ISSN 1004-4140
CN 11-3017/P
Volume 30 Issue 6
Nov.  2021
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LIU Na, ZHAO Zhengkai, ZOU Jiayu, LI Yi, LIU Jian. Evaluation of Detection and Diagnostic Efficiency of Pulmonary Nodules by Chest CT Based on Artificial Intelligence[J]. CT Theory and Applications, 2021, 30(6): 709-715. DOI: 10.15953/j.1004-4140.2021.30.06.06
Citation: LIU Na, ZHAO Zhengkai, ZOU Jiayu, LI Yi, LIU Jian. Evaluation of Detection and Diagnostic Efficiency of Pulmonary Nodules by Chest CT Based on Artificial Intelligence[J]. CT Theory and Applications, 2021, 30(6): 709-715. DOI: 10.15953/j.1004-4140.2021.30.06.06
shu

Evaluation of Detection and Diagnostic Efficiency of Pulmonary Nodules by Chest CT Based on Artificial Intelligence

  • Department of Radiology, the Third People's Hospital of Chengdu, Chengdu 610031, China

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  • Received Date: January 27, 2020
  • Available Online: November 03, 2021
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    • Abstract
  • Objective: To evaluate the value of artificial intelligence (AI) based on deep learning in the detection and diagnosis of chest CT pulmonary nodules. Methods: A total of 172 patients with pulmonary nodules diagnosed by surgery in our hospital from June 2018 to April 2020 were collected, and a total of 204 nodules were surgically resected. 172 cases of preoperative high-resolution chest CT images were imported into the artificial intelligence recognition system, then artificial intelligence (AI) and radiologists detected and diagnosed the pulmonary nodules respectively, and we compared the sensitivity, positive predictive value and false positive rate between the two diagnostic methods. Using the pathological results as the gold standard for diagnosis, the sensitivity, specificity and area under the receiver operating characteristic (ROC) curve of AI and radiologists in the diagnosis of malignant pulmonary nodules were compared. Results: A total of 796 true nodules were detected in the 172 cases of high-resolution chest CT images. The sensitivity of AI and radiologists in detecting nodules was respectively 90.5% and 75.0%, the positive predictive value was respectively 74.5% and 99.7%, and the number of false positive nodules was respectively 247 and 2.As for the surgically resected 204 nodules, the sensitivity of AI, radiologist and AI-radiologist combination in diagnosing malignant pulmonary nodules was respectively 93.3%, 78.5% and 98.6%, the specificity was respectively 34.8%, 79.7% and 79.7%, and the area under the receiver operating characteristic (ROC) curve for the diagnosis of malignant pulmonary nodules by AI, radiologist and AI-radiologist combination was respectively 0.641, 0.791 and 0.819. Conclusion: The sensitivity of AI in detecting pulmonary nodules is significantly higher than that of radiologists, yet the false positive rate of AI is also higher. The efficiency of AI-radiologist combination in diagnosing malignant pulmonary nodules is higher than that of AI or radiologists separately. Therefore, we recommend that AI and radiologists should collaborate to detect and diagnose pulmonary nodules, which can reduce the rate of missed diagnosis and improve the diagnosis accuracy.
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