Title: Analysis Of Dental Images Using Artificial Intelligence: Radiography, Cbct, And Ai-Based Diagnostics
Authors: Ergashev Bekzod
Volume: 9
Issue: 7
Pages: 49-51
Publication Date: 2025/07/28
Abstract:
This article explores the integration of artificial intelligence (AI) in dental imaging diagnostics, focusing on its applications in the interpretation of radiographs, Cone Beam Computed Tomography (CBCT), and advanced image-based diagnostics. With the increasing demand for precision and early detection in dentistry, AI offers valuable tools that enhance image analysis, reduce diagnostic errors, and support clinical decision-making. The study begins by reviewing conventional imaging techniques and their limitations, followed by an in-depth analysis of how machine learning, convolutional neural networks (CNNs), and deep learning algorithms are transforming image interpretation. Materials and methods include a comparative evaluation of AI-enhanced diagnostic tools versus traditional assessments, highlighting accuracy, sensitivity, and clinical efficiency. Results indicate a significant improvement in detecting dental caries, periapical lesions, root fractures, and anatomical anomalies with AI-supported imaging, especially using CBCT datasets. The discussion addresses the ethical implications, integration challenges, and future potential of AI in dental education and clinical workflows. Conclusions emphasize that AI, when used as an adjunct to professional expertise, has the potential to standardize diagnostics and optimize treatment outcomes. The study contributes to ongoing innovation in digital dentistry and supports broader AI adoption for clinical excellence.