mlsanityData to 7 Oct 2026

Convolutional Neural Networks in Orthodontics: a review

arXiv 2104.08886 ↗First posted 18 Apr 2021cs.CV cs.AI eess.IVcited by 0

Not on the map. Why?

Abstract (arXiv)

Convolutional neural networks (CNNs) are used in many areas of computer vision, such as object tracking and recognition, security, military, and biomedical image analysis. This review presents the application of convolutional neural networks in one of the fields of dentistry - orthodontics. Advances in medical imaging technologies and methods allow CNNs to be used in orthodontics to shorten the planning time of orthodontic treatment, including an automatic search of landmarks on cephalometric X-ray images, tooth segmentation on Cone-Beam Computed Tomography (CBCT) images or digital models, and classification of defects on X-Ray panoramic images. In this work, we describe the current methods, the architectures of deep convolutional neural networks used, and their implementations, together with a comparison of the results achieved by them. The promising results and visualizations of the described studies show that the use of methods based on convolutional neural networks allows for the improvement of computer-based orthodontic treatment planning, both by reducing the examination time and, in many cases, by performing the analysis much more accurately than a manual orthodontist does.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 145 references that are in the map. First posted on arXiv 18 Apr 2021.

Builds on

31 of 145 references.

References · 31

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  5. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  6. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  7. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  8. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  9. 2016Stacked Hourglass Networks for Human Pose Estimationcited by 913
  10. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  11. 2019CenterNet: Keypoint Triplets for Object Detectioncited by 654
  12. 2016Fully-Convolutional Siamese Networks for Object Trackingcited by 566
19 more
  1. 2017Deep Layer Aggregationcited by 436
  2. 2016Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learningcited by 209
  3. 2019Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screeningcited by 94
  4. 2018Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Cloudscited by 62
  5. 2020Structured Landmark Detection via Topology-Adapting Deep Graph Learningcited by 50
  6. 2014MatConvNet - Convolutional Neural Networks for MATLABcited by 36
  7. 2019Cephalometric Landmark Detection by AttentiveFeature Pyramid Fusion and Regression-Votingcited by 32
  8. 2019An Attention-Guided Deep Regression Model for Landmark Detection in Cephalogramscited by 27
  9. 2020Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentationcited by 10
  10. 2018Deep Geodesic Learning for Segmentation and Anatomical Landmarkingcited by 8
  11. 2019Estimation of preterm birth markers with U-Net segmentation networkcited by 7
  12. 2016Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysiscited by 6
  13. 2020Individual Tooth Detection and Identification from Dental Panoramic X-Ray Images via Point-wise Localization and Distance Regularizationcited by 6
  14. 2020Spontaneous preterm birth prediction using convolutional neural networkscited by 5
  15. 2018Coarse-to-fine volumetric segmentation of teeth in Cone-Beam CTcited by 4
  16. 2020Locating Cephalometric X-Ray Landmarks with Foveated Pyramid Attentioncited by 2
  17. 2016LightNet: A Versatile, Standalone Matlab-based Environment for Deep Learningcited by 1
  18. 2018Estimating Achilles tendon healing progress with convolutional neural networkscited by 1
  19. 2019Using Natural Language Processing to Develop an Automated Orthodontic Diagnostic Systemcited by 1

Built on by

Not cited yet.