Abstract:

Accurately predicting the difficulty of wisdom tooth extraction is paramount in oral surgery, guiding treatment planning and ensuring patient safety. This study focuses on developing a predictive model for classifying the difficulty of wisdom tooth extraction using advanced deep learning techniques. Leveraging the insights from previous research and clinical exper- tise, we integrate Cone Beam Computed Tomography (CBCT) imaging and deep learning methodologies to automate the assessment of extraction complexity. Through a series of experimental stages, we refine our model by evaluating different annotation techniques, incorporation of edge information, feature fusion methods, backbone architectures, and projection-based classification approaches. Our findings demonstrate the efficacy of incorpo- rating segmented data from CBCT imaging, utilizing Principal Component Analysis (PCA) for feature extraction, and optimizing projection-based classification methods. By automating the identification of tooth positions and surrounding structures, our model provides valuable decision-making support to clinicians, enhancing surgical planning and risk assessment. This interdisciplinary approach to predicting wisdom tooth extraction difficulty represents a significant advancement in oral surgery, promising to improve surgical outcomes and patient care in clinical practice.