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Cited by in F6Publishing
For: Handa T, Tanizawa K, Oguma T, Uozumi R, Watanabe K, Tanabe N, Niwamoto T, Shima H, Mori R, Nobashi TW, Sakamoto R, Kubo T, Kurosaki A, Kishi K, Nakamoto Y, Hirai T. Novel Artificial Intelligence-based Technology for Chest Computed Tomography Analysis of Idiopathic Pulmonary Fibrosis. Ann Am Thorac Soc 2021. [PMID: 34410886 DOI: 10.1513/AnnalsATS.202101-044OC] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 Nam JG, Choi Y, Lee SM, Yoon SH, Goo JM, Kim H. Prognostic value of deep learning-based fibrosis quantification on chest CT in idiopathic pulmonary fibrosis. Eur Radiol 2023. [PMID: 36928568 DOI: 10.1007/s00330-023-09534-y] [Reference Citation Analysis]
2 Aoki R, Iwasawa T, Saka T, Yamashiro T, Utsunomiya D, Misumi T, Baba T, Ogura T. Effects of Automatic Deep-Learning-Based Lung Analysis on Quantification of Interstitial Lung Disease: Correlation with Pulmonary Function Test Results and Prognosis. Diagnostics (Basel) 2022;12. [PMID: 36553045 DOI: 10.3390/diagnostics12123038] [Reference Citation Analysis]
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4 Furukawa T, Oyama S, Yokota H, Kondoh Y, Kataoka K, Johkoh T, Fukuoka J, Hashimoto N, Sakamoto K, Shiratori Y, Hasegawa Y. A comprehensible machine learning tool to differentially diagnose idiopathic pulmonary fibrosis from other chronic interstitial lung diseases. Respirology 2022. [PMID: 35697345 DOI: 10.1111/resp.14310] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
5 Choudhury S, Chohan A, Dadhwal R, Vakil AP, Franco R, Taweesedt PT. Applications of artificial intelligence in common pulmonary diseases. Artif Intell Med Imaging 2022; 3(1): 1-7 [DOI: 10.35711/aimi.v3.i1.1] [Reference Citation Analysis]
6 Tanabe N, Kaji S, Shima H, Shiraishi Y, Maetani T, Oguma T, Sato S, Hirai T. Kernel Conversion for Robust Quantitative Measurements of Archived Chest Computed Tomography Using Deep Learning-Based Image-to-Image Translation. Front Artif Intell 2022;4:769557. [DOI: 10.3389/frai.2021.769557] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
7 Du K, Zhu Y, Mao R, Qu Y, Cui B, Ma Y, Zhang X, Chen Z. Medium-long term prognosis prediction for idiopathic pulmonary fibrosis patients based on quantitative analysis of fibrotic lung volume. Respir Res 2022;23:372. [PMID: 36550474 DOI: 10.1186/s12931-022-02276-3] [Reference Citation Analysis]