ISSN 1004-4140
CN 11-3017/P
Volume 30 Issue 4
Sep.  2021
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WEN Deying, PAN Xuelin, YAO Hui, LI Jijie, DENG Qiao, TANG Lu, WU Xi, SUN Jiayu. To Explore the Effect of CT Scan Dose on the Efficacy of Artificial Intelligence in Detecting Lung Nodules[J]. CT Theory and Applications, 2021, 30(4): 455-465. DOI: 10.15953/j.1004-4140.2021.30.04.06
Citation: WEN Deying, PAN Xuelin, YAO Hui, LI Jijie, DENG Qiao, TANG Lu, WU Xi, SUN Jiayu. To Explore the Effect of CT Scan Dose on the Efficacy of Artificial Intelligence in Detecting Lung Nodules[J]. CT Theory and Applications, 2021, 30(4): 455-465. DOI: 10.15953/j.1004-4140.2021.30.04.06
shu

To Explore the Effect of CT Scan Dose on the Efficacy of Artificial Intelligence in Detecting Lung Nodules

  • 1. Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China;

  • 2. Department of Medical Record, West China Second Hospital, Sichuan University, Chengdu 610041, China

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  • Received Date: April 28, 2021
  • Available Online: September 23, 2021
    Graphical Abstract
    • Abstract
  • Objective: To explore the factors that affect the effectiveness of artificial intelligence systems in detecting lung nodules, and strive to provide personalized scanning conditions and artificial intelligence systems for nodules of different natures, and provide references for the appropriate scanning conditions of various artificial intelligence systems. Methods: Standard adult male chest X-ray/CT image model, in which 15 simulated lung nodules with different density and size are randomly distributed was scanned by different tube voltages and tube current at a total of 50 times. The artificial intelligence systems of different companies are used to detect lung nodules. The Pearson χ2 test or Fisher exact probability method is used to compare the detection rate and false negative rate of each group; the Kruskal-Wallis H test is used to compare the false positive rate. Results: (1) Under different kV conditions, the detection rates of nodules of different densities and sizes of company A and C were not statistically different; the detection rate of +100 nodules in company B is higher in the 70kV (100%) group than that in the 120kV (80%) and 140kV (80%) group; the detection rate of 3mm nodules in company B was higher in the 70kV group (33.33%) than in the 120kV (0%) and 140kV (0%) group. (2) There was no significant difference in detection rate and false negative rate among different mAs in each kV group of the three companies or each kV group. The difference in false positive rate among the kV groups was statistically significant. (3) The detection rate of company A in the70kV group (64.44%) is lower than that of company B (80.00%), and the false negative rate (35.56%) is higher than that of company B (20.00%); The false positive rate of company A is higher than that of company B and company C. There is no statistical difference in the detection rate, false negative rate, and false positive rate between company B and C. Conclusions: The sensitivity of artificial intelligence-assisted lung nodule detection has nothing to do with the CT scan dose, but is related to the nature of the nodule and the performance of the AI system. In this study, the overall performance of company B and C is higher than that of company A, and the best scanning tube voltages are 70kV, 70kV, and 100kV respectively.
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