Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis 'Expanding Horizons in Radiology'

Autor: Gaetano Rea, Nicola Sverzellati, Marialuisa Bocchino, Roberta Lieto, Gianluca Milanese, Michele D’Alto, Giorgio Bocchini, Mauro Maniscalco, Tullio Valente, Giacomo Sica
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Diagnostics, Vol 13, Iss 14, p 2333 (2023)
Druh dokumentu: article
ISSN: 2075-4418
DOI: 10.3390/diagnostics13142333
Popis: 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.
Databáze: Directory of Open Access Journals
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