Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Aug 17:2021:9971325.
doi: 10.1155/2021/9971325. eCollection 2021.

Evaluation of the Effectiveness of Artificial Intelligence Chest CT Lung Nodule Detection Based on Deep Learning

Fukui Liang  1 , Caiqin Li  1 , Xiaoqin Fu  1
Affiliations

Evaluation of the Effectiveness of Artificial Intelligence Chest CT Lung Nodule Detection Based on Deep Learning

Fukui Liang et al. J Healthc Eng. .

Retraction in

Abstract

Lung cancer is one of the most malignant tumors. If it can be detected early and treated actively, it can effectively improve a patient's survival rate. Therefore, early diagnosis of lung cancer is very important. Early-stage lung cancer usually appears as a solitary lung nodule on medical imaging. It usually appears as a round or nearly round dense shadow in the chest radiograph. It is difficult to distinguish lung nodules and lung soft tissues with the naked eye. Therefore, this article proposes a deep learning-based artificial intelligence chest CT lung nodule detection performance evaluation study, aiming to evaluate the value of chest CT imaging technology in the detection of noncalcified nodules and provide help for the detection and treatment of lung cancer. In this article, the Lung Medical Imaging Database Consortium (LIDC) was selected to obtain 536 usable cases based on inclusion criteria; 80 cases were selected for examination, artificial intelligence software, radiologists, and thoracic imaging specialists. Using 80 pulmonary nodules detection in each case, the pathological type of pulmonary nodules, nonlime tuberculous test results, detection sensitivity, false negative rate, false positive rate, and CT findings were individually analyzed, and the detection efficiency software of artificial intelligence was evaluated. Experiments have proved that the sensitivity of artificial intelligence software to detect noncalcified nodules in the pleural, peripheral, central, and hilar areas is higher than that of radiologists, indicating that the method proposed in this article has achieved good detection results. It has a better nodule detection sensitivity than a radiologist, reducing the complexity of the detection process.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Indicator reliability test analysis chart.
Figure 2
Figure 2
Nodule detection sensitivity analysis chart.
Figure 3
Figure 3
Analysis of the results of noncalcified nodule size detection.
Figure 4
Figure 4
Analysis of the detection results of the properties of noncalcified nodules.
Figure 5
Figure 5
Analysis of the detection results of noncalcified nodules.
Figure 6
Figure 6
Analysis of CT findings of pulmonary nodules (https://image.baidu.com/). (a) Test results. (b) Experts manually circle the results.
See this image and copyright information in PMC

References

    1. Ma J., Guarnera M. A., Zhou W., Fang H., Jiang F. A prediction model based on biomarkers and clinical characteristics for detection of lung cancer in pulmonary nodules. Translational oncology. 2017;10(1):40–45. doi: 10.1016/j.tranon.2016.11.001. - DOI - PMC - PubMed
    1. Hassabis D., Kumaran D., Summerfield C., Botvinick M. Neuroscience-inspired artificial intelligence. Neuron. 2017;95(2):245–258. doi: 10.1016/j.neuron.2017.06.011. - DOI - PubMed
    1. Nguyen X. V., Davies L., Eastwood J. D., Hoang J. K. Extrapulmonary findings and malignancies in participants screened with chest CT in the national lung screening trial. Journal of the American College of Radiology. 2017;14(3):324–330. doi: 10.1016/j.jacr.2016.09.044. - DOI - PubMed
    1. Liu C., Hu S.-C., Wang C., Lafata K., Yin F.-F. Automatic detection of pulmonary nodules on CT images with YOLOv3: development and evaluation using simulated and patient data. Quantitative Imaging in Medicine and Surgery. 2020;10(10):1917–1929. doi: 10.21037/qims-19-883. - DOI - PMC - PubMed
    1. Le V., Yang D., Zhu Y., et al. Automated classification of pulmonary nodules for lung adenocarcinomas risk evaluation: an effective CT analysis by clustering density distribution algorithm. Journal of Medical Imaging and Health Informatics. 2017;7(8):1753–1758. doi: 10.1166/jmihi.2017.2259. - DOI

Publication types

MeSH terms

LinkOut - more resources