Detection of Implant Brands Using Artificial Intelligence and Deep Learning Modeling Based on Orthopantomogram...

This study explores the application of artificial intelligence (AI) and deep learning models, specifically convolutional neural networks (CNNs), to identify dental implant brands using orthopantomogram (OPG) images. Dental implants are crucial in modern dentistry for replacing missing teeth, and accurate identification of implant brands is essential for effective treatment and long-term patient care. Traditional methods of implant identification rely on manual comparison with reference libraries and visual assessment, which are often time-consuming and prone to errors due to the increasing variety of implant brands. AI offers a promising alternative by automating the detection process, thereby improving accuracy and reducing the reliance on human expertise. The study highlights the importance of preprocessing OPG images through techniques such as contrast enhancement and noise reduction to improve the visibility of critical implant features. It also discusses the role of image segmentation in isolating implants from surrounding anatomical structures, which is crucial for accurate analysis. Feature extraction, another key step, involves identifying and isolating unique implant characteristics that distinguish different brands. The integration of AI in clinical practice offers significant advantages, including faster and more reliable implant identification, which can enhance treatment planning and patient outcomes. However, the study also acknowledges the challenges associated with AI, such as the need for large, well-annotated datasets and the ethical concerns related to data privacy and the interpretability of AI models. The research concludes by emphasizing the potential of AI to revolutionize implantology, particularly as AI technologies continue to advance and become more integrated into clinical workflows.