Republic of Korea
Analysis Results in CSV
Refer to the following research papers
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The objective of this study is to investigate the prognostic value of deep learning (DL)-driven CT fibrosis quantification in idiopathic pulmonary fibrosis (IPF) using Aview Lung Texture, by Corelinesoft.
161 patients were evaluated. CT-Norm% and CT-Fib% showed significant correlations with FVC and DLCO.
CT-Norm% and CT-Fib% calculated using chest CT-based deep learning software were independent prognostic factors for overall survival in IPF.
Nam JG, Choi Y, Lee SM, Yoon SH, Goo JM, Kim H. Prognostic value of deep learning-based fibrosis quantification on chest CT in idiopathic pulmonary fibrosis. Eur Radiol. 2023 May;33(5):3144-3155.
To investigate whether a proposed content-based image retrieval (CBIR) of similar chest CT images by using deep learning can aid in the diagnosis of ILD by readers with different levels of experience.
A total of 288 patients with four disease classes were included in the study to be analyzed by the Aview Lung Texture, by Corelinesoft.
After applying content-based image retrieval (CBIR), the overall diagnostic accuracy improved in all readers; before CBIR, 46.1%, after CBIR 60.9%.
The proposed content-based image retrieval system for chest CT images with deep learning improved the diagnostic accuracy of interstitial lung disease and interreader agreement in readers with different levels of experience.
Choe J, Hwang HJ, Seo JB, Lee SM, Yun J, Kim MJ, Jeong J, Lee Y, Jin K, Park R, Kim J, Jeon H, Kim N, Yi J, Yu D, Kim B. Content-based Image Retrieval by Using Deep Learning for Interstitial Lung Disease Diagnosis by Chest CT. Radiology. 2022 Jan;302(1):187-197.