  Cone-beam computed tomography (CBCT) is a popular imaging modality in
dentistry for diagnosing and planning treatment for a variety of oral diseases
with the ability to produce detailed, three-dimensional images of the teeth,
jawbones, and surrounding structures. CBCT imaging has emerged as an essential
diagnostic tool in dentistry. CBCT imaging has seen significant improvements in
terms of its diagnostic value, as well as its accuracy and efficiency, with the
most recent development of artificial intelligence (AI) techniques. This paper
reviews recent AI trends and practices in dental CBCT imaging. AI has been used
for lesion detection, malocclusion classification, measurement of buccal bone
thickness, and classification and segmentation of teeth, alveolar bones,
mandibles, landmarks, contours, and pharyngeal airways using CBCT images.
Mainly machine learning algorithms, deep learning algorithms, and
super-resolution techniques are used for these tasks. This review focuses on
the potential of AI techniques to transform CBCT imaging in dentistry, which
would improve both diagnosis and treatment planning. Finally, we discuss the
challenges and limitations of artificial intelligence in dentistry and CBCT
imaging.
