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

Table 2 Differences in computer-aided detection rates according to imaging conditions, n (%)

Imaging conditions		Overall (image series, n = 74)	Detection success (image series, n = 44)	Detection failure (image series, n = 30)	P value
Contrast	Non-contrast	33	19 (58)	14 (42)	0.29
	Pulmonary arterial phase	24	17 (71)	7 (29)
	Parenchymal phase	17	8 (47)	9 (53)
Window setting	Lung window	31	19 (61)	12 (39)	0.79
Window setting	Mediastinal window	43	25 (58)	18 (42)	0.79

Detection success and failure were evaluated on an image-series basis. A total of 26 lesions with 74 imaging series were evaluated.

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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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