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Articles Published Processes
4/24/2026 2:48:52 AM | Browse: 1 | Download: 1
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Received |
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2026-02-09 01:23 |
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Peer-Review Started |
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2026-02-09 01:23 |
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First Decision by Editorial Office Director |
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2026-02-25 08:06 |
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Return for Revision |
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2026-02-25 08:06 |
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Revised |
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2026-03-05 12:35 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-04-07 02:44 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-04-07 08:46 |
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Articles in Press |
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2026-04-07 08:46 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-04-15 00:15 |
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Publish the Manuscript Online |
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2026-04-24 02:48 |
| ISSN |
1949-8470 (online) |
| Open Access |
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. |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Radiology, Nuclear Medicine & Medical Imaging |
| Manuscript Type |
Retrospective Cohort Study |
| Article Title |
Artificial intelligence-based lung nodule detection for pulmonary arteriovenous fistulas on chest computed tomography
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Kimei Azama, Nanae Tsuchiya, Shun Toyosato, Koji Yonemoto and Akihiro Nishie |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Kimei Azama, Assistant Professor, MD, PhD, Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, Kiyuna 1076, Ginowan 9012720, Okinawa, Japan. okinawan@iemik.onmicrosoft.com |
| Key Words |
Pulmonary arteriovenous fistulas; Lung nodule; Artificial intelligence; Computer-aided detection; Computed tomography |
| Core Tip |
Pulmonary arteriovenous fistulas (PAVFs) are vascular lesions, frequently detected on chest computed tomography. This study demonstrated that an artificial intelligence-based computer-aided detection (CAD) system originally developed for pulmonary nodule detection identified PAVFs with a detection rate of 65% and provided reliable quantitative measurements of lesion length. Although the detection performance was lower than that previously reported for pulmonary nodules, CAD-based analysis showed strong agreement with manual measurements. These findings suggest that lung nodule CAD systems may have broader clinical utility beyond their original purpose, including a potential role as adjunct tools for the opportunistic detection and longitudinal follow-up of PAVFs. |
| Publish Date |
2026-04-24 02:48 |
| 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 |
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