<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The article aims to explore how a Complex Network (CN) computer-aided technique targeted for interstitial lung disease (ILD) approach can enhance the work of clinicians and if a CN-based computer-aided diagnosis can provide new data to help manage ILDs more successfully. The CN technique is used to evaluate the progression of the disease by analyzing relevant axial HRCT slices and dynamic CN evaluation using the relative speed for each layer. The article presents the results from a study of 65 patients with interstitial lung disease (ILD), comprising 18 females with a mean age of 59.35 years (ranging from 34 to 76). The initial clinical diagnosis was idiopathic pulmonary fibrosis (IPF) in 28 patients (43.07%), Non-Specific Interstitial Pneumonia (NSIP) in 11 patients, and other ILDs in the remaining patients. Each CT scan fulfilled the criteria for high-resolution CT with constant characteristics across the group. All patients underwent imagistic follow-up for at least 11 months, and additional data were provided for each investigation. The cohort was chosen based on concordant lung function decline and imaging evolution decline. The article concludes that the complex network approach provides both a qualitative visual map and quantitative metrics to enhance ILD diagnosis and progression tracking. The results suggest that a CN-based computer-aided diagnosis can provide new required data to manage ILDs more effectively. This approach may enable clinicians to make more precise conclusions regarding the structure of the analyzed lung area, which can help tailor disease management strategies to individual patient profiles.</p>
</abstract>

The article aims to explore how a Complex Network (CN) computer-aided technique targeted for interstitial lung disease (ILD) approach can enhance the work of clinicians and if a CN-based computer-aided diagnosis can provide new data to help manage ILDs more successfully. The CN technique is used to evaluate the progression of the disease by analyzing relevant axial HRCT slices and dynamic CN evaluation using the relative speed for each layer. The article presents the results from a study of 65 patients with interstitial lung disease (ILD), comprising 18 females with a mean age of 59.35 years (ranging from 34 to 76). The initial clinical diagnosis was idiopathic pulmonary fibrosis (IPF) in 28 patients (43.07%), Non-Specific Interstitial Pneumonia (NSIP) in 11 patients, and other ILDs in the remaining patients. Each CT scan fulfilled the criteria for high-resolution CT with constant characteristics across the group. All patients underwent imagistic follow-up for at least 11 months, and additional data were provided for each investigation. The cohort was chosen based on concordant lung function decline and imaging evolution decline. The article concludes that the complex network approach provides both a qualitative visual map and quantitative metrics to enhance ILD diagnosis and progression tracking. The results suggest that a CN-based computer-aided diagnosis can provide new required data to manage ILDs more effectively. This approach may enable clinicians to make more precise conclusions regarding the structure of the analyzed lung area, which can help tailor disease management strategies to individual patient profiles.

Interstitial lung diseases computer-aided imaging diagnosis, using complex networks

‘Victor Babes’ University of Medicine and Pharmacy

Department of Computer and Information Technology, Automation and Computers Faculty

“Politehnica” University of Timișoara

Department of Radiology and Medical Imaging

‘Victor Babes’ University of Medicine and Pharmacy Timisoara

Pneumologia

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The article aims to explore how a Complex Network (CN) computer-aided technique targeted for interstitial lung disease (ILD) approach can enhance the work of...

Interstitial lung diseases computer-aided imaging diagnosis, using complex networks

Publié en ligne: 10 juin 2024

Pages: 48 - 58

DOI: https://doi.org/10.2478/pneum-2024-0008

Mots clés
interstitial lung disease, imaging, complex network algorithm, HRCT computer-aided diagnosis, progression

© 2023 Trușculescu Adriana et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Interstitial lung diseases computer-aided imaging diagnosis, using complex networks

Publié en ligne: 10 juin 2024

Pages: 48 - 58

DOI: https://doi.org/10.2478/pneum-2024-0008

Mots clésinterstitial lung disease, imaging, complex network algorithm, HRCT computer-aided diagnosis, progression

© 2023 Trușculescu Adriana et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
interstitial lung disease, imaging, complex network algorithm, HRCT computer-aided diagnosis, progression