Artificial intelligence-based lung nodule detection for pulmonary arteriovenous fistulas on chest computed tomography.
Authors
Affiliations (3)
Affiliations (3)
- Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, Ginowan 9012720, Okinawa, Japan. [email protected].
- Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, Ginowan 9012720, Okinawa, Japan.
- Department of Biostatistics, School of Health Sciences, Faculty of Medicine, University of the Ryukyus, Ginowan 9012720, Okinawa, Japan.
Abstract
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. To evaluate the detectability of PAVFs on chest CT using an AI-based CAD system for lung nodules. 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. 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 <i>vs</i> 58% for mediastinal window). Detection success was higher for complex-type lesions than for simple-type lesions (100% <i>vs</i> 59%, <i>P</i> = 0.26). CAD-derived maximum lesion length correlated strongly with manual measurements (<i>r</i> = 0.90, <i>P</i> < 0.001), as did CAD-derived lesion volume (<i>r</i> = 0.92, <i>P</i> < 0.001). 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.