Factors for increasing positive predictive value of pneumothorax detection on chest radiographs using artificial intelligence
Author:
Funder
Grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute
Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s41598-024-70780-1.pdf
Reference22 articles.
1. Jouneau, S. et al. SPLF/SMFU/SRLF/SFAR/SFCTCV Guidelines for the management of patients with primary spontaneous pneumothorax. Ann. Intensive Care 13, 88. https://doi.org/10.1186/s13613-023-01181-2 (2023).
2. Barton, E. C., Maskell, N. A. & Walker, S. P. Expert review on spontaneous pneumothorax: Advances, controversies, and new directions. Semin. Res. Crit. Care Med. 44, 426–436. https://doi.org/10.1055/s-0043-1769615 (2023).
3. Butler, H. et al. A scoping review of clinical practice guidelines for the diagnosis of primary spontaneous pneumothorax. J. Med. Imaging Radiat. Sci. 53, 728–736. https://doi.org/10.1016/j.jmir.2022.09.005 (2022).
4. Hwang, E. J. et al. Artificial intelligence system for identification of false-negative interpretations in chest radiographs. Eur. Radiol. https://doi.org/10.1007/s00330-022-08593-x (2022).
5. Shin, H. J., Lee, S., Kim, S., Son, N. H. & Kim, E. K. Hospital-wide survey of clinical experience with artificial intelligence applied to daily chest radiographs. PloS One 18, e0282123. https://doi.org/10.1371/journal.pone.0282123 (2023).
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