CheXchoNet: A Chest Radiograph Dataset with Gold Standard Echocardiography Labels
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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.
现有的胸部X线摄影（chest radiograph）数据集，例如CheXpert与ChestX-ray14，推动了新型机器学习方法的发展，使其在多项医学影像任务中达到专家级或接近专家级的性能水平。依托此类数据集开发的模型，其核心目标是通过对从放射学报告中经计算机自动提取的标签进行训练，以复现人类水平的诊断性能。我们提出了一种全新的研究范式：将现有诊断检测手段与更为精准、保真度更高的诊断检测所对应的标签进行配对关联。该研究范式旨在探究：成本更低、保真度更低的诊断检测所产生的数据，是否蕴含利用金标准（gold standard）标签来检测病理特征的有效信息。以胸部X线为例，典型案例为心脏增大（cardiomegaly）的放射学诊断描述——该术语为泛指性表述，指心脏出现异常增大的情况。现有研究表明，心脏增大对心脏疾病的预测效能较差，且无法触发具有临床意义的诊疗干预措施。对此，我们可将胸部X线影像与源自同一患者超声心动图（echocardiogram）的金标准结构性心脏病标签进行配对关联。本数据集包含来自24689名独立患者的71589张独特胸部X线影像，这些影像与反映左心室肥厚（left ventricular hypertrophy）与左心室扩张（dilated left ventricle）的关键超声心动图测量指标相配对——这两类病理特征均出现在心力衰竭（heart failure）的早期阶段。该数据集还包含胸部X线拍摄的相对时序、拍摄时患者的年龄与性别，以及相关元数据（metadata）信息。本数据集可作为开源资源供学界使用，用于开发能够检测具有临床指导价值标签的新型机器学习方法。
