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.

搜集汇总

背景与挑战

背景概述

CheXchoNet是一个包含71,589张独特胸部X光片的数据集，来自24,689名患者，每张X光片都配有金标准超声心动图测量值，用于检测左心室结构异常。这是首个将胸部X光与超声心动图数据配对的大规模数据集，旨在开发可扩展的心脏病筛查工具。