Table 2. Main features of surveyed studies using machine learning algorithms in periodontics.

Dentistry field	Application	Data set	Machine learning algorithms	Performance
Periodontics	Diagnosis and classification of periodontitis based on the molecular profile Kebschull et al. [63] (2013)	Gene expression profiles of the entire genome from 310 biopsies of “healthy” or “sick” gingival tissue	SVMa	AUCb: 0.63-0.99
	Diagnosis of aggressive periodontitis and chronic periodontitis trained by immunologic parameters Papantonopoulos et al. [42] (2014)	4 distinct samples of patient with periodontitis advanced obtained from previous studies	ANNsc	Accuracy: 90-98%
	Diagnosis of periodontal diseases (preliminary study) Ozden et al. [64] (2015)	150 patients	SVM, DTd, and ANNs	Accuracy: SVM, 98%; DT, 98%; ANNs, 46%
	Classification of generalized chronic periodontitis, generalized aggressive periodontitis and periodontal health from bacterial profiles Feres et al. [65] (2018)	3915 subgingival biofilm samples from 435 patients SVM	SVM	See: 86%, Spf:79%, AUC: 0.83
	Detection and classification of the periodontal bone loss of each individual tooth Chang et al. [66] (2020)	340 panoramic radiographs	CNNsg	ICCh: 0.91; P<0.01
	Periodontitis risk assessment Shimpi et al. [67] (2020)	11048 patients (4766 positive cases and 6282 controls)	Naïve Bayes, Logistic Regression, SVM, ANNs, and DT	Accuracy: Naïve Bayes, 0.801 (0.791-0.811); Logistic Regression, 0.768 (0.778-0.795); SVM, 0.790 (0.780-0.799); ANNs, 0.841 (0.833-0.849); DT, 0.901 (0.892-0.908)

^aSVM: Support vector machine;

^bAUC: Area under receiver operating;

^cANNs: Artificial neural networks;

^dDT: Decision tree; Se^e: Sensibility; Sp

^f:Specificity;

^gCNN: Convolutional neural networks;

^hICC: Intraclass correlation coefficient.