Enabling chronic obstructive pulmonary disease diagnosis through chest X-rays: A multi-site and multi-modality study

Ryan Wang; Li-Ching Chen; Lama Moukheiber; Kenneth P Seastedt; Mira Moukheiber; Dana Moukheiber; Zachary Zaiman; Sulaiman Moukheiber; Tess Litchman; Hari Trivedi; Rebecca Steinberg; Judy W Gichoya; Po-Chih Kuo; Leo A Celi

doi:10.1016/j.ijmedinf.2023.105211

Enabling chronic obstructive pulmonary disease diagnosis through chest X-rays: A multi-site and multi-modality study

Int J Med Inform. 2023 Oct:178:105211. doi: 10.1016/j.ijmedinf.2023.105211. Epub 2023 Sep 2.

Authors

Affiliations

¹ Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan.
² Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
³ Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
⁴ The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁵ Department of Computer Science, Emory University, Atlanta, GA, USA.
⁶ Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA.
⁷ Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
⁸ Department of Radiology, Emory University, Atlanta, GA, USA.
⁹ Department of Medicine, Emory University, Atlanta, GA, USA.
¹⁰ Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan. Electronic address: kuopc@cs.nthu.edu.tw.
¹¹ Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Division of Pulmonary Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

PMID: 37690225
DOI: 10.1016/j.ijmedinf.2023.105211

Abstract

Purpose: 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.

Method: 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.

Results: 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.

Conclusions: 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.

Keywords: Chest X-ray; Chronic obstructive pulmonary disease; Convolutional neural network; Data fusion; Model fusion.