SPJ Publication
The Radpac
Open Access Short Communication

Evaluation of AI-Assisted Detection of Pulmonary Nodules on Chest CT

*

* Corresponding author: Rahat Kalam Aziz

DOI10.62502/tr/v1i1ar.5
Pages 21–25Language ENLicence CC BY-NC 4.0 Crossmark XML metadata
  1. Received24 Jun 2025
  2. Accepted30 Jul 2025
  3. Published25 Aug 2025

Abstract

Pulmonary nodules are commonly detected on chest computed tomography (CT), and their accurate identification is important for appropriate diagnosis and follow-up. However, small and subtle nodules may be difficult to detect during routine image interpretation. Artificial intelligence (AI)-assisted detection systems have emerged as supportive tools for improving pulmonary nodule detection. The present study evaluated the performance of an AI-assisted system for detecting pulmonary nodules on chest CT. A total of 1,256 reference-standard nodules were evaluated, including an independent test set of 376 nodules. Nodules were categorized according to size as <6 mm, 6–10 mm, and ≥10 mm. The AI system detected 1,194 of 1,256 nodules, giving an overall detection rate of 95.1%, while 62 nodules (4.9%) were missed. Detection performance varied according to nodule size, with larger nodules generally being detected more effectively than smaller nodules. These findings demonstrate that AI-assisted analysis can detect a high proportion of pulmonary nodules on chest CT and may provide useful support to radiologists during image interpretation. However, missed detections, particularly among small nodules, indicate that AI should be used as a supplementary tool rather than a replacement for expert radiological assessment. Further prospective and multicentre studies are required to validate the performance of AI-assisted pulmonary nodule detection in routine clinical practice.

Keywords

Pulmonary NodulesAI DetectionChest CT3D U-Net

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