ArticleFrontiers in oncology2021
Artificial Intelligence-Aided Diagnosis Software to Identify Highly Suspicious Pulmonary Nodules.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Has computer-aided diagnosis now outperformed radiologists in the accurate localization of lung nodules?-a multicenter retrospective study.Quantitative imaging in medicine and surgery · 2026Article
- A novel strategy to bind pyrimidine sulfonamide derivatives with odd even chains: exploration of their design, synthesis and biological activity evaluation.Molecular diversity · 2024Article
- Unplanned Excision of Soft Tissue Sarcomas of the Extremities in a Low-to-Middle-Income Country.Annals of surgical oncology · 2023Review
- Early Diagnosis of Lung Cancer: The Urgent Need of a Clinical Test.Journal of clinical medicine · 2022Article
- Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning.Frontiers in cardiovascular medicine · 2022Article
- Prognostic Predicting Model of Pancreatic Body Tail Carcinoma Using Clinical and CT Radiomic Data.Technology in cancer research & treatmentArticle
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Authors and funding
15 authors.
Funding
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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.
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