Application of machine learning in oral and maxillofacial surgery

Table 1 Machine learning applications in oral and maxillofacial surgery

Ref.	Applications	Purpose	Method
[23]	Maxillofacial cystic lesions and benign tumors	Accurate diagnosis	A support vector machine and bagging with logistic regression
[24]			Integration of graph-based random walks segmentation and machine learning-based boosted classification algorithms
[26]			Deep convolution neural network
[27]			Deep transfer learning
[28]			Convolution neural work You OnlyLook Once v2’s
[25]		Early detection	Deep learning
[30]	Maxillofacial malignant tumors	Early diagnosis	Deep artificial neural network
[31]			Deep learning (DenseNet121 and faster R-Convolution neural work)
[29,32]			Regression-based partitioned convolution neural network
[46]			Deep learning
[47]			Machine learning
[48]		Early detection	Convolution neural network
[49]			End-to-end deep deconvolutional neural network
[44]			Deep learning
[33]		Prognosis estimation	Minimum-redundancy maximum-relevance algorithm
[34-39]			Deep learning
[40-42]			Machine learning
[43]		Treatment complication evaluation	Convolution neural network
[50]			Random forest
[51]	Maxillofacial bone defect reconstruction	Missing bone prediction and facia symmetry evaluation	Iterative closest point
[52]		Midline symmetry plane identification	Convolution neural network
[57]	Orthognathic surgery	Surgery necessity evaluation	Deep learning
[58]		Facial symmetry assessment	Convolution neural network
[59]			Machine learning
[60]		Diagnosis	Machine learning
[61]		Facial appearance and attractiveness evaluation	Convolution neural network
[63]	Dental implant	Implant planning designing	Deep learning
[70]		Implant planning optimizing	Artificial neural network
[64]		Prognosis estimation	Machine learning
[65]		Detection and classification of fractured dental implant	Deep convolution neural network
[66]		Complicationprediction	Machine learning
[67-69]		Implant type recognition	Machine learning