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Artificial intelligence models for clinical usage in dentistry with a focus on dentomaxillofacial CBCT: a systematic review

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This study aimed at performing a systematic review of the literature on the application of artificial intelligence (AI) in dental and maxillofacial cone beam computed tomography (CBCT) and providing comprehensive descriptions of current technical innovations to assist future researchers and dental professionals. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA) Statement was followed. The study’s protocol was prospectively registered. Following databases were searched, based on MeSH and Emtree terms: PubMed/MEDLINE, Embase and Web of Science. The search strategy enrolled 1473 articles. 59 publications were included, which assessed the use of AI on CBCT images in dentistry. According to the PROBAST guidelines for study design, seven papers reported only external validation and 11 reported both model building and validation on an external dataset. 40 studies focused exclusively on model development. The AI models employed mainly used deep learning models (42 studies), while other 17 papers used conventional approaches, such as statistical-shape and active shape models, and traditional machine learning methods, such as thresholding-based methods, support vector machines, k-nearest neighbors, decision trees, and random forests. Supervised or semi-supervised learning was utilized in the majority (96.62%) of studies, and unsupervised learning was used in two (3.38%). 52 publications included studies had a high risk of bias (ROB), two papers had a low ROB, and four papers had an unclear rating. Applications based on AI have the potential to improve oral healthcare quality, promote personalized, predictive, preventative, and participatory dentistry, and expedite dental procedures.

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Artificial intelligence


As low as diagnostically acceptable being indication-oriented and patient-specific




Average symmetrical surface distance


Active shape model


Cone-beam computed tomography


Convolutional neural network


Dice similarity coefficient


Deep learning


Generative adversarial network


Hausdorff distance


Inferior alveolar nerve


Intra-oral scan


Intersection over union


Iterative closest point


K-nearest neighbors


Mean error


Mandibular canal


Machine learning


Mean radial error


Mean surface distance


Neural network


Negative predictive value


Leave one out cross-validation




Positive predictive value


Prediction risk of bias assessment tool


Rectified linear unit


Root mean squared error


Recurrent neural network


Risk of bias




Surface error




Support vector machine


Temporomandibular joint


Temporomandibular disorder


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Mureșanu, S., Almășan, O., Hedeșiu, M. et al. Artificial intelligence models for clinical usage in dentistry with a focus on dentomaxillofacial CBCT: a systematic review. Oral Radiol 39, 18–40 (2023).

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