Artificial intelligence-based lung nodule detection for pulmonary arteriovenous fistulas on chest computed tomography

doi:10.4329/wjr.v18.i4.119851

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World Journal of Radiology

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1949-8470

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Radiol. Apr 28, 2026; 18(4): 119851
Published online Apr 28, 2026. doi: 10.4329/wjr.v18.i4.119851

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Figure 3 Correlation of lesion measurements between the computer-aided detection system and manual assessment. Pearson’s correlation coefficient (r) and corresponding P values are shown. A and B: Scatter plots demonstrating the correlation between computer-aided detection-measured maximum lesion length and computer-aided detection-measured lesion volume and the manually measured maximum lesion length. CAD: Computer-aided detection.

Citation: Azama K, Tsuchiya N, Toyosato S, Yonemoto K, Nishie A. Artificial intelligence-based lung nodule detection for pulmonary arteriovenous fistulas on chest computed tomography. World J Radiol 2026; 18(4): 119851
URL: https://www.wjgnet.com/1949-8470/full/v18/i4/119851.htm
DOI: https://dx.doi.org/10.4329/wjr.v18.i4.119851

Azama K, Tsuchiya N, Toyosato S, Yonemoto K, Nishie A. Artificial intelligence-based lung nodule detection for pulmonary arteriovenous fistulas on chest computed tomography. World J Radiol 2026; 18(4): 119851 [PMID: 42057839 DOI: 10.4329/wjr.v18.i4.119851]

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