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

<p>Interstitial Lung Diseases (ILDs) represent a heterogeneous group of several rare diseases that are di cult to predict, diagnose and monitor. There are no predictive biomarkers for ILDs, clinical signs are similar to the ones for other lung diseases, the radiological features are not easy to recognize, and require manual radiologist review. Data-driven support for ILD prediction, diagnosis and disease-course monitoring are great unmet need. Numerous image processing techniques and computer-aided diagnostic and decision-making support methods have been developed over the recent years. The current review focuses on such solutions, discussing advancements on the fields of Quantitative CT, Complex Networks, and Convolutional Neural Networks.</p>
</abstract>

Interstitial Lung Diseases (ILDs) represent a heterogeneous group of several rare diseases that are di cult to predict, diagnose and monitor. There are no predictive biomarkers for ILDs, clinical signs are similar to the ones for other lung diseases, the radiological features are not easy to recognize, and require manual radiologist review. Data-driven support for ILD prediction, diagnosis and disease-course monitoring are great unmet need. Numerous image processing techniques and computer-aided diagnostic and decision-making support methods have been developed over the recent years. The current review focuses on such solutions, discussing advancements on the fields of Quantitative CT, Complex Networks, and Convolutional Neural Networks.

Enhanced imagistic methodologies augmenting radiological image processing in interstitial lung diseases

University Research, Innovation and Service Center, Óbuda University

Sapientia Hungarian University of Transylvania

Acta Universitatis Sapientiae, Informatica

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

Interstitial Lung Diseases (ILDs) represent a heterogeneous group of several rare diseases that are di cult to predict, diagnose and monitor. There are no...

Enhanced imagistic methodologies augmenting radiological image processing in interstitial lung diseases

Data publikacji: 08 sie 2023

Zakres stron: 146 - 169

Otrzymano: 29 cze 2023

DOI: https://doi.org/10.2478/ausi-2023-0011

Słowa kluczowe
interstitial lung diseases, high-resolution computed tomography, radiography image processing, complex networks, convolutional neural networks, computer-aided diagnostics, deep learning, machine learning

© 2023 József Palatka et al., published by Sciendo

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

Enhanced imagistic methodologies augmenting radiological image processing in interstitial lung diseases

Data publikacji: 08 sie 2023

Zakres stron: 146 - 169

Otrzymano: 29 cze 2023

DOI: https://doi.org/10.2478/ausi-2023-0011

Słowa kluczoweinterstitial lung diseases, high-resolution computed tomography, radiography image processing, complex networks, convolutional neural networks, computer-aided diagnostics, deep learning, machine learning

© 2023 József Palatka et al., published by Sciendo

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

Słowa kluczowe
interstitial lung diseases, high-resolution computed tomography, radiography image processing, complex networks, convolutional neural networks, computer-aided diagnostics, deep learning, machine learning