g., hemorrhage). X-ray imaging is a clinical mainstay and is among the most available imaging technologies around the world. CT imaging provides a full 3D visualization of the lung, enabling more regional and specific diagnostic assessment. Both chest CT and X-ray have been used as key tools for the diagnosis and clinical characterization of COVID-19 and have been applied to the study of disease severity, progression, and recovery.

In addition to the lung disease, imaging in COVID-19 patients has revealed additional complications associated with the disease that can be monitored by imaging methodologies, including:

 

  • CT images have enabled the identification of pulmonary embolism as a critical complication
  • Diagnosis of stroke with CT and CT angiography
  • CT and MRI identification of COVID-19–associated acute hemorrhagic necrotizing encephalopathy

 

References

CT and X-ray

Clinical Characteristics of Coronavirus Disease 2019 in China

Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients

Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review

Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review

Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases

Clinical Characterization

Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study

Pulmonary Embolism

Pulmonary embolism in patients with COVID-19: Time to change the paradigm of computed tomography

Diagnosis of Stroke

Large-Vessel Stroke as a Presenting Feature of Covid-19 in the Young

Advanced Image Analysis

While imaging is critical for patient management and treatment planning, the complex presentation of COVID-19 complicates radiological assessment, requiring radiologists and pulmonologists to learn in real-time and with limited data. Advanced image analysis offers the opportunity to assist medical professionals in the diagnosis and staging of disease and, potentially, in the prediction of progression and need for intensive medical care. Analysis methods, including machine learning and artificial intelligence, require large amounts of data that encapsulate the diversity of phenotypes to develop novel tools.

It is our goal to accelerate these efforts by enabling global data scientists to both contribute and access curated data to garner additional insights. There are illustrative examples of initial analytical tools in development that we hope can be expanded and deployed, including:

 

  • Artificial intelligence and deep learning have been utilized to identify COVID-19 patients from pneumonia using chest CT images
  • Neural networks or deep learning to detect COVID-19 disease using chest X-ray images
  • Deep learning combined with chest CT images to develop a ‘score’ of scans to predict progression over time

 

References

Artificial Intelligence and Deep Learning

Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT

Lung Infection Quantification of COVID-19 in CT Images with Deep Learning

A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)

Neural Networks and Deep Learning Tools

COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images

Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks

Covid‑19: automatic detection from X‑ray images utilizing transfer learning with convolutional neural networks

COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Deep Learning to Predict Progression

Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis

Partners for COVID-19 Research

Invicro would like to thank our partners at Microsoft®, Johns Hopkins University, WMIS and other leading medical institutions for their support and contributions to the COVID-19 iPACS. For more information go to https://covid19.ipacs.invicro.com or email us at covid19research@invicro.com.