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Stanford releases large dataset of chest X-rays to develop AI for Chest radiography

CheXpert, the database was released by Stanford's Machine Learning Group

CheXpert is a large dataset of chest X-rays and competition for automated chest x-ray interpretation, which features uncertainty labels and radiologist-labeled reference standard evaluation sets.This was recently published by Stanford University School of Medicine researchers.

Chest radiography is the most common imaging examination globally, critical for screening, diagnosis, and management of many life threatening diseases. Automated chest radiograph interpretation at the level of practicing radiologists could provide substantial benefit in many medical settings, from improved workflow prioritization and clinical decision support to large-scale screening and global population health initiatives.”For progress in both development and validation of automated algorithms, we realized there was a need for a labeled dataset that was large, had strong reference standards, and provided expert human performance metrics for comparison,” says the Stanford researchers group led by Jeremy Irvin, Pranav Rajpurkar et al.

CheXpert is a large public dataset for chest radiograph interpretation, consisting of 224,316 chest radiographs of 65,240 patients. “We retrospectively collected the chest radiographic examinations from Stanford Hospital, performed between October 2002 and July 2017 in both inpatient and outpatient centers, along with their associated radiology reports,” says the researchers.

Curt Langlotz, Professor of Radiology says, ” In the U.S., about half of all radiology studies are x-rays, mostly of the chest. Chest x-ray studies are even more common around the world. Chest x-ray interpretation is a “bread and butter” problem for radiologists with vital public health implications. Chest x-rays can stop the spread of tuberculosis, detect lung cancer early, and support the responsible use of antibiotics.”

“Ground truth is critical in evaluating deep learning models in medical imaging and provide the foundation for clinical relevance when interpreting results in this field – this is why we focus a lot of our effort on considering the best available ground truth via a panel of medical sub specialist experts to best understand the clinical implication of our model results,” says Matt Lungren, Assistant Professor of Radiology.

“We released the dataset to the public as a standard benchmark to evaluate performance of chest radiograph interpretation models,” researchers said.

The data can be accessed at the CheXpert website here. Developers can develop AI algorithms and use this data to test their models. The group has also launched a competition for developers of AI algorithms to test their models.

One of the main obstacles in the development of chest radiograph interpretation models has been the lack of datasets with strong radiologist-annotated groundtruth and expert scores against which researchers can compare their models. “We hope that CheXpert will address that gap, making it easy to track the progress of models over time on a clinically important task,” say the researchers.

“Furthermore, we have developed and open-sourced the CheXpert labeler, an automated rule-based labeler to extract observations from the free text radiology reports to be used as structured labels for the images. We hope that this makes it easy to help other institutions extract structured labels from their reports and release other large repositories of data that will allow for cross-institutional testing of medical imaging models,” they add.

 

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