Identification of postacute COVID-19 patterns in tomography using artificial intelligence
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1331Keywords:
Multidetector Computed Tomography, Artificial Intelligence, Postacute COVID-19 SyndromeAbstract
Objective: Develop AI models capable of recognizing post-COVID lung patterns in computed tomography scans. Method: Radiologists analyzed 87 CT scans to establish tomographic patterns for training and testing deep learning models. The best model was then selected to read eight full scans. Results: The chosen model showed an average accuracy of 92.21% in detecting post-COVID patterns.
Conclusion: Although the sample size was limited, testing with image sets and full scans showed promising results. The sample used in the study reflects the epidemiological profile found in the literature.
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