Deep Learning–based Diagnosis of Pulmonary Tuberculosis on Chest X-ray in the Emergency Department: A Retrospective Study

IF 2.9 2区 工程技术 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Digital Imaging Pub Date : 2024-01-10 DOI:10.1007/s10278-023-00952-4
Chih-Hung Wang, Weishan Chang, Meng-Rui Lee, Joyce Tay, Cheng-Yi Wu, Meng-Che Wu, Holger R. Roth, Dong Yang, Can Zhao, Weichung Wang, Chien-Hua Huang
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Abstract

Prompt and correct detection of pulmonary tuberculosis (PTB) is critical in preventing its spread. We aimed to develop a deep learning–based algorithm for detecting PTB on chest X-ray (CXRs) in the emergency department. This retrospective study included 3498 CXRs acquired from the National Taiwan University Hospital (NTUH). The images were chronologically split into a training dataset, NTUH-1519 (images acquired during the years 2015 to 2019; n = 2144), and a testing dataset, NTUH-20 (images acquired during the year 2020; n = 1354). Public databases, including the NIH ChestX-ray14 dataset (model training; 112,120 images), Montgomery County (model testing; 138 images), and Shenzhen (model testing; 662 images), were also used in model development. EfficientNetV2 was the basic architecture of the algorithm. Images from ChestX-ray14 were employed for pseudo-labelling to perform semi-supervised learning. The algorithm demonstrated excellent performance in detecting PTB (area under the receiver operating characteristic curve [AUC] 0.878, 95% confidence interval [CI] 0.854–0.900) in NTUH-20. The algorithm showed significantly better performance in posterior-anterior (PA) CXR (AUC 0.940, 95% CI 0.912–0.965, p-value < 0.001) compared with anterior–posterior (AUC 0.782, 95% CI 0.644–0.897) or portable anterior–posterior (AUC 0.869, 95% CI 0.814–0.918) CXR. The algorithm accurately detected cases of bacteriologically confirmed PTB (AUC 0.854, 95% CI 0.823–0.883). Finally, the algorithm tested favourably in Montgomery County (AUC 0.838, 95% CI 0.765–0.904) and Shenzhen (AUC 0.806, 95% CI 0.771–0.839). A deep learning–based algorithm could detect PTB on CXR with excellent performance, which may help shorten the interval between detection and airborne isolation for patients with PTB.

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基于深度学习的急诊科胸部 X 光片肺结核诊断:一项回顾性研究
及时、正确地检测肺结核(PTB)对于防止其传播至关重要。我们旨在开发一种基于深度学习的算法,用于检测急诊科胸部 X 光片(CXR)上的肺结核。这项回顾性研究包括从台湾大学医院(台大医院)获取的 3498 张 CXR。这些图像按时间顺序分为训练数据集 NTUH-1519(2015 年至 2019 年采集的图像;n = 2144)和测试数据集 NTUH-20(2020 年采集的图像;n = 1354)。在模型开发过程中还使用了公共数据库,包括美国国立卫生研究院ChestX-ray14数据集(模型训练;112,120张图像)、蒙哥马利县(模型测试;138张图像)和深圳(模型测试;662张图像)。EfficientNetV2 是该算法的基本架构。来自 ChestX-ray14 的图像被用于伪标签,以执行半监督学习。在 NTUH-20 中,该算法在检测 PTB 方面表现出色(接收者操作特征曲线下面积 [AUC] 0.878,95% 置信区间 [CI] 0.854-0.900)。与前后位(AUC 0.782,95% CI 0.644-0.897)或便携式前后位(AUC 0.869,95% CI 0.814-0.918)CXR 相比,该算法在后前位(PA)CXR 中的表现明显更好(AUC 0.940,95% CI 0.912-0.965,p 值为 0.001)。该算法能准确检测出细菌学确诊的 PTB 病例(AUC 0.854,95% CI 0.823-0.883)。最后,该算法在蒙哥马利县(AUC 0.838,95% CI 0.765-0.904)和深圳(AUC 0.806,95% CI 0.771-0.839)的测试结果良好。基于深度学习的算法可以在CXR上检测出PTB,并且表现出色,这可能有助于缩短PTB患者从检测到空气隔离的时间间隔。
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来源期刊
Journal of Digital Imaging
Journal of Digital Imaging 医学-核医学
CiteScore
7.50
自引率
6.80%
发文量
192
审稿时长
6-12 weeks
期刊介绍: The Journal of Digital Imaging (JDI) is the official peer-reviewed journal of the Society for Imaging Informatics in Medicine (SIIM). JDI’s goal is to enhance the exchange of knowledge encompassed by the general topic of Imaging Informatics in Medicine such as research and practice in clinical, engineering, and information technologies and techniques in all medical imaging environments. JDI topics are of interest to researchers, developers, educators, physicians, and imaging informatics professionals. Suggested Topics PACS and component systems; imaging informatics for the enterprise; image-enabled electronic medical records; RIS and HIS; digital image acquisition; image processing; image data compression; 3D, visualization, and multimedia; speech recognition; computer-aided diagnosis; facilities design; imaging vocabularies and ontologies; Transforming the Radiological Interpretation Process (TRIP™); DICOM and other standards; workflow and process modeling and simulation; quality assurance; archive integrity and security; teleradiology; digital mammography; and radiological informatics education.
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