Table 1.
The most cited articles relevant to the queried keywords in researched topic.
| # | Authors | Title | Citations | FWCI | Reference | Published |
|---|---|---|---|---|---|---|
| 1 | Kunz et al. | Artificial intelligence in orthodontics: Evaluation of a fully automated cephalometric analysis using a customized convolutional neural network | 65 | 12.89 | [33] | 2020 |
| 2 | Maspero et al. | Available technologies, applications and benefits of teleorthodontics. A literature review and possible applications during the COVID-19 pandemic | 59 | 3.44 | [34] | 2020 |
| 3 | Yu et al. | Automated Skeletal Classification with Lateral Cephalometry Based on Artificial Intelligence | 57 | 10.21 | [35] | 2020 |
| 4 | Lee et al. | Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks | 40 | 7.51 | [36] | 2020 |
| 5 | Leite et al. | Radiomics and Machine Learning in Oral Healthcare | 38 | 1.83 | [37] | 2020 |
| 6 | Wang et al. | Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning | 27 | 11.36 | [38] | 2021 |
| 7 | Bichu et al. | Applications of artificial intelligence and machine learning in orthodontics: a scoping review | 20 | 6.35 | [39] | 2021 |
| 9 | Schwendicke et al. | Deep learning for cephalometric landmark detection: systematic review and meta-analysis | 18 | 3.06 | [40] | 2021 |
| 10 | Deshpande et al. | Teledentistry: A boon amidst COVID-19 Lockdown—A narrative review | 16 | 1.67 | [41] | 2021 |
| 11 | Ahmed et al. | Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry—A Systematic Review | 16 | 1.34 | [42] | 2021 |
| 12 | Mohammadad-Rahimi et al. | Machine learning and orthodontics, current trends and the future opportunities: A scoping review | 14 | 4.34 | [43] | 2021 |
| 13 | Juerchott et al. | In vivo comparison of MRI- and CBCT-based 3D cephalometric analysis: beginnning of a non-ionizing diagnostic era in craniomaxillofacial imaging? | 14 | 1.44 | [44] | 2020 |
| 14 | MacHoy et al. | The ways of using machine learning in dentistry | 14 | 0.84 | [45] | 2020 |
| 15 | Ren et al. | Machine learning in dental, oral and craniofacial imaging: A review of recent progress | 13 | 1.39 | [46] | 2021 |
| 18 | Dalessandri et al. | Attitude towards telemonitoring in orthodontists and orthodontic patients | 11 | 4.8 | [47] | 2021 |
| 20 | Caruso et al. | A knowledge-based algorithm for automatic monitoring of orthodontic patients: The dental monitoring system. Two cases | 10 | 1.81 | [27] | 2021 |
| 21 | Impellizzeri | Dental Monitoring Application: I tis a valid innovation in the Orthodontics Pracice? | 9 | 0.86 | [48] | 2020 |
| 22 | Monill-González et al. | Artificial intelligence in orthodontics: Where are we now? A scoping review | 9 | 2.58 | [49] | 2021 |
| 23 | Thurzo et al. | Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis | 8 | 5.15 | [12] | 2022 |
| 24 | Bulatova et al. | Assessment of automatic cephalometric landmark identification using artificial intelligence | 3.72 | [50] | 2021 | |
| 25 | Park et al. | Teledentistry platforms for orthodontics | 8 | 3.39 | [51] | 2021 |
| 26 | Sangalli et al. | Effects of remote digital monitoring on oral hygiene of orthodontic patients: a prospective study | 7 | 3.05 | [52] | 2021 |
| 27 | Achmad et al. | Teledentistry as a solution in dentistry during the covid-19 pandemic period: A systematic review | 6 | 0.78 | [53] | 2020 |