Table 6. Main features of surveyed studies using machine learning algorithms in prosthetics, conservative dentistry and implantology.

Dentistry field	Application	Data set	Machine learning algorithms	Performance
Prosthetics, conservative dentistry and implantology	Prediction of facial deformation after complete denture prosthesis Cheng et al. [94] (2015)	Preoperative and postoperative 3D face scan of 48 patients ANNsa	ANNs	Average error rate: 22.49%
	Classification of specific characteristics of teeth based on 3D scan data Raith et al. [95] (2017)	129 data sets, consisting of 69 upper jaw virtual models and 60 lower jaw virtual models	ANNs	Success rate: 93.3% and 93.5%
	Prediction of patient mean peri-implant bone level Papantonopoulos et al. [101] (2017)	72 implant-treated patients with 237 implants (mean 7.4±3.5 years of function)	SVMb	Sec: 55% , Spd: 91%, particle swarm optimization-SVM: Se: 62%, Sp: 85%
	Improve the computer color matching system (CCM) to measure a tooth color quantitatively and offer a porcelain recipe with instructions Wei et al. [96] (2018)	43 metal-ceramic specimen	ANNs	MDe: 1.89 ± 0.75; P<0.01
	Predicting the debonding probability of a computer-aided design/computer-aided manufacturing composite resin crowns with 3D stereolithography models of a die scanned from patients Yamaguchi et al. [98] (2019)	8640 images	CNNsf	Accuracy: 98.5%, precision: 97.0%, recall: 100%, F-measure: 0.985, AUCg: 0.998
	Automatic detection and classification of various dental restorations on panoramic radiographs Abdalla-Aslan et al. [97] (2020)	738 dental restorations in 83 anonymized panoramic images	SVM	Overall accuracy: 93.6%
	Construct a clinical decision support model to predict tooth extraction therapy in clinical situations by using electronic dental records Cui et al. [99] (2020)	4135 unidentified electronic dental records from 3559 patients	Regression tree algorithm, AdaBoosth, GBDTi, Light GBMj, and XGBoostk	Accuracy, AUCl: regression tree algorithm: 0.953, 0.919; AdaBoost: 95;5, 0.970; GBDT: 0.957, 0.970; LightGBM: 0.957, 0.970; XGBoost: 0.962,0.970
	Identification of the brand and model of a dental implant from radiographs Hadj Saïd et al. [100] (2020)	1206 dental implant radiographic images	CNNs	Accuracy: 93.8% (95% CIm: 87.2-99.4%), Se: 93.5% (95% CI: 84.2-99.3%), Sp: 94.2% (95% CI: 83.5-99.4%), PPVn: 92% (95% CI: 83.9-97.2%), NPVo: 91.5% (95% CI: 80.2-97.1%), AUC: 0.918 (95% CI: 0.826-0.973)
	Identification and classification of dental implant systems, using panoramic and periapical radiographs (a pilot study) Lee et al. [45] (2020)	5390 panoramic and 5380 periapical radiographic images from 3 types of dental implant systems	CNNs	AUC: 0.971, 95% CI: 0.963-0.978

^aANN: Artificial neural networks;

^bSVM: Support vector machine;

^cSe: Sensibility;

^dSp: Specificity;

^eMD: Mean absolute difference;

^fCNN: Convolutional neural networks;

^gAUC: Area under receiver operating;

^hAdaBoost: Adaptive boosting;

ⁱGBDT: Gradient boosting decision tree;

^jLight GBM: Light gradient boosting machine;

^kXGBoost: Extreme gradient boosting;

^lAUC: Area under receiver operating curve;

^mCI: Confidence interval;

ⁿPPV: Positive predictive value;

^oNPV: Negative predictive value.