|
| S. no. | Author, year of publication, country | Objective | Algorithm | Language | Dataset size | Accuracy |
|
| 1 | (1) Devito | To diagnose proximal dental caries | Hidden-layer perceptron with backpropagation | English | 160 X-dental radiograph | 88.4% |
| (2) de Souza Barbosa |
| (3) Filho (2008, Brazil) |
| 2 | (1) Mayank | To detect tooth caries in bitewing radiographs | F-CNN | English | 3000 | 70% |
| (2) Pratyush Kumar |
| (3) Lalit Pradhan |
| (4) Srikrishna Varadarajan (2017, USA) |
| 3 | (1) Casalegno | To predict occlusal and proximal caries | CNN | English | 217 dental images | (1) Occlusal = 83.6% |
| (2) Newton |
| (3) Daher | (2) Proximal = 85.6% |
| (4) Abdelaziz A. Lodi-Rizzini | |
| (5) F. Schürmann |
| (6) I. Krejci |
| (7) H. Markram (2018, Switzerland) |
| 4 | (1) Jae-Hong Leea | To evaluate the efficacy of deep CNN algorithms for detection and diagnosis of dental caries on periapical radiographs | CNN | English | 3000 X-dental images | (1) Molar = 89% |
| (2) Do-Hyung Kima |
| (3) Seong-Nyum Jeonga | (1) Molar | (2) Premolar = 88% |
| (4) Seong-Ho Choib(2018, Korea) | (2) Premolar |
| (3) Both molar and premolar |
| (3) Both molar and premolar = 82% |
| 5 | (1) Laura A. Zanella-Calzada | To diagnose caries using socioeconomic and nutritional features as determinants | ANN | English | 189 images | 69% |
| (2) Carlos E. Galván-Tejada |
| (3) Nubia M. Chávez-Lamas |
| (4) Jesús Rivas-Gutierre |
| (5) Rafael Magallanes-Quintanar |
| (6) Jose M. Celaya-Padilla |
| (7) Jorge I. Galván-Tejada |
| (8) Hamurabi Gamboa-Rosales (2018, Mexico) |
| 6 | (1) K. Moutselos | To determine occlusal caries in dental intraoral images | MASK | English | 88 | (1) MC = most common = 88.9% |
| (2) E. Berdouses | (R-CNN) | In-vitro dental images | (2) CPC = center pixel class = 77.8% |
| (3) C. Oulis |
| (4) I. Maglogiannis (2019, Greece) | (3) WC = worst class = 66.7% |
| 7 | (1) Shashi Kant Patil | To evaluate accurate detection of caries using feature extraction and classification of the dental images along with amalgamation-adaptive dragonfly algorithm (DA) algorithm and neural network (NN) classifier | (1) Adaptive dragonfly algorithm (ADA-NN) | English | 120 dental images | Summarizes the performance analysis of proposed ADA-NN classifier over the other conventional classifiers. |
| (2) Vaishali Kulkarni | Test case 1. Here, the accuracy of the proposed model is 5.55% better than KNN, SVM, NB and LM-NN. |
| (3) Archana Bhise (2019, India) | (2) K-nearest neighbors (KNN) | 40 for each test case | Test case 2. ADA model is 11.76% and 52% superior to the existing models like KNN and SVM in terms of accuracy. |
| Test case 3. The accuracy of the proposed model is 6.30% better than SVM and NB classifier |
| (3) Support vector machine (SVM) |
| (4) Naive Bayes (NB) |
| (5) LM-NN |
| 8 | (1) Syed Javed | To predict of post- Streptococcus mutans in dental caries | Feedforward backpropagation | English | 45 premolar teeth images | 99% |
| (2) M. Zakirulla | ANN |
| (3) Rahmath Ulla Baig | (as it causes the dental caries) |
| (4) S.M. Asif |
| (5) Allah Baksh Meer (2019, Saudi Arabia) |
| 9 | (1) Man Hung | Application of machine learning for diagnostic prediction of root caries | Support vector machine (SVM) | English | 5,135 | From all the machine learning algorithms developed, support vector machine (SVM) demonstrated the best performance with an accuracy of 97.1% |
| (2) Maren W. Voss |
| (3) Megan N. Rosales | Random forest regression (RF) |
| (4) Wei Li |
| (5) Weicong Su | k-nearest neighbors (k-NN) |
| (6) Julie Xu |
| (7) Jerry Bounsanga, | Logistic regression |
| (8) Bianca Ruiz-Negrón Evelyn Lauren |
| (9) Frank W. Licari |
| (2019, Jordan) |
| 10 | (1) Geetha K. | To diagnose dental caries | Backpropagation | English | 105 | 97.1% |
| (2) S. Aprameya |
| (3) Dharam |
| (4) M. Hinduja (2020, India) |
| 11 | (1) Duc Long Duong | Automated caries detection with smartphone color photography using machine learning | Support vector machine (SVM) | English | 620 unrestored molars/premolars | 92.37% |
| (2) Malitha Humayun Kabir |
| (3) Rong fu Kuo (2021, Taiwan) |
| 12 | (1) J. Kühnisch | Caries detection on intraoral images using artificial intelligence | Convolutional neural networks (CNNs) | English | 2,417 peranent teeth | 93.3% |
| (2) O. Meyer | (1,317 occlusal and 1,100 smooth surfaces) |
| (3) M. Hesenius |
| (4) R. Hickel1 |
| (5) V. Gruhn (2022, Germany) |
|
