Table 2.

Summary of Data Extracted From Selected Studies^a

S.No	Author, Year	Country	Age Range, y	Sample Size	CVM Method Used	Inputs	Reference Standards/Comparisons	Outcome
1	Kim et al., 202126	Korea	6–18	Training: 600 images Testing: 120 images	Baccetti et al.	Images of lateral cephalograms equally distributed across stages	Two specialists	The combination of the CNN with a region-of-interest detector and segmentor module was significantly more accurate (62.5%) than without them.
2	Seo et al., 202127	Korea	6–19	600 lateral cephalograms	Baccetti and Franchi	Cropped images of lateral cephalograms equally distributed across stages displaying the inferior border of C2 to C4	One radiologist	A pretrained network, Inception-ResNet-v2, had relatively high accuracy of 0.941 ± 0.018 when adapted. It also had the highest recall and precision scores among all pretrained models tested.
3	Amasya et al., 202028	Turkey	10–30	72 images	Baccetti and Franchi	Manually labeled image data set with 54 features and ratios with equal distribution across stages	Three dentomaxillofacial radiologists and an orthodontist	Interobserver agreement between researchers and the ANN model was substantial to almost perfect (wκ = 0.76–0.92). Percentage agreements between the ANN model and each researcher were 59.7%, 50%, 62.5%, and 61.1%.
4	Amasya et al., 202029	Turkey	10–30	647 images (498 for training and 149 for testing)	Baccetti and Franchi	Manually labeled image data set with 54 features and results of the evaluation by a clinical decision support system	Expert visual evaluation	Percentage agreement between the model and the visual analysis of the researcher was 86.93%, which was the highest among all models tested.
5	Kök et al., 202030	Turkey	8–17	419 individuals	Hassel and Farman	Measurements used in different combinations for seven neural networks	Human observer's classification	Highest classification accuracy was obtained from the model that used all 32 measurements and age as inputs. The overall accuracy was 94.2% for this model on the test data set.
6	Kök et al., 202031	Turkey	8–17	360 individuals	—	Measurements on the second, third, fourth, and fifth vertebrae used in different combinations as inputs for four models	Human observer's classification	Highest accuracy obtained with one of the neural networks was 0.95 when the training and test data were split into a ratio of 70%:30%.
7	Kök et al., 201932	Turkey	8–17	300 individuals	Hassel and Farman	Linear measurements performed on second, third, and fourth cervical vertebrae	Orthodontist	The neural network model had the second highest accuracy values for determining individual stages, except the fifth stage second-highest accuracy values (93%, 89.7%, 68.8%, 55.6%, 47.4%, and 78%) but was the most stable among all algorithms tested.
8	Makaremi et al., 201933	France	Not mentioned	1870 cephalograms	Baccetti and Franchi	Cropped images without filters and cropped images processed with mean, median, and entropy filter	Human observer	The pretrained models were not as effective as the neural network made by the researchers. The accuracy of the neural network did not exceed 90% on test images. The accuracy improved with more images and preprocessing with the entropy filter.

^aANN indicates artificial neural network; CNN, convolutional neural network.