
This study evaluates the effectiveness of a case-based reasoning (CBR) system for predicting definitive obturator prosthesis designs in maxillectomy patients. Using data from 209 cases, the CBR system matched new cases to historical ones based on attributes like Aramany class and defect extension. In tests with 33 cases, the system demonstrated high precision (0.8) and strong correlation (ρ = 0.84) between confidence scores and clinician validation, suggesting its potential to reduce workload, streamline design, and improve patient engagement.
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