Evidence map›Paper›PMID 38048012›Full record

ArticleJournal of medical systems2023

Deep Learning-based Diagnosis and Localization of Pneumothorax on Portable Supine Chest X-ray in Intensive and Emergency Medicine: A Retrospective Study.

Chih-Hung Wang, Tzuching Lin, Guanru Chen, Meng-Rui Lee, Joyce Tay, Cheng-Yi Wu, Meng-Che Wu, Holger R Roth, Dong Yang, Can Zhao and 2 more

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Article in Journal of medical systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

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5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Chih-Hung WangDepartment of Emergency Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.
Tzuching Lin *Institute of Applied Mathematical Sciences, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 106, Taiwan.
Guanru Chen *Institute of Applied Mathematical Sciences, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 106, Taiwan.
Meng-Rui LeeDepartment of internal medicine, National Taiwan University Hospital, Taipei, Taiwan.
Joyce TayDepartment of Emergency Medicine, National Taiwan University Hospital, No. 7, Zhongshan S. Rd., Taipei, Zhongzheng Dist., 100, Taiwan.
Cheng-Yi WuDepartment of Emergency Medicine, National Taiwan University Hospital, No. 7, Zhongshan S. Rd., Taipei, Zhongzheng Dist., 100, Taiwan.
Meng-Che WuDepartment of Emergency Medicine, National Taiwan University Hospital, No. 7, Zhongshan S. Rd., Taipei, Zhongzheng Dist., 100, Taiwan.
Holger R RothNVIDIA Corporation, Bethesda, USA.
Dong YangNVIDIA Corporation, Bethesda, USA.
Can ZhaoNVIDIA Corporation, Bethesda, USA.
Weichung Wang *Institute of Applied Mathematical Sciences, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 106, Taiwan. wwang@ntu.edu.tw.
Chien-Hua Huang *Department of Emergency Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan. chhuang5940@ntu.edu.tw.

Funding

National Science and Technology Council MOST 111-2634-F-002-015National Taiwan University Hospital 112-UN0022
6 · The paper itself

Abstract

purposeTo develop two deep learning-based systems for diagnosing and localizing pneumothorax on portable supine chest X-rays (SCXRs).

methodsFor this retrospective study, images meeting the following inclusion criteria were included: (1) patient age ≥ 20 years; (2) portable SCXR; (3) imaging obtained in the emergency department or intensive care unit. Included images were temporally split into training (1571 images, between January 2015 and December 2019) and testing (1071 images, between January 2020 to December 2020) datasets. All images were annotated using pixel-level labels. Object detection and image segmentation were adopted to develop separate systems. For the detection-based system, EfficientNet-B2, DneseNet-121, and Inception-v3 were the architecture for the classification model; Deformable DETR, TOOD, and VFNet were the architecture for the localization model. Both classification and localization models of the segmentation-based system shared the UNet architecture.

resultsIn diagnosing pneumothorax, performance was excellent for both detection-based (Area under receiver operating characteristics curve [AUC]: 0.940, 95% confidence interval [CI]: 0.907-0.967) and segmentation-based (AUC: 0.979, 95% CI: 0.963-0.991) systems. For images with both predicted and ground-truth pneumothorax, lesion localization was highly accurate (detection-based Dice coefficient: 0.758, 95% CI: 0.707-0.806; segmentation-based Dice coefficient: 0.681, 95% CI: 0.642-0.721). The performance of the two deep learning-based systems declined as pneumothorax size diminished. Nonetheless, both systems were similar or better than human readers in diagnosis or localization performance across all sizes of pneumothorax.

conclusionsBoth deep learning-based systems excelled when tested in a temporally different dataset with differing patient or image characteristics, showing favourable potential for external generalizability.

Indexed as

Deep LearningEmergency MedicinePneumothoraxAdultHumansRetrospective StudiesX-RaysYoung AdultChest RadiographDeep LearningDetectionLocalizationPneumothorax

Identifiers

PMID38048012
PMCPMC10695857

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