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Performance of a deep-learning algorithm for referable thoracic abnormalities on chest radiographs: A multicenter study of a health screening cohort

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  • Eun Young Kim
  • Young Jae Kim
  • Won-Jun Choi
  • Gi Pyo Lee
  • Ye Ra Choi
  • Kwang Nam Jin
  • Young Jun Cho

Abstract

Purpose: This study evaluated the performance of a commercially available deep-learning algorithm (DLA) (Insight CXR, Lunit, Seoul, South Korea) for referable thoracic abnormalities on chest X-ray (CXR) using a consecutively collected multicenter health screening cohort. Methods and materials: A consecutive health screening cohort of participants who underwent both CXR and chest computed tomography (CT) within 1 month was retrospectively collected from three institutions’ health care clinics (n = 5,887). Referable thoracic abnormalities were defined as any radiologic findings requiring further diagnostic evaluation or management, including DLA-target lesions of nodule/mass, consolidation, or pneumothorax. We evaluated the diagnostic performance of the DLA for referable thoracic abnormalities using the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity using ground truth based on chest CT (CT-GT). In addition, for CT-GT-positive cases, three independent radiologist readings were performed on CXR and clear visible (when more than two radiologists called) and visible (at least one radiologist called) abnormalities were defined as CXR-GTs (clear visible CXR-GT and visible CXR-GT, respectively) to evaluate the performance of the DLA. Results: Among 5,887 subjects (4,329 males; mean age 54±11 years), referable thoracic abnormalities were found in 618 (10.5%) based on CT-GT. DLA-target lesions were observed in 223 (4.0%), nodule/mass in 202 (3.4%), consolidation in 31 (0.5%), pneumothorax in one 1 (

Suggested Citation

  • Eun Young Kim & Young Jae Kim & Won-Jun Choi & Gi Pyo Lee & Ye Ra Choi & Kwang Nam Jin & Young Jun Cho, 2021. "Performance of a deep-learning algorithm for referable thoracic abnormalities on chest radiographs: A multicenter study of a health screening cohort," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-12, February.
    • Handle: RePEc:plo:pone00:0246472
    DOI: 10.1371/journal.pone.0246472
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    References listed on IDEAS

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    1. Pranav Rajpurkar & Jeremy Irvin & Robyn L Ball & Kaylie Zhu & Brandon Yang & Hershel Mehta & Tony Duan & Daisy Ding & Aarti Bagul & Curtis P Langlotz & Bhavik N Patel & Kristen W Yeom & Katie Shpanska, 2018. "Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists," PLOS Medicine, Public Library of Science, vol. 15(11), pages 1-17, November.
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