Artificial-intelligence in tomography for diagnosis of interstitial lung diseases
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1277Keywords:
Interstitial, Lung Diseases, Tomography, Artificial IntelligenceAbstract
Objective: Analyze the influence of Artificial Intelligence on the pathological diagnosis of Interstitial Lung Diseases (ILDs) through Tomography (CT) using Deep Learning (DL) in an integrative review. Methodology: We utilized English Mesh descriptors for the respective keywords, combined with the boolean operator "AND," on the MEDLINE and PubMed platforms. Results: Out of 36 articles from each database, 8 retrospective cohorts were analyzed, addressing the use of algorithms in quantifying parenchymal lesions, lung volume, image retrieval in databases, and performance comparison between technology and observer in the context of ILD diagnosis in CT scans. Conclusion: DL through algorithms in CT scans shows promise in aiding ILD diagnosis more efficiently, potentially streamlining this process in the future. However, further studies, particularly prospective ones with extensive databases, are necessary for even better results.
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