Abstract
IntroductionTo 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).MethodA 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.ResultsThe 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.ConclusionUnder the operation and decision of AI, the combined scanning scheme may be beneficial for screening high-risk populations.
Highlights
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)
Total radiation doses were significantly lower for LDCT combined with local HRCT than for conventional-dose CT (358.93 vs. 473.67, p < 0.01) (Supplementary Table 1)
Assisted software was used to determine the number of lung nodules detected via LDCT and conventional-dose CT
Summary
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). Diagnosis and treatment are essential for improving survival and reducing mortality among patients with lung cancer. Low-dose computed tomography (LDCT) screening for lung cancer has been validated in several randomised controlled trials [2, 3], this method is not without limitations, including a high falsepositive rate [2]. The high false-positive rate is related to the relatively poor ability of LDCT to reveal fine structural details within lesions, compared to high-resolution computed. To our knowledge, no previous studies have investigated the types of lung nodules or patient subgroups in which this combined scanning strategy may be effective.
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