Table 5. Main features of surveyed studies using machine learning algorithms in endodontics and cariology.

Dentistry field	Application	Data set	Machine learning algorithms	Performance
Endodontics	Localization of the lesser apical foramen using X-ray feature extraction procedures and then processing data using an artificial neural network as a decision-making system Saghiri et al. [85] (2012)	50 single-rooted mandibular incisors and second premolars with curvatures <30° in the apical region, extracted for orthodontic or periodontal reasons	ANNsa	Success rate: 93%
	Location of the minor apical foramen using feature extraction procedures from radiographs (a cadaver study) Saghiri et al. [86] (2012)	50 single-rooted teeth from 19 male cadavers; age range 49-73 years	ANNs	Success rate: 96%, CIb: 90.57–101.43; P<0.001
	Diagnosis of vertical root fracture (an ex vivo study) Kositbowornchai et al. [87] (2013)	200 digital radiographic images of extracted human premolar teeth with single root (50 sound and 150 vertical root fractures)	ANNs	Sec: 98%, Spd: 90.5%, accuracy: 95.7%
	Diagnosis of the number of the distal roots of mandibular first molars Hiraiwa et al. [88] (2019)	Panoramic radiographs of 760 mandibular first molars from 400 patients	CNNse	Accuracy:86.9%
	Detection of vertical root fracture on panoramic radiograph using neural network system Fukuda et al. [89] (2019)	300 panoramic images containing a total of 330 teeth with clearly visible fracture lines	CNNs	Recall: 0.75, precision: 0.93, F-measure: 0.83
	Diagnosis of periapical pathosis on CBCTf images Orhan et al. [49] (2020)	Images of 153 periapical lesions obtained from 109 patients	CNNs	Recall: 0.89, precision:0.95, F-measure: 0.93
Cariology	Approximal caries diagnosis using a computer-assisted diagnostic system Araki et al. [26] (2010)	100 approximate surfaces of 50 human teeth extracted (first and second maxillary premolars)	ANNs	AUCg:: Inner half enamel caries, 0.691±0.048; dentine caries, 0.745±0.088; all caries, 0.662±0.061
	Diagnosis of dental caries on periapical radiographs Lee et al. [27] (2018)	3000 periapical radiographic images	CNNs	Premolar and molar model-Se: 81.0 % (74.5-86.1), Sp: 83.0 % (76.5-88.1), accuracy: 82.0% (75.5-87.1), PPVh: 82.7% (76.1-87.9), NPVi: 81.4 (75.0-86.4), AUC: 0.845 (95% CI 0.790-0.901)
	Develop models for identification of root caries risk Hung et al. [28] (2019)	5135 individuals; mean age (±SDj): 46,6 ± 18,1 years	SVMk, XGBoostl, Random forest, k-NNm, LRn	Se, Sp, accuracy, precision, AUC: SVM: 0.996, 0.943, 0.97, 0.951, 0.997; XGBoost: 1.000, 0.889, 0.947, 0.908, 0.987; Random forest:1.000, 0.875, 0.941, 0.947, 0.999; k?NN: 0.971, 0.679, 0.832, 0.769, 0.881; LR: 0.771, 0.711, 0.742, 0.742, 0.818
	Detection of caries lesions on bitewing radiographs images Cantu et al. [29] (2020)	3686 bitewing radiographs	CNNs	Se:0.75, Sp: 0.83, accuracy: 0.80, PPV: 0.70, NPV: 0.86, F-measure: 0.73
	Diagnosis of dental caries on digital periapical radiographs Geetha et al. [30] (2020)	105 images derived from intra-oral digital radiography	BPNNo	Accuracy: 0.971, AUC: 0.987
	Caries risk prediction in geriatric people Liu et al. [31] (2020)	1144 geriatrics; range age 65-74 years	GRNNp	Se: 91.41% (training set), 85.16% (test set); Sp: 72.38% (training set), 70.27% (test set); AUC: 0.777
	Detect caries lesions in near-infrared-light transillumination images Schwendicke et al. [32] (2020)	226 extracted posterior permanent human teeth (113 premolars, 113 molars)	CNNs	AUC: 0.74 (0.66-0.82), Se: 0.59 (0.47-0.70), Sp: 0.76 (0.68-0.84), PPV: 0.63 (0.51-0.74), NPV: 0.73 (0.65-0.80)
	Evaluation of cost effectiveness in detection of proximal caries on bitewing radiographs using AI Schwendicke et al. [90] (2020)	3686 bitewing radiographs	CNNs	Accuracy: 0.80; P<0.05, ICERq: –13.9 euro/year

^aANN: Artificial neural networks;

^bCI: Confidence interval;

^cSe: Sensibility;

^dSp: Specificity;

^eCNN: Convolutional neural networks;

^fCBCT: Cone beam computed tomography;

^gAUC: Area under receiver operating;

^hPPV: Positive predictive value;

ⁱNPV: Negative predictive value;

^jSD: Standard deviation;

^kSVM: Support vector machine;

^lXGBoost: Extreme gradient boosting;

^mk-NN: k-nearest neighbors;

ⁿLR: Logistic regression;

^oBPNN: Back propagation neural networks;

^pGRNN: General regression neural network;

^qICER: Incremental cost-effectiveness ratio.