Table 3. Main features of surveyed studies using machine learning algorithms in oral medicine and maxillofacial surgery.

Dentistry field	Application	Data set	Machine learning algorithms	Performance
Oral medicine and maxillofacial surgery	Diagnosis and classification of dental periapical cyst lesions and keratocystic odontogenic tumor through decision support system Yilmaz et al. [43] (2017)	50 CBCTa 3D images obtained from 50 patients	k-NNb, Naive Bayes, DTc, Random forest , ANNsd, and SVMe	Best accuracy (SVM) : 96.0%
	Diagnosis and classification of radiolucent lesions in the mandible on panoramic radiographs Ariji et al.[23] (2019)	210 panoramic radiographs images with mandibular radiolucent lesions	CNNsf	Seg: 0.88
	Diagnosis for cases of orthognathic surgery Choi et al. [79] (2019)	316 cases	ANNs	Success rate (surgery/non-surgery diagnosis): 96% ICCh: 0.97- 0.99
	Develop an intelligent automated system to support specialists for differential diagnosis in oral medicine Ehtesham et al. [24] (2019)	500 cases of six principal axes of oral diseases	CBRi	Success rate: 76.9 %
	Predict perioperative blood loss prior to orthognathic surgery Stehrer et al. [78] (2019)	950 patients	Random forest	MDj:7.4 ml, SDk: 172.3 ml, p < 0.001
	Automatic classification of dental artifact status for efficient image veracity checks Welch et al. [51] (2019)	1538 head and neck planning CBCT images	CNNs	AUCl: 0.91±0.01
	Detection and segmentation of the mental foramen Kats et al. [74] (2020)	112 digital panoramic radiographs	CNNs	Precision: 71.13%, recall:68.24%
	Diagnosis of odontogenic cysts and tumors of both jaws on panoramic radiographs Kwon et al. [71] (2020)	1282 panoramic radiographs (350 dentigerous cysts, 302 periapical cysts, 300 odontogenic keratocysts, 230 ameloblastomas, and 100 normal jaws with no disease	CNNs	Se: 88.9%, Spm: 97.2%, accuracy: 95.6%, and AUC: 0.94, (augmented data set)
	Diagnosis of odontogenic cystic lesions Lee et al. [44] (2020)	2126 images, including 1140 (53.6%) panoramic and 986 (46.4%) CBCT images	CNNs	AUC: 0.914, Se: 96.1%, Sp:77.1%
	Develop a method for automated dental segmentation of panoramic dental images for diagnostic purposes Lee et al. [75] (2020)	846 images with tooth annotations from panoramic radiographs	CNNs	F1-score: 0.875, precision: 0.858, recall: 0.893, IoUn:0.877
	Classification of head and neck computed tomography images to detect the presence of dental artifacts that affect the visualization of structures Welch et al. [76] (2020)	1538 computed tomography images	CNNs	AUC: 0.92±0.03
	Classification of maxillary sinus lesions compared to healthy maxillary sinuses Kuwana et al. [77] (2021)	Imaging data for healthy maxillary sinuses (587 sinuses), inflamed maxillary sinuses (416 sinuses), cysts of maxillary sinus regions (171 sinuses)		Accuracy, Se and Sp: , 90-91%, 88-85%, and 91-96% (diagnosis maxillary sinusitis); 97-100%, 80-100%, and 100% (cysts of the maxillary sinus regions)

^aCBCT: Cone beam computed tomography;

^bk-NN: k-nearest neighbors;

^cDT: Decision tree;

^dANNs: Artificial neural networks;

^eSVM: Support vector machine;

^fCNN: Convolutional neural networks;

^gSe: Sensibility;

^hICC: Intraclass correlation;

ⁱCBR: Case-based reasonig;

^jMD: Mean absolute difference;

^kSD: Standard deviation;

^lAUC: Area under receiver operating curve;

^mSp: Specificity;

ⁿIoU: Intersection-over-union.