Computational system for identifying sinusitis in Computed Tomography (CT) scans
DOI:
https://doi.org/10.33448/rsd-v14i9.49476Keywords:
Computed tomography, Sinusitis, Artificial inteligence, Imaging.Abstract
Introduction: Sinusitis is an inflammation of the paranasal sinuses, with high incidence and potential complications when associated with other respiratory diseases. During the Covid-19 pandemic, case numbers decreased but lethality increased. Pollution, especially in industrial areas, exacerbates the incidence. Objective: To develop advanced imaging techniques to improve sinusitis diagnosis through the analysis of computed tomography scans. Methods: Application of artificial intelligence, particularly Machine Learning, and object detection techniques to identify structural changes in axial computed tomography images of the paranasal sinuses. Expected Results: Faster and more accurate diagnosis, contributing to the optimization of the diagnostic process and reduction of associated clinical complications. Conclusion: AI-based methods represent an important tool to enhance sinusitis diagnosis, enabling early interventions and better clinical management.
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