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3D bone segmentation and missing tooth prediction for implants. #DentalAI

Deep learning-based approach for 3D bone segmentation and prediction of missing tooth region for dental implant planning

Artificial Intelligence (AI) is being utilized in dentistry for disease detection, classification, and organ and lesion segmentation. Most applications involve 2D radiographs, with limited studies using 3D radiographs like CBCT. AI advancements in dentistry can benefit dentists by providing computer-aided systems for 3D imaging procedures. Treatment planning in dentistry requires accurate diagnosis, and AI models can aid in personalized treatment strategies. Dental segmentation faces challenges due to intricate structures, limited contrast, and the presence of noises in images. Dental implant planning heavily relies on radiographic imaging, with 3D imaging equipment being crucial for detailed planning. Various studies have explored the use of deep learning models for dental implant planning, tooth segmentation, and missing tooth bone identification.

Studies have shown promising results in using AI for dental implant planning, tooth segmentation, and identifying missing tooth bone. Deep learning models have been developed to accurately predict optimal implant positions, detect missing tooth regions, and localize radiographic markers for implant planning. While these studies provide valuable insights, there are limitations in the accuracy and reliability of the models, as well as the software tools used for segmentation. Future improvements are needed to enhance the accuracy and efficiency of AI models in dental practices.

Overall, the research highlights the potential of AI in enhancing dental implant planning and improving patient outcomes. Further developments in AI technology are expected to revolutionize dental practices by providing real-time guidance during surgical procedures and integrating technologies like augmented reality for enhanced implant planning. Addressing limitations in accuracy, reliability, and efficiency will be crucial for the successful implementation of AI in dentistry.

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Source link: https://www.nature.com/articles/s41598-024-64609-0

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