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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2306.03025 (eess)
[Submitted on 5 Jun 2023 (v1), last revised 6 Jun 2023 (this version, v2)]

Title:AI Techniques for Cone Beam Computed Tomography in Dentistry: Trends and Practices

Authors:Saba Sarwar, Suraiya Jabin
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Abstract: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.
Comments: Recent Advances in Electrical, Electronics & Digital Healthcare Technologies REEDCON 2023
Subjects: Image and Video Processing (eess.IV); Machine Learning (cs.LG)
ACM classes: I.5.1; I.4.6; I.4.7; I.4.8
Cite as: arXiv:2306.03025 [eess.IV]
  (or arXiv:2306.03025v2 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2306.03025

Submission history

From: Saba Sarwar [view email]
[v1] Mon, 5 Jun 2023 16:45:39 UTC (1,747 KB)
[v2] Tue, 6 Jun 2023 19:11:24 UTC (1,747 KB)
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