Chronic obstructive pulmonary
disease analysis AI solution

Interstitial Lung Abnormalities (ILA)
Analysis Solutions

Interstitial Lung Disease Pattern Analysis Solution

Early diagnosis is crucial in ILD due to its progressive fibrotic nature.
aview Lung Texture utilizes artificial intelligence to automatically quantify fibrous lesions,
enabling precise diagnosis through systematic reports.

DEMO

VIDEO

brochure

aview Lung Texture | Interstitial Lung Disease Pattern Analysis Solution

Conformity Certifications

MFDS

Republic of Korea
CE

Europe
TGA

Australia
HSA

Singapore
ANVISA

Brazil
HC

Canada

Patterns Analysis in Interstitial Lung Disease

Automated analysis of the locations and distribution
of the six patterns observed in Interstitial Lung Disease (ILD).

Consistent performance and reduction in interpretation variances lead to the achievement of high-quality results.

Multi-Layout

Pattern Comparison & Modification

The analyzed pattern can be compared to the original
in real-time and can be easily modified

Delivering Comprehensive Analysis Results

Provides quantitative and intuitive results.

Automatically generated Bull's eye charts and tables are highly comprehensible.

The analysis results are presented in segmented lobes, making the visualized patterns easily perceptible.

Analysis Results in CSV
Report

Publications

Refer to the following research papers

These contents represent summaries of scientific
publications and are unrelated to any form of advertising.

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.

More Publications