Table 1 Summary characteristics of the studies

1

Talaat et al

Egypt [12]

Orthodontics

Validation study

Classification

Local

Intraoral photographs

2

Both orthodontists

700

Malocclusion detection

CNN (YOLO)

Accuracy: 99.99

Preceison:0.99

Recall:1

F1 score:1

2

2

Khazaei et al

Iran [13]

Orthodontics

Validation

Study

Classification

Local

Lateral Cephalograms

N/A

As sex was input from patient records

NM

1476

Gender determination

CNN, Dense Net, ResNet, VGG

DenseNet Accuracy 0.9

Resnet Accuracy 0.62

VGG 16 Accuracy 0.75

2

3

Ehtesham et al

Iran [14]

Oral Medicine

Pilot study

Decision Support System

Local

Health records

N/A

As the cases and symptoms were retrieved from medical records

NM

500 training cases, 39 testing cases

Differential diagnosis

ML (Nearest neighbor algorithm)

Metrics were not mentioned explicitly but out of 39 diagnosis there were 9 misdiagnosis

4

4

Koopaie et al

Iran [15]

Restorative Dentistry

Case-control study

Classification

Local

Health records + cystatin S levels of saliva

3

Dentist

20

Caries detection

ML, ANN, XGBoost

Random Forest

SVM

ANN: Acc 90%, Sen 100%, Spec 72%.

XGboost: Acc 89%, Sen 93%, Spec 84%.

Random Forest: Acc 85%, Sen 93%, Spec 76%.

SVM: Acc 85%, Sen 86%, Spec 84%.

2

5

Adnan et al

Pakistan [1]

Endodontics

Validation study

Instance Segmentation

Local

OPGs

2

Endodontist

40 OPG, 1280 teeth crops

Teeth detection and numbering

CNN

88.2

2

6

Mariam et al

Pakistan [16]

Oral Pathology

Validation study

Diagnostic test accuracy

Local

Histology patches

2

Pathologist

280

Gaze pattern for annotation (Keratin pearl)

Faster R-CNN with Inceptionv2 architecture [2]

YOLOv3 with DarkNet53 architecture [3] YOLOv5 with DarkNet53

architecture.

85% less time was consumed in gaze annotations

3

7

Fatima et al

Pakistan [17]

Endodontics

Validation study

Classification and Decision Support System

Local and International

Periapical x rays

2

Radiologist and Dentist

516

Lesion detection

Length measurement/ apical foramen

Root morphology

Root Fracture

Case difficulty

CNN

Mask RCNN with back bone

Resnet 50

Resnet 101

Mobile Net V2

Proposed Backbone Network

Resnet 50: Pre.82, Rec.83, F1.78

Resnet 101: Pre .81, Rec .84,F1 .74

Mobile net V2: Pre.86, Rec .87,F1 .84

Proposed backbone network: Pre 0.86, Rec 0.89, F1 0.89

2

8

Bharathi et al

India [18]

Oral Pathology

Experimental study (why)

Classification

Others (Internet sources, mobile cameras)

Focused photographs

NM

NM

500 total, 140 diabetic

Diabetes tongue

CNN

83

2

9

Patil e tal

India [19]

Oral and Maxillofacial Surgery

Validation study

Classification

Local

OPGs

1

Oral and Maxillofacial surgeon

509

Gender determination (Mandible)

ANN

and Regression

The discriminant analysis had an overall

accuracy of 69.1%, logistic regression showed an accuracy of 69.9%, and ANN

exhibited a higher accuracy of 75%.

2

10

Prasad et al

India [20]

Orthodontics

Validation study

Decision Support System

Local

OPGs, Dental cast, dental clinical data

10–15

Orthodontists

700 training set: test set 70:30

10–15

Treatment planning (Maxilla, mandible, facial landmarks, age, dentition)

ML predictive models:

XGB Classifier

Random Forest Classifier

Linear SVM

Decision Tree Classifier

Logistic Regression

K-Neighbor Classifier

Naive Bayes Classifier

The model

showed an overall average accuracy of 84%, with the Decision Tree, Random Forest and XGB classifier

algorithms showing the highest accuracy ranging from 87 to 93%.

2

11

Katyal et al

India [21]

Orthodontics

Validation study

Decision Support System

Local

OPGs

NM

WebCeph

25

Cephalometric analysis (Maxilla, mandible, facial landmarks, dentition)

CNN

No statistically sig difference between manual tracing, digital tracing and WebCeph, WebCeph was more efficient

3

12

Sherly et al

India [22]

Restorative dentistry

Validation study

Classification

NM

OPGs, Clinical pictures

NM

NM

NM

Caries detection

DL (MDP (maximum directional pattern) using CNN

98.6%

2

13

Benakatti et al

India [23]

Oral and Maxillofacial surgery

Validation study

Identification

Local

OPGs

NM

NM

NM (only mentioned 80% of data set for training and 20% for testing)

Implant system identification

Support vector machine (SVM), K-Nearest

Neighbor (KNN), XG boost, and Logistic Regression

Classifiers

Best accuracy: Logistic Regression,

Overall 0.67

2

14

India [24]

Orthodontics

Validation study

Decision Support System

Local

Dental clinical data

1

NM

1000

Influence of anatomical risk factors in determining number of traumatized teeth per affected individual

MLP model of ANN

Overbite (100%) as the strongest risk factor for TDI in number of teeth of affected individual

2

15

Yadalam et al

India [25]

Oral and Maxillofacial surgery

Validation study

Decision Support System

Local

Dental clinical data

NM

NM

1032,825 (80%) training set, 207 (20%) data authentication, 45 controls

Prediction of postoperative pain after implant surgery (VAS)

ML (Multiple Linear Regression MLR) model

The data set’s output is evaluated using a

confusion matrix: the higher the confusion matrix’s diagonal

values, the more correct the predictions. The training sample

accuracy and the validation sample

2

16

Shwewood et al

India [26]

Endodontics

Validation

Identification and classification

Local

CBCT

2

Endodontists

135, 100 for training and 35 for testing

identification of C-shaped root canal anatomy

DL/CNN (U-Net, Residual U-Net and Xception U-Net)

Xception U-Net with CLAHE

provided the best results

the mean

sensitivity values were highest for Xception

U-Net (0.786 ± 0.0378, diagnostic test grade:

78.6% [good]) followed by residual U-Net

(mean sensitivity 5 0.746 ± 0.0391,

diagnostic test grade: 74.6% [good]) and

U-Net (0.720 ± 0.0495, diagnostic test grade:

72.0% [good]). The mean positive predictive

2

17

Mallishery et al

India [27]

Endodontics

Validation study

Decision Support System

Local

Radiovisographs, AAE Case difficulty Assessment forms

2

Endodontists

500

Difficulty of endodontic case

ML (support vector machine (SVM), and deep

neural network (DNN))

94.96%

2

18

Moidu et al

India [28]

Endodontics

Validation study

Classification

Local

Periapical x-ray

3

Endodontists

3000 periapical root areas (PRA) on 1950 digital IOPA

450 PRA for model validation

540 PRA on 250 IOPA for testing performance

Periapical lesion

DL (CNN YOLO.v3)

Across all PAI score, 303 PRA (56.11%) exhibited true

prediction.

True prediction:

PAI score 1

90.9%

PAI scores 2 and 5:

30%

PAI scores 3: 60%

PAI score 4: 71%

False

Prediction:

PAI score

2 was interpreted as PAI score 3 (32%) and PAI score

1 (25%).

PAI score 5 was interpreted as PAI

score 4 (35.8%) and PAI score 3 (20.8%)

PAI scores were dichotomized as healthy (PAI

scores 1 and 2) and diseased (PAI scores 3, 4, and 5),

the model achieved a true prediction of 76.6 and 92%,

respectively

The CNN model achieved a 92.1%

sensitivity,

76% specificity,

86.4% PPV and 86.1% NPV

Accuracy:86.3% F1 score: 0.89

Matthews correlation coefficient: 0.71

2

19

Ghosh et al

India [29]

Restorative dentistry

Randomized parallel group study

Dental practice management

Local

Others (Scientific papers)

NM

NM

250

Improving patient recall rate (Patient recall)

topic modeling using Labeled LDA

(Latent Dirichlet Allocation)

improved the patient recall rate from 21.1 to 37.8% (p-value =

0.024).

4

20

Fidya et al

Indonesia [30]

Orthodontics

Validation study

Classification

NM

Casts

NM

Health records

150

Gender Determination

ML

Naive Bayes

Decision tree

MLP

The accuracy rate of the Naive Bayes method was 82%, while that of the decision tree and MLP amounted to 84%.

2

21

Widyaningrum et al

Indonesia [31]

Periodontics

Validation study

Classification

Local

OPGs

2

Periodontist and General Dentist

1100

Periodontitis

CNN

U-Net, RCNN

U-Net showed the characteristic of semantic segmentation, and Mask

R-CNN performed instance segmentation with accuracy, precision, recall, and F1-score values of 95, 85.6, 88.2%,

and 86.6%,

2

22

Mahto et al

Nepal [32]

Orthodontics

Validation study

Decision Support System

local

Lateral Cephalograms

NM

WebCeph

30

compare the linear and angular cephalometric measurements obtained from web-based

fully automated Artificial Intelligence (AI) driven platform “WebCeph”™ with that from manual tracing

CNN

overall Intraclass correlation coefficient for manual and WebCeph tracing was > 0.9

3

23

Thahn et al

Vietnam [33]

Restorative dentistry

Validation study

Classification

Local

Dental clinical data

1

Dentist

1902

Dental caries

CNN

Faster Region-Based Convolutional Neural Networks (Faster R-CNNs), You Only Look

Once version 3 (YOLOv3), RetinaNet, and Single-Shot Multi-Box Detector (SSD)

For cavitated caries: YOLOv3:87.4% and Faster R-CNN: 71.4%

For visually non-cavitated: YOLOv3: 36.9% and Faster R-CNN: 26%

Specificity of all 4 models for cavitated caries: 86%

Sensitivity of all 4 models for non-cavitated caries: 71%

2

24

Tuan et al

Vietnam [34]

Oral Radiology

Validation study

Segmentation

NM

OPGs

NM

NM

56

new semi-supervised fuzzy clustering

algorithm named as SSFC-FS based on Interactive

Fuzzy Satisficing for the dental X-ray image segmentation

problem.

ML

Not mentioned

1

25

Ngoc et almachine

Vietnam [35]

Endodontics

Validation study

Classification

NM

Bitewing

2

Dentist

130

Periapical lesion diagnosis

CNN

Faster R-CNN

Specificity: 97.9%

Sensitivity: 89.5%

Accuracy: 95.6%

2