COVID-19 Image Data Collection: Prospective Predictions Are the Future

TL;DR

This paper introduces the first large public COVID-19 chest X-ray dataset, enabling development of predictive tools for patient prognosis and disease progression, with initial use cases demonstrated.

Contribution

It provides the largest publicly available COVID-19 image dataset with detailed metadata, facilitating machine learning research for diagnosis and prognosis.

Findings

Dataset contains hundreds of frontal and lateral X-rays with metadata

Potential use cases include predicting ICU need and patient survival

Dataset supports trajectory analysis during treatment

Abstract

Across the world's coronavirus disease 2019 (COVID-19) hot spots, the need to streamline patient diagnosis and management has become more pressing than ever. As one of the main imaging tools, chest X-rays (CXRs) are common, fast, non-invasive, relatively cheap, and potentially bedside to monitor the progression of the disease. This paper describes the first public COVID-19 image data collection as well as a preliminary exploration of possible use cases for the data. This dataset currently contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of COVID-19. It was manually aggregated from publication figures as well as various web based repositories into a machine learning (ML) friendly format with accompanying dataloader code. We collected frontal and lateral view imagery and metadata such as the time since first symptoms, intensive care unit (ICU) status, survival status, intubation status, or hospital location. We present multiple possible use cases for the data such as predicting the need for the ICU, predicting patient survival, and understanding a patient's trajectory during treatment. Data can be accessed here: https://github.com/ieee8023/covid-chestxray-dataset