Smart Decision Support Framework for Precise Diagnosis of Chronic Odontogenic Rhinosinusitis using U-Net Segmentation

The proposed methodology aims to revolutionize the diagnosis of Chronic Odontogenic Rhinosinusitis (CORS) by leveraging advanced artificial intelligence (AI) techniques, with a focus on U-Net segmentation. CORS, characterized by persistent inflammation originating from dental infections affecting paranasal sinuses, presents a complex diagnostic challenge due to its intricate etiology and symptom overlap with conventional rhinosinusitis. This study introduces a Smart Decision Support Framework employing U-Net segmentation, enabling precise and efficient diagnosis. The framework integrates crucial algorithms, Data Preprocessing, U-Net Segmentation Training, and Post-processing forming the backbone of this innovative approach. These algorithms collectively facilitate precise segmentation and diagnosis of Chronic Odontogenic Rhinosinusitis using U-Net segmentation, empowering clinicians to make well-informed diagnostic and treatment decisions.