Application of artificial intelligence-assisted pulmonary nodule screening and qualitative diagnosis

LI Juanjuan; LIU Min; YANG Bin; DU Wei; YANG Hongkai; LIAO Yanquan; LI Junhang; WANG Jun

doi:10.7619/jcmp.20214698

Journal of Clinical Medicine in Practice > 2022 > 26(8): 8-12. > DOI: 10.7619/jcmp.20214698

LI Juanjuan, LIU Min, YANG Bin, DU Wei, YANG Hongkai, LIAO Yanquan, LI Junhang, WANG Jun. Application of artificial intelligence-assisted pulmonary nodule screening and qualitative diagnosis[J]. Journal of Clinical Medicine in Practice, 2022, 26(8): 8-12. DOI: 10.7619/jcmp.20214698

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Application of artificial intelligence-assisted pulmonary nodule screening and qualitative diagnosis

1.
Department of Medical Imaging, Xiangyun County People′s Hospital of Yunnan Province, the Ninth Affiliated Hospital of Dali University, Dali, Yunnan, 672100
2.
Medical Imaging Center, Kunming First People′s Hospital of Yunnan Province, Kunming, Yunnan, 650000
3.
Department of Radiology, the First Affiliated Hospital of Dali University, Dali, Yunnan, 671000

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Received Date: November 28, 2021
Available Online: April 27, 2022
Published Date: April 27, 2022

Graphical Abstract

Abstract

Abstract

Objective To explore the clinical research and application value of artificial intelligence (AI) aided diagnosis software in lung nodule screening and qualitative diagnosis of CT screening of chest low-dose CT.
Methods The clinical data of 103 patients with pulmonary nodules diagnosed by pathology were analyzed retrospectively. The preoperative chest low-dose CT images of 103 patients with pulmonary nodules were imported into the AI analysis software of apricot pulse sharp shadow pulmonary nodules. The methods of AI and radiologists′film reading were used to screen pulmonary nodules and make benign and malignant diagnosis. The AI aided diagnosis software was compared with the screening of pulmonary nodules by radiologists, and the pathological diagnosis was taken as the gold standard, the accuracy of AI aided diagnosis software and radiologist diagnosis was analyzed.
Results A total of 258 nodules were detected by chest low-dose CT in 103 patients. The sensitivity of pulmonary nodules detected by AI assistant software and radiologist were 96.12% and 89.53%, respectively, the positive predictive values were 95.00% and 100.00%, respectively, the false positive rate of pulmonary nodules detected by AI assisted diagnostic software was 5.00%, and radiologists did not detect false positive pulmonary nodules. There was significant difference between AI aided diagnosis software and radiologists in screening ability of pulmonary nodules (P < 0.05). A total of 108 nodules were diagnosed by pathological examination in 103 patients with pulmonary nodules, the sensitivity of AI aided diagnosis software and radiologists in diagnosing pulmonary nodules were 95.35% and 91.86%, respectively, and the specificities were 72.73% and 81.82%, respectively.
Conclusion AI aided diagnosis software has high accuracy in the screening and detection of pulmonary nodules and the diagnosis of malignant nodules, but the accuracy of differentiating benign from malignant pulmonary nodules is lower than that of radiologists. Therefore, AI aided diagnosis software as an auxiliary approach can be combined with diagnosis of radiologists to improve the overall diagnosis and treatment efficiency of pulmonary nodules.

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References

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Application of artificial intelligence-assisted pulmonary nodule screening and qualitative diagnosis