دورية أكاديمية
Enabling chronic obstructive pulmonary disease diagnosis through chest X-rays: A multi-site and multi-modality study.
| العنوان: | Enabling chronic obstructive pulmonary disease diagnosis through chest X-rays: A multi-site and multi-modality study. |
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| المؤلفون: | Wang R; Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan., Chen LC; Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan., Moukheiber L; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA., Seastedt KP; Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA., Moukheiber M; The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA., Moukheiber D; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA., Zaiman Z; Department of Computer Science, Emory University, Atlanta, GA, USA., Moukheiber S; Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA., Litchman T; Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA., Trivedi H; Department of Radiology, Emory University, Atlanta, GA, USA., Steinberg R; Department of Medicine, Emory University, Atlanta, GA, USA., Gichoya JW; Department of Radiology, Emory University, Atlanta, GA, USA., Kuo PC; Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan. Electronic address: kuopc@cs.nthu.edu.tw., Celi LA; 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. |
| المصدر: | International journal of medical informatics [Int J Med Inform] 2023 Oct; Vol. 178, pp. 105211. Date of Electronic Publication: 2023 Sep 02. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Science Ireland Ltd Country of Publication: Ireland NLM ID: 9711057 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-8243 (Electronic) Linking ISSN: 13865056 NLM ISO Abbreviation: Int J Med Inform Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Shannon, Co. Clare, Ireland : Elsevier Science Ireland Ltd., c1997- |
| مستخلص: | 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. Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2023 Elsevier B.V. All rights reserved.) |
| فهرسة مساهمة: | Keywords: Chest X-ray; Chronic obstructive pulmonary disease; Convolutional neural network; Data fusion; Model fusion |
| تواريخ الأحداث: | Date Created: 20230910 Latest Revision: 20231001 |
| رمز التحديث: | 20231002 |
| DOI: | 10.1016/j.ijmedinf.2023.105211 |
| PMID: | 37690225 |
| قاعدة البيانات: | MEDLINE |
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Full Text Finder | تدمد: | 1872-8243 |
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| DOI: | 10.1016/j.ijmedinf.2023.105211 |