Introduction: To evaluate the value of artificial intelligence (AI)-assisted software in the diagnosis of lung nodules using a combination of low-dose computed tomography (LDCT) and high-resolution computed tomography (HRCT).
Method: A total of 113 patients with pulmonary nodules were screened using LDCT. For nodules with the largest diameters, an HRCT local-target scanning program (combined scanning scheme) and a conventional-dose CT scanning scheme were also performed. Lung nodules were subjectively assessed for image signs and compared by size and malignancy rate measured by AI-assisted software. The nodules were divided into improved visibility and identical visibility groups based on differences in the number of signs identified through the two schemes.
Results: The nodule volume and malignancy probability for subsolid nodules significantly differed between the improved and identical visibility groups. For the combined scanning protocol, we observed significant between-group differences in subsolid nodule malignancy rates.
Conclusion: Under the operation and decision of AI, the combined scanning scheme may be beneficial for screening high-risk populations.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8886673 | PMC |
| http://dx.doi.org/10.3389/fonc.2021.749219 | DOI Listing |
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