Enhanced imagistic methodologies augmenting radiological image processing in interstitial lung diseases-Reference-Cited by-同舟云学术

Enhanced imagistic methodologies augmenting radiological image processing in interstitial lung diseases

Published:2023-08-01 Issue:1 Volume:15 Page:146-169
ISSN:2066-7760
Container-title:Acta Universitatis Sapientiae, Informatica
language:en
Short-container-title:

Author:

Palatka József¹,Kovács Levente²^ORCID,Szilágyi László³^ORCID

Affiliation:

1. 1 Doctoral School of Applied Informatics and Applied Mathematics , University Research, Innovation and Service Center, Óbuda University , Budapest , Hungary

2. 2 University Research, Innovation and Service Center, Óbuda University , Budapest , Hungary

3. 3 Computational Intelligence Research Group , Sapientia Hungarian University of Transylvania , Târgu Mureş , Romania , University Research, Innovation and Service Center, Óbuda University , Budapest , Hungary , Department of Mechanical Engineering , University of Canterbury , Christchurch , New Zealand

Abstract

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.

Publisher

Walter de Gruyter GmbH

Link

https://www.sciendo.com/pdf/10.2478/ausi-2023-0011

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