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Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis “Expanding Horizons in Radiology”
Diffuse lung disorders (DLDs) and interstitial lung diseases (ILDs) are pathological conditions affecting the lung parenchyma and interstitial network. There are approximately 200 different entities within this category. Radiologists play an increasingly important role in diagnosing and monitoring I...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378251/ https://www.ncbi.nlm.nih.gov/pubmed/37510077 http://dx.doi.org/10.3390/diagnostics13142333 |
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author | Rea, Gaetano Sverzellati, Nicola Bocchino, Marialuisa Lieto, Roberta Milanese, Gianluca D’Alto, Michele Bocchini, Giorgio Maniscalco, Mauro Valente, Tullio Sica, Giacomo |
author_facet | Rea, Gaetano Sverzellati, Nicola Bocchino, Marialuisa Lieto, Roberta Milanese, Gianluca D’Alto, Michele Bocchini, Giorgio Maniscalco, Mauro Valente, Tullio Sica, Giacomo |
author_sort | Rea, Gaetano |
collection | PubMed |
description | Diffuse lung disorders (DLDs) and interstitial lung diseases (ILDs) are pathological conditions affecting the lung parenchyma and interstitial network. There are approximately 200 different entities within this category. Radiologists play an increasingly important role in diagnosing and monitoring ILDs, as they can provide non-invasive, rapid, and repeatable assessments using high-resolution computed tomography (HRCT). HRCT offers a detailed view of the lung parenchyma, resembling a low-magnification anatomical preparation from a histological perspective. The intrinsic contrast provided by air in HRCT enables the identification of even the subtlest morphological changes in the lung tissue. By interpreting the findings observed on HRCT, radiologists can make a differential diagnosis and provide a pattern diagnosis in collaboration with the clinical and functional data. The use of quantitative software and artificial intelligence (AI) further enhances the analysis of ILDs, providing an objective and comprehensive evaluation. The integration of “meta-data” such as demographics, laboratory, genomic, metabolomic, and proteomic data through AI could lead to a more comprehensive clinical and instrumental profiling beyond the human eye’s capabilities. |
format | Online Article Text |
id | pubmed-10378251 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103782512023-07-29 Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis “Expanding Horizons in Radiology” Rea, Gaetano Sverzellati, Nicola Bocchino, Marialuisa Lieto, Roberta Milanese, Gianluca D’Alto, Michele Bocchini, Giorgio Maniscalco, Mauro Valente, Tullio Sica, Giacomo Diagnostics (Basel) Review Diffuse lung disorders (DLDs) and interstitial lung diseases (ILDs) are pathological conditions affecting the lung parenchyma and interstitial network. There are approximately 200 different entities within this category. Radiologists play an increasingly important role in diagnosing and monitoring ILDs, as they can provide non-invasive, rapid, and repeatable assessments using high-resolution computed tomography (HRCT). HRCT offers a detailed view of the lung parenchyma, resembling a low-magnification anatomical preparation from a histological perspective. The intrinsic contrast provided by air in HRCT enables the identification of even the subtlest morphological changes in the lung tissue. By interpreting the findings observed on HRCT, radiologists can make a differential diagnosis and provide a pattern diagnosis in collaboration with the clinical and functional data. The use of quantitative software and artificial intelligence (AI) further enhances the analysis of ILDs, providing an objective and comprehensive evaluation. The integration of “meta-data” such as demographics, laboratory, genomic, metabolomic, and proteomic data through AI could lead to a more comprehensive clinical and instrumental profiling beyond the human eye’s capabilities. MDPI 2023-07-10 /pmc/articles/PMC10378251/ /pubmed/37510077 http://dx.doi.org/10.3390/diagnostics13142333 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Rea, Gaetano Sverzellati, Nicola Bocchino, Marialuisa Lieto, Roberta Milanese, Gianluca D’Alto, Michele Bocchini, Giorgio Maniscalco, Mauro Valente, Tullio Sica, Giacomo Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis “Expanding Horizons in Radiology” |
title | Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis “Expanding Horizons in Radiology” |
title_full | Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis “Expanding Horizons in Radiology” |
title_fullStr | Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis “Expanding Horizons in Radiology” |
title_full_unstemmed | Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis “Expanding Horizons in Radiology” |
title_short | Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis “Expanding Horizons in Radiology” |
title_sort | beyond visual interpretation: quantitative analysis and artificial intelligence in interstitial lung disease diagnosis “expanding horizons in radiology” |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378251/ https://www.ncbi.nlm.nih.gov/pubmed/37510077 http://dx.doi.org/10.3390/diagnostics13142333 |
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