BPG is committed to discovery and dissemination of knowledge
Featured Articles
4/24/2026 2:58:43 AM | Browse: 62 | Download: 180
 |
Received |
|
2026-02-09 01:23 |
|
Peer-Review Started |
|
2026-02-09 01:23 |
|
First Decision by Editorial Office Director |
|
2026-02-25 08:06 |
|
Return for Revision |
|
2026-02-25 08:06 |
|
Revised |
|
2026-03-05 12:35 |
 |
Publication Fee Transferred |
|
|
|
Second Decision by Editor |
|
2026-04-07 02:44 |
|
Second Decision by Editor-in-Chief |
|
|
|
Final Decision by Editorial Office Director |
|
2026-04-07 08:46 |
|
Articles in Press |
|
2026-04-07 08:46 |
|
Edit the Manuscript by Language Editor |
|
|
|
Typeset the Manuscript |
|
2026-04-15 00:15 |
|
Publish the Manuscript Online |
|
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
|
| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
|
| 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
|
| Manuscript Source |
Invited Manuscript |
| All Author List |
Kimei Azama, Nanae Tsuchiya, Shun Toyosato, Koji Yonemoto and Akihiro Nishie |
| Funding Agency and Grant Number |
|
| 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 |
All content on this site: Copyright © 1993-2026 Baishideng Publishing Group Inc, its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply.
