CheXchoNet: A Chest Radiograph Dataset with Gold Standard Echocardiography Labels

Existing chest radiograph datasets, such as CheXpert and ChestX-ray14, have
driven the development of new machine learning approaches to achieve expert or
near-expert level performance on a variety of tasks. The primary focus of
models developed using these datasets has been to replicate human-level
performance by training on labels computationally extracted from radiology
reports. We propose a different paradigm: pair an existing diagnostic test
with labels from a more accurate, higher fidelity diagnostic test. This
approach seeks to ask whether data from a cheaper, lower fidelity diagnostic
test contains information for detection of pathologies using more accurate,
gold standard labels. In the context of chest X-rays, a good example is the
radiologic comment of cardiomegaly, a catch-all term or an abnormally enlarged
heart. Cardiomegaly is known to be poorly predictive of cardiac disease and
does not trigger meaningful clinical action. Instead, we can pair chest X-rays
with gold standard structural heart disease labels derived from
echocardiograms conducted on the same patients. This resource contains 71,589
unique chest X-rays from 24,689 different patients paired with key
echocardiography measurements indicative of left ventricular hypertrophy and
dilated left ventricle, pathologies which occur during early stage heart
failure. The data also includes information about the relative times of the
chest X-rays, the age/sex of the patient at the time of recording, and related
metadata information. This data can be used as a resource for the community to
build novel approaches to detect clinically actionable labels.