ISSN 1004-4140
CN 11-3017/P
刘强, 曾勇明, 孙静坤, 等. 基于人工智能的CT肺结节检出影响因素分析:体模研究[J]. CT理论与应用研究(中英文), 2024, 33(4): 471-477. DOI: 10.15953/j.ctta.2023.190.
引用本文: 刘强, 曾勇明, 孙静坤, 等. 基于人工智能的CT肺结节检出影响因素分析:体模研究[J]. CT理论与应用研究(中英文), 2024, 33(4): 471-477. DOI: 10.15953/j.ctta.2023.190.
LIU Q, ZENG Y M, SUN J K, et al. Analysis of Influencing Factors on Pulmonary Nodule Detection by Computed Tomography with Artificial Intelligence: A Phantom Study[J]. CT Theory and Applications, 2024, 33(4): 471-477. DOI: 10.15953/j.ctta.2023.190. (in Chinese).
Citation: LIU Q, ZENG Y M, SUN J K, et al. Analysis of Influencing Factors on Pulmonary Nodule Detection by Computed Tomography with Artificial Intelligence: A Phantom Study[J]. CT Theory and Applications, 2024, 33(4): 471-477. DOI: 10.15953/j.ctta.2023.190. (in Chinese).

基于人工智能的CT肺结节检出影响因素分析:体模研究

Analysis of Influencing Factors on Pulmonary Nodule Detection by Computed Tomography with Artificial Intelligence: A Phantom Study

  • 摘要: 目的:基于胸部仿真体模实验,对人工智能(AI)诊断系统CT肺结节检出的影响因素进行分析。方法:使用东芝Aquilion CXL 64排CT,设定不同扫描参数(扫描剂量、重建算法及重建层厚)和非扫描参数(结节的大小、密度及胸部位置),对体模扫描并图像重建,运用AI诊断系统检出肺结节。结果:①不同剂量扫描时,肺结节检出真阳性率无统计学差异,低剂量模式时检出假阳性数较高。②3种迭代重建算法比较,检出的肺结节真阳性率无统计学差异,Strong组的真阳性率及假阳性数均较高。③薄层图像的肺结节检出真阳性率明显高于厚层图像。④纵隔旁肺结节真阳性率明显高于胸膜下和肺实质。⑤直径≥8 mm组肺结节检出真阳性率明显高于直径≤5 mm组。⑥3种密度肺结节检出时,真阳性率均无明显差异。结论:CT扫描因素(扫描剂量、重建算法及重建层厚)和非扫描因素(结节的大小、密度及胸部位置)可影响AI肺结节的检出。

     

    Abstract: Objective: This study aimed to analyze the factors influencing artificial intelligence (AI) diagnostic system detection of pulmonary nodules on computed tomography (CT) scans, using a chest simulation phantom experiment with simulated lung nodules. Methods: A Toshiba (Canon) 64-slice spiral CT scanner was used. The phantom was scanned and images were reconstructed under varying scanning parameters (dose, reconstruction algorithm, and layer thickness) and non-scanning parameters (nodule size, density, and chest location). Subsequently, the AI diagnostic system performed pulmonary nodule detection on the images. Results: (1) No statistically significant difference was observed in the true positive rate of pulmonary nodules detected at different scanning doses. However, the low-dose mode produced a higher number of false positives. (2) Comparisons across the three iterative reconstruction algorithms revealed no statistically significant difference in the true positive rate of detected pulmonary nodules. However, the Strong group exhibited a higher true positive rate and a higher number of false positives. (3) The true positive rate of lung nodules detected in thin images was significantly higher than in thick images. (4) The true positive rate of pulmonary nodules located in the paramediastinum was significantly higher in the subpleural and lung parenchyma. (5) The true positive rate of nodules ≥8 mm was significantly higher than that of nodules ≤5 mm. (6) No statistically significant difference was observed in the true positive rate when detecting nodules with differing densities. Conclusion: Both CT scanning parameters (dose, reconstruction algorithm, and layer thickness) and non-scanning parameters (nodule size, density, and chest location) can influence the detection of pulmonary nodules by AI systems.

     

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