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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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Abstract

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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Abbreviations

AI:

Artificial intelligence

ALADAIP:

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

AME:

Ameloblastoma

ASD:

Average symmetrical surface distance

ASM:

Active shape model

CBCT:

Cone-beam computed tomography

CNN:

Convolutional neural network

DICE:

Dice similarity coefficient

DL:

Deep learning

GAN:

Generative adversarial network

HD:

Hausdorff distance

IAN:

Inferior alveolar nerve

IOS:

Intra-oral scan

IOU:

Intersection over union

ICP:

Iterative closest point

KNN:

K-nearest neighbors

ME:

Mean error

MC:

Mandibular canal

ML:

Machine learning

MRE:

Mean radial error

MSD:

Mean surface distance

NN:

Neural network

NPV:

Negative predictive value

LOOCV:

Leave one out cross-validation

PA:

Periapical

PPV:

Positive predictive value

PROBAST:

Prediction risk of bias assessment tool

ReLU:

Rectified linear unit

RMSE:

Root mean squared error

RNN:

Recurrent neural network

ROB:

Risk of bias

Se:

Sensitivity

SE:

Surface error

Sp:

Specificity

SVM:

Support vector machine

TMJ:

Temporomandibular joint

TMD:

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). https://doi.org/10.1007/s11282-022-00660-9

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