Deep Learning in Dental Radiology

Abstract

The integration of Artificial Intelligence (AI), particularly deep learning, into dental radiology is revolutionizing the way oral diseases are diagnosed and managed. This chapter explores how convolutional neural networks (CNNs) and related deep learning techniques are being applied to interpret dental imaging modalities such as panoramic X-rays, periapical radiographs, and cone-beam computed tomography (CBCT). By leveraging large datasets, AI models can detect caries, periodontal disease, periapical lesions, and other abnormalities with accuracy comparable to or exceeding that of experienced clinicians. The chapter reviews current tools, architectures, and real-world applications, and discusses the benefits and challenges of adopting AI in clinical practice, including ethical concerns, data privacy, and model transparency. Through case studies and comparative evaluations, we demonstrate the transformative potential of AI-powered radiology in making dental diagnostics faster, more consistent, and accessible across varying healthcare settings.

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