Table 1 The top 20 most cited articles in AI in orthodontics and orthognathic surgery
No | Article titles | Authors | Journals | Year of publication | Citations | Average citations per year | Classification of the topics |
|---|---|---|---|---|---|---|---|
1 | Fully automated quantitative cephalometry using convolutional neural networks | Arik et al. | Journal of Medical Imaging | 2017 | 119 | 19.83 | 2. Automated cephalometric landmarking and/or analysis |
2 | Artificial intelligence in orthodontics Evaluation of a fully automated cephalometric analysis using a customized convolutional neural network | Kunz et al. | Journal of Orofacial Orthopedics | 2020 | 72 | 24.00 | 2. Automated cephalometric landmarking and/or analysis |
3 | Automated identification of cephalometric landmarks: Part 2-Might it be better than human? | Hwang et al. | Angle Orthodontist | 2020 | 65 | 21.67 | 2. Automated cephalometric landmarking and/or analysis |
4 | Artificial Neural Network Modeling for Deciding if Extractions Are Necessary Prior to Orthodontic Treatment | Xie et al. | Angle Orthodontist | 2010 | 65 | 5.00 | 1. Diagnosis and treatment planning |
5 | Automated identification of cephalometric landmarks: Part 1-Comparisons between the latest deep-learning methods YOLOV3 and SSD | Park et al. | Angle Orthodontist | 2019 | 64 | 16.00 | 2. Automated cephalometric landmarking and/or analysis |
6 | Automated Skeletal Classification with Lateral Cephalometry Based on Artificial Intelligence | Yu et al. | Journal of Dental Research | 2020 | 57 | 19.00 | 1. Diagnosis and treatment planning |
7 | Deep Geodesic Learning for Segmentation and Anatomical Landmarking | Torosdagli et al. | IEEE Transactions on Medical Imaging | 2019 | 57 | 14.25 | 2. Automated cephalometric landmarking and/or analysis |
8 | Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks | Lee et al. | BMC Oral Health | 2020 | 49 | 16.33 | 2. Automated cephalometric landmarking and/or analysis |
9 | Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age | Patcas et al | International Journal of Oral & Maxillofacial Surgery | 2019 | 47 | 11.75 | 1. Diagnosis and treatment planning |
10 | An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms | Zhong et al. | Lecture Notes in Computer Science | 2019 | 45 | 11.25 | 2. Automated cephalometric landmarking and/or analysis |
11 | Orthodontic Treatment Planning based on Artificial Neural Networks | Li et al. | Scientific Reports | 2019 | 43 | 10.75 | 1. Diagnosis and treatment planning |
12 | Web-based fully automated cephalometric analysis by deep learning | Kim et al. | Computer Methods and Programs in Biomedicine | 2020 | 42 | 14.00 | 2. Automated cephalometric landmarking and/or analysis |
13 | Personal Computer-Based Cephalometric Landmark Detection With Deep Learning, Using Cephalograms on the Internet | Nishimoto et al. | The Journal of Craniofacial Surgery | 2019 | 42 | 10.50 | 2. Automated cephalometric landmarking and/or analysis |
14 | Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics | Kok et al. | Progress in Orthodontics | 2019 | 41 | 10.25 | 3. Assessment of growth and development |
15 | Artificial Intelligent Model With Neural Network Machine Learning for the Diagnosis of Orthognathic Surgery | Choi et al. | The Journal of Craniofacial Surgery | 2019 | 39 | 9.75 | 1. Diagnosis and treatment planning |
16 | Artificial Intelligence for Fast and Accurate 3-Dimensional Tooth Segmentation on Cone-beam Computed Tomography | Lahoud et al. | The Journal of Endodontics | 2021 | 37 | 18.50 | 2. Automated cephalometric landmarking and/or analysis |
17 | A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery | Knoops et al. | Scientific Reports | 2019 | 37 | 9.25 | 1. Diagnosis and treatment planning |
18 | Cephalometric Landmark Detection by Attentive Feature Pyramid Fusion and Regression-Voting | Chen et al. | Lecture Notes in Computer Science | 2019 | 34 | 8.50 | 2. Automated cephalometric landmarking and/or analysis |
19 | Current Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice | Hung et al. | International Journal of Environmental Research and Public Health | 2020 | 32 | 10.67 | 4. Miscellaneous |
20 | Automatic Cephalometric Landmark Detection on X-ray Images Using a Deep-Learning Method | Song et al. | Applied Sciences | 2020 | 32 | 10.67 | 2. Automated cephalometric landmarking and/or analysis |