Table 1.

Summary of studies examining the use of artificial intelligence in dental diagnosis.

Study	Algorithm Used	Study Factor	Modality	Number of Input Data	Performance	Comparison	Outcome
Lee J et al. (2018) [27]	CNN	Dental caries	Periapical radiographs	600	Mean AUC—0.890	4 Dentists	Deep CNN showed a considerably good performance in detecting dental caries in periapical radiographs.
Casalegno et al. (2019) [29]	CNN	Dental caries	Near-infrared transillumination imaging	217	ROC of 83.6% for occlusal caries; ROC of 84.6% for proximal caries	Dentists with clinical experience	CNN showed increased speed and accuracy in detecting dental caries
Cantu et al. (2019) [30]	CNN	Dental caries	Bitewing radiographs	141	Accuracy 0.80; sensitivity 0.75%; specificity 0.83%;	4 experienced dentists	AI model was more accurate than dentists
Radke et al. (2003) [45]	ANN	Disk displacement	Frontal plane jaw recordings from chewing	68	Accuracy 86.8%, specificity 100%, sensitivity 91.8%	None	The proposed model has an acceptable level of error and an excellent cost/benefit ratio.
Park YH et al. (2021) [31]	ML	Early childhood caries	Demographic details, oral hygiene management details, maternal details	4195	AUROC between 0.774 and 0.785	Traditional regression model	Both ML-based and traditional regression models showed favorable performance and can be used as a supporting tool.
Kuwana et al. (2021) [39]	CNN	Maxillary sinus lesions	Panoramic radiographs	1174	Diagnostic accuracy, sensitivity, and specificity were 90–91%, 81–85% and 91–96% for maxillary sinusitis and 97–100%, 80–100% and 100% for maxillary sinus cysts.	None	The proposed deep learning model can be reliably used for detecting the maxillary sinuses and identifying lesions in them.
Murata et al. (2018) [37]	CNN	Maxillary sinusitis	Panoramic radiographs	120	Accuracy 87.5%; sensitivity 86.7%; specificity 88.3%	2 experienced radiologists, 2 dental residents	The AI model can be a supporting tool for inexperienced dentists
Kim et al. (2019) [38]	CNN	Maxillary sinusitis	Water’s view radiographs	200	AUC of 0.93 for temporal; AUC of 0.88 for geographic external	5 radiologists	the AI-based model showed statistically higher performance than radiologists.
Hung KF et al. (2022) [40]	CNN	maxillary sinusitis	Cone-beam computed tomography	890	AUC for detection of mucosal thickening and mucous retention cyst was 0.91 and 0.84 in low dose, and 0.89 and 0.93 for high dose	None	The proposed model can accurately detect mucosal thickening and mucous retention cysts in both low and high-dose protocol CBCT scans.
Danks et al. (2021) [34]	DNN symmetric hourglass architecture	Periodontal bone loss	Periapical radiographs	340	Percentage Correct Keypoints of 83.3% across all root morphologies	Asymmetric hourglass architecture, Resnet	The proposed system showed promising capability in localizing landmarks and periodontal bone loss and performed 1.7% better than the next best architecture.
Chang et al. (2020) [36]	CNN	Periodontal bone loss	Panoramic radiographs	340	Pixel accuracy of 0.93; Jaccard index of 0.92; dice coefficient values of 0.88 for localization of periodontal bone.	None	The proposed model showed high accuracy and excellent reliability in the detection of periodontal bone loss and classification of periodontitis
Ozden et al. (2015) [18]	ANN	Periodontal disease	Risk factors, periodontal data, and radiographic bone loss	150	Performance of SVM & DT was 98%; ANN was 46%	SVM &DT	SVM and DT showed good performance in the classification of periodontal disease while ANN had the worst performance
Devito et al. (2008) [26]	ANN	Proximal caries	Bitewing radiograph	160	ROC curve area of 0.884	25 examiners	ANN could improve the performance of diagnosing proximal caries.
Dar-Odeh et al. (2010) [20]	ANN	Recurrent aphthous ulcers	Predisposing factor and RAU status	96	Accuracy of prediction for network 3 & 8 is 90%; 4,6 & 9 is 80%; 1& 7 is 70%; 2 & 5 is 60%	None	the ANN model seemed to use gender, hematologic and mycologic data, tooth brushing, fruit, and vegetable consumption for the prediction of RAU.
Hung M et al. (2019) [28]	CNN	Root caries	Data set	5135	Accuracy 97.1%; Precision 95.1%; sensitivity 99.6%; specificity 94.3%	Trained medical personnel	Shows good performance and can be clinically implemented.
Iwasaki et al. (2015) [47]	BBN	Temporomandibular disorders	Magnetic resonance imaging	590	Of the 11 BBN algorithms used path conditions using resubstitution validation and 10—fold cross-validation showed an accuracy of >99%	necessary path condition, path condition, greedy search-and-score with Bayesian information criterion, Chow-Liu tree, Rebane-Pearl poly tree, tree augmented naïve Bayes model, maximum log-likelihood, Akaike information criterion, minimum description length, K2 and C4.5	The proposed model can be used to predict the prognosis of TMDs.
Orhan et al. (2021) [49]	ML	Temporomandibular disorders	Magnetic resonance imaging	214	The performance accuracy for condylar changes and disk displacement are 0.77 and 0.74	logistic regression (LR), random forest (RF), decision tree (DT), k-nearest neighbors (KNN), XGBoost, and support vector machine (SVM)	The proposed model using KNN and RF was found to be optimal for predicting TMJ pathologies
Diniz de lima et al. (2021) [50]	ML	Temporomandibular disorders	Infrared thermography	74	Semantic and radiomic-semantic associated ML feature extraction methods and MLP classifier showed statistically good performance in detecting TMDs	KNN, SVM, MLP	ML model associated with infrared thermography can be used for the detection of TMJ pathologies
Bas B et al. (2012) [46]	ANN	TMJ internal derangements	Clinical symptoms and diagnoses	219	Sensitivity and specificity for unilateral and anterior disk displacement with and without reduction were 80% & 95% and 69% & 91%; for bilateral and anterior disk displacement with and without reduction were 37% &100% and 100% & 89% respectively.	Experienced surgeon	The developed model can be used as a supportive diagnostic tool for the diagnoses of subtypes of TMJ internal derangements
Choi et al. (2021) [48]	CNN	TMJ osteoarthritis	Panoramic radiographs	1189	Accuracy of 0.78, the sensitivity of 0.73, and specificity of 0.82	Oral and maxillofacial radiologist	The developed model showed performance equivalent to experts and can be used in general practices where OMFR experts or CT is n
Fukuda et al. (2019) [33]	CNN	Vertical root fracture	Panoramic radiograph	60	The precision of 0.93; Recall of 0.75	2 Radiologists and 1 Endodontist	The CNN model was a promising supportive tool for the detection of vertical root fracture.