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 [DOI: 10.4329/wjr.v18.i4.119851]
Corresponding Author of This Article
Kimei Azama, MD, PhD, Assistant Professor, Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, Kiyuna 1076, Ginowan 9012720, Okinawa, Japan. okinawan@iemik.onmicrosoft.com
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
Article-Type of This Article
Retrospective Cohort Study
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Radiol. Apr 28, 2026; 18(4): 119851 Published online Apr 28, 2026. doi: 10.4329/wjr.v18.i4.119851
Artificial intelligence-based lung nodule detection for pulmonary arteriovenous fistulas on chest computed tomography
Kimei Azama, Nanae Tsuchiya, Shun Toyosato, Koji Yonemoto, Akihiro Nishie
Kimei Azama, Nanae Tsuchiya, Shun Toyosato, Akihiro Nishie, Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, Ginowan 9012720, Okinawa, Japan
Koji Yonemoto, Department of Biostatistics, School of Health Sciences, Faculty of Medicine, University of the Ryukyus, Ginowan 9012720, Okinawa, Japan
Author contributions: Azama K drafted the manuscript and collected the data; Azama K and Tsuchiya N conceived and designed the study; Azama K, Tsuchiya N, and Toyosato S performed the image evaluation; Azama K and Tsuchiya N analyzed and interpreted the data; Yonemoto K verified the statistical methods and reviewed the statistical analyses; Tsuchiya N and Nishie A critically revised the manuscript for important intellectual content; and all authors approved the final version of the manuscript.
Institutional review board statement: This study was approved by the Medical Ethics Committee of University of the Ryukyus, approval No. 1938.
Informed consent statement: The requirement for informed consent was waived due to the retrospective nature of the study.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: The data that support the findings of this study are available from the corresponding author upon reasonable request. Due to institutional and ethical restrictions, the data are not publicly available.
Corresponding author: Kimei Azama, MD, PhD, Assistant Professor, Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, Kiyuna 1076, Ginowan 9012720, Okinawa, Japan. okinawan@iemik.onmicrosoft.com
Received: February 9, 2026 Revised: March 5, 2026 Accepted: April 7, 2026 Published online: April 28, 2026 Processing time: 75 Days and 21.7 Hours
Abstract
BACKGROUND
Pulmonary arteriovenous fistulas (PAVFs) are abnormal vascular communications between pulmonary arteries and veins that may cause hypoxemia and paradoxical embolism. Because many patients are asymptomatic, PAVFs are often detected incidentally on chest computed tomography (CT). Accurate identification of PAVFs is clinically important for appropriate management; however, small or atypical lesions may be overlooked during routine interpretation. Computer-aided detection (CAD) systems for pulmonary nodules are widely used in clinical practice, but their ability to detect PAVFs has not been systematically evaluated. We hypothesized that a lung nodule-based artificial intelligence (AI)-CAD system could detect PAVFs on chest CT.
AIM
To evaluate the detectability of PAVFs on chest CT using an AI-based CAD system for lung nodules.
METHODS
This retrospective observational study included 21 patients with 26 PAVFs identified at University of the Ryukyus Hospital between 2009 and 2021. Chest CT images, including non-contrast and contrast-enhanced scans, were analyzed using a commercially available AI-based lung nodule CAD system. Detection performance was classified as consistent, conditional, or failed detection, and lesion characteristics associated with successful detection were analyzed. Correlations between CAD-derived measurements and manual measurements were assessed using Pearson’s correlation coefficient.
RESULTS
Among the 26 PAVFs, 15 lesions (58%) were consistently detected, 2 lesions (8%) were detected under certain imaging conditions, and 9 lesions (35%) were not detected, yielding an overall detection success rate of 65% (17/26). Detection rates did not differ significantly according to contrast phase (58% for non-contrast, 71% for pulmonary arterial phase, and 47% for parenchymal phase) or window setting (61% for lung window vs 58% for mediastinal window). Detection success was higher for complex-type lesions than for simple-type lesions (100% vs 59%, P = 0.26). CAD-derived maximum lesion length correlated strongly with manual measurements (r = 0.90, P < 0.001), as did CAD-derived lesion volume (r = 0.92, P < 0.001).
CONCLUSION
A lung nodule-based AI-CAD system detected a substantial proportion of PAVFs on chest CT and provided reliable quantitative measurements, supporting its potential adjunctive role in PAVF detection and follow-up.
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.
