Wang Ryan, Chen Li-Ching, Moukheiber Lama, Seastedt Kenneth P, Moukheiber Mira, Moukheiber Dana, Zaiman Zachary, Moukheiber Sulaiman, Litchman Tess, Trivedi Hari, Steinberg Rebecca, Gichoya Judy W, Kuo Po-Chih, Celi Leo A
Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan.
Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Int J Med Inform. 2023 Oct;178:105211. doi: 10.1016/j.ijmedinf.2023.105211. Epub 2023 Sep 2.
Chronic obstructive pulmonary disease (COPD) is one of the most common chronic illnesses in the world. Unfortunately, COPD is often difficult to diagnose early when interventions can alter the disease course, and it is underdiagnosed or only diagnosed too late for effective treatment. Currently, spirometry is the gold standard for diagnosing COPD but it can be challenging to obtain, especially in resource-poor countries. Chest X-rays (CXRs), however, are readily available and may have the potential as a screening tool to identify patients with COPD who should undergo further testing or intervention. In this study, we used three CXR datasets alongside their respective electronic health records (EHR) to develop and externally validate our models.
To leverage the performance of convolutional neural network models, we proposed two fusion schemes: (1) model-level fusion, using Bootstrap aggregating to aggregate predictions from two models, (2) data-level fusion, using CXR image data from different institutions or multi-modal data, CXR image data, and EHR data for model training. Fairness analysis was then performed to evaluate the models across different demographic groups.
Our results demonstrate that DL models can detect COPD using CXRs with an area under the curve of over 0.75, which could facilitate patient screening for COPD, especially in low-resource regions where CXRs are more accessible than spirometry.
By using a ubiquitous test, future research could build on this work to detect COPD in patients early who would not otherwise have been diagnosed or treated, altering the course of this highly morbid disease.
慢性阻塞性肺疾病（COPD）是世界上最常见的慢性病之一。不幸的是，COPD在干预措施能够改变疾病进程时往往难以早期诊断，存在诊断不足或诊断过晚而无法进行有效治疗的情况。目前，肺活量测定法是诊断COPD的金标准，但获取该检测可能具有挑战性，尤其是在资源匮乏的国家。然而，胸部X光片（CXR）很容易获得，并且可能有潜力作为一种筛查工具，用于识别应该接受进一步检测或干预的COPD患者。在本研究中，我们使用了三个CXR数据集及其各自的电子健康记录（EHR）来开发并外部验证我们的模型。
为了利用卷积神经网络模型的性能，我们提出了两种融合方案：（1）模型级融合，使用自助聚合来聚合来自两个模型的预测结果；（2）数据级融合，使用来自不同机构的CXR图像数据或多模态数据、CXR图像数据和EHR数据进行模型训练。然后进行公平性分析，以评估不同人口群体的模型。
我们的结果表明，深度学习模型可以使用CXR检测COPD，曲线下面积超过0.75，这有助于对COPD患者进行筛查，特别是在CXR比肺活量测定法更容易获得的低资源地区。
通过使用一种普遍可用的检测方法，未来的研究可以在此基础上开展工作，早期发现那些原本不会被诊断或治疗的COPD患者，从而改变这种高发病率疾病的病程。
