ArticleJournal of thoracic disease2025
High-resolution computed tomography with 1,024-matrix for artificial intelligence-based computer-aided diagnosis in the evaluation of pulmonary nodules.
Article in Journal of thoracic disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Progressive Upsampling Generative Adversarial Network with Collaborative Attention for Single-Image Super-Resolution.Journal of imaging · 2026Article
- Non-contact artificial intelligence-assisted intraoperative 3D navigation technology prospective application study in lung cancer surgery.Journal of thoracic disease · 2025Article
- Performance Evaluation of Four Deep Learning-Based CAD Systems and Manual Reading for Pulmonary Nodules Detection, Volume Measurement, and Lung-RADS Classification Under Varying Radiation Doses and Reconstruction Methods.Diagnostics (Basel, Switzerland) · 2025Article
- Clinical analysis of incidental discovery of pulmonary nodules with computed tomography: a retrospective cohort study.Journal of thoracic disease · 2025Article
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Authors and funding
14 authors.
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Abstract
Background: Computed tomography (CT) plays an important role in the diagnosis of lung nodules and early screening of lung cancer. The purpose of this study was to compare the efficacy of 1,024×1,024 matrix and 512×512 matrix in an artificial intelligence-based computer-aided diagnosis (AI-CAD) for evaluating lung nodules based on CT images. Methods: This retrospective analysis included 344 patients from two hospitals between January 2020 and November 2023. CT images presenting lung nodules smaller than 30 mm were reconstructed using the 512×512 and 1,024×1,024 matrix. We evaluated image quality and AI-CAD detection of lung nodules. Image quality was subjectively scored using a 5-point Likert method and objectively assessed using image noise and signal-to-noise ratio (SNR). For lung nodules detection, we recorded the accuracy, precision, and recall of AI-CAD for detecting of different types and sizes of lung nodules. Results: The 512×512 matrix's overall image subjective evaluation score was 3.63, whereas the 1,024×1,024 matrix's was 4.18, among 344 individuals with 4,319 lung nodules. The detection accuracy, precision, and recall of 512×512 and 1,024×1,024 for AI-CAD in all lung nodules were 91.63% Conclusions: The 1,024 matrix performed significantly better than the 512 matrix in terms of overall subjective image quality and lung nodule AI-CAD detection rate.
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