Table 4. Main features of surveyed studies using machine learning algorithms in forensic dentistry.

Dentistry field	Application	Data set	Machine learning algorithms	Performance
Forensic dentistry	Classification of tooth types on dental CBCTa images for forensic identification purposes Miki et al. [80] (2017)	52 CBCT image	CNNsb	Accuracy (augmented training data): 88.8%
	Use of automated learning techniques for predicting mandibular morphology in skeletal class I, II and III Niño-Sandoval et al. [84] (2017)	229 lateral cephalograms (95 women and 134 men); age range 18-25 years	ANNsc, SVRd	ICCe,: ANNs, 0.84 - 0.99; SVR, two coefficients above 0.7
	Age estimation in children and adolescents for forensic purposes Štepanovský et al. [81] (2017)	976 orthopantomographs (662 males, 314 females) of children and adolescents; age range 2.7-20.5 years	SVMf, ANNs, k-NNg, K-Star, Regression tree, M5P Tree, Random forest	MAEh: M5P tree, SVM: under 0.7 years for both males and females
	Develop an automated method for dental age estimation based on lower third molars stage on panoramic radiographs (pilot study) De Tobel et al. [82] (2019)	400 panoramic radiographs	CNNs	Mean accuracy: 0.51, MDi: 0.6 stages, kappa: 0.82
	Automatic human identification from panoramic dental radiographs Fan et al. [83] (2020)	15369 panoramic dental radiographs from 6300 individuals	CNNs	Accuracy: 85.16% and 97.74%
	Automated estimation of tooth age from lower left third molar development stages assessed on panoramic radiographs Merdietio et al. [48] (2020)	400 panoramic radiographs	CNNs	Accuracy: 0.61, MD: 0.53 stages, kappa: 0.84

^aCBCT: Cone beam computed tomography;

^bCNNs: Convolutional neural networks;

^cANNs: Artificial neural networks;

^dSVR: Support vector regression;

^eICC: Intraclass correlation coefficient;

^fSVM: Support vector machine;

^gk-NN: k-nearest neighbors;

^hMAE: Mean absolute error;

ⁱMD: Mean absolute difference.