Optimal implant planning in dentistry requires a precise analysis of the dimensions and quality of mandibular alveolar bone, encompassing both the cortical and cancellous segments. This study introduces an innovative deep learning approach to automate and enhance this critical process. The proposed method utilizes Convolutional Neural Networks (CNN) with instance segmentation techniques to accurately determine bone dimensions such as height, width, area of the jaw bone and generate implant screw from 2D radiographic images. By generating precise masks for the alveolar bone and its constituent sections, the algorithm enables automated measurements, scaling, and quantitative evaluation of bone quality through 2D cortical bone area analysis. By automating the process of implant planning, this study aims to enhance treatment outcomes, reduce planning time, and minimize human error in dental implantology, thereby paving the way for more precise, personalized, and successful dental implant procedures that contribute to advancements in dental surgery and patient care.
Kannan et al. (Sun,) studied this question.
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