Locating Anatomical Landmarks on 2D Lateral Cephalograms Through Adversarial Encoder-Decoder Networks

Dai, Xiubin; Zhao, Hao; Liu, Tianliang; Cao, Dan; Xie, Lizhe

doi:10.1109/access.2019.2940623

Cited by 20 publications

(9 citation statements)

References 38 publications

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“…For clinical applications, a mean error within a 2 mm threshold has been suggested to be acceptable in many related studies [ 2 , 3 , 4 , 5 , 6 , 9 , 10 , 11 , 13 , 14 , 15 , 16 , 24 ]. Therefore, the MRE in the present study was clinically acceptable.…”

Section: Discussionmentioning

confidence: 99%

“…Most research to date has focused on head-to-head comparisons between artificial intelligence (AI)-based systems and dentists for the localization of cephalometric landmarks [ 2 , 3 , 4 , 5 , 6 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Previous studies have shown that AI is equivalent or even superior to experienced orthodontists under experimental conditions [ 11 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Effectiveness of Human–Artificial Intelligence Collaboration in Cephalometric Landmark Detection

Kang

et al. 2022

JPM

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Detection of cephalometric landmarks has contributed to the analysis of malocclusion during orthodontic diagnosis. Many recent studies involving deep learning have focused on head-to-head comparisons of accuracy in landmark identification between artificial intelligence (AI) and humans. However, a human–AI collaboration for the identification of cephalometric landmarks has not been evaluated. We selected 1193 cephalograms and used them to train the deep anatomical context feature learning (DACFL) model. The number of target landmarks was 41. To evaluate the effect of human–AI collaboration on landmark detection, 10 images were extracted randomly from 100 test images. The experiment included 20 dental students as beginners in landmark localization. The outcomes were determined by measuring the mean radial error (MRE), successful detection rate (SDR), and successful classification rate (SCR). On the dataset, the DACFL model exhibited an average MRE of 1.87 ± 2.04 mm and an average SDR of 73.17% within a 2 mm threshold. Compared with the beginner group, beginner–AI collaboration improved the SDR by 5.33% within a 2 mm threshold and also improved the SCR by 8.38%. Thus, the beginner–AI collaboration was effective in the detection of cephalometric landmarks. Further studies should be performed to demonstrate the benefits of an orthodontist–AI collaboration.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effectiveness of Human–Artificial Intelligence Collaboration in Cephalometric Landmark Detection

Kang

et al. 2022

JPM

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“…This work has addressed a complex task in computational biology: automatic landmark digitalization. The interest in this topic is not new, but the relevant algorithms and computational power have only been available fairly recently [60,61,62,63]. Even so most methods are seldom used by biologist and even so by entomologist.…”

Section: Discussion and Perspectivesmentioning

confidence: 99%

Automated landmarking for insects morphometric analysis using deep neural networks

Beurton-Aimar

Zemmari

et al. 2020

Ecological Informatics

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Landmarks are one of the important concepts in morphometry analysis. They are anatomical points that can be located consistently (e.g., corner of the eyes) and used to establish correspondence or divergence among morphologies of biological or non-biological specimens. Currently, the landmarks are mostly positioned manually by entomologists on numerical images. In this work, we propose a method to automatically predict the landmarks on entomological images based on Deep Learning methods, more specifically by using Convolutional Neural Network (CNN). We propose a CNN architecture, EB-Net, which is built in a modular way the concept of "Elementary Blocks", each made up of usual layer types of CNN. After using a custom data augmentation procedure, the network has been trained and tested on a data set of different anatomical part of carabids (pronotum, head and elytra). In this numerical experiment, we have generated two strategies to evaluate the network and to improve the obtained results: training from scratch or applying a fine-tuning step. The predicted landmark coordinates have been compared to the coordinates of the manual landmarks provided by the biologists. The statistical analysis of the distances between predicted and manual coordinates has shown that our predictions can replace efficiently manual landmarking and allows to propose automatization of such operation.

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“…Similarly, a 3D CNN was used for surgery landmark prediction using CT images, achieving a mean error of 5.8 mm in comparison to the original landmark 116 . X‐ray images have been used as inputs for the landmark detection task with an encoder‐decoder architecture 114 or Bayesian CNNs 115 …”

Section: Deep Learning Applications In Dentistrymentioning

confidence: 99%

Applications of artificial intelligence in dentistry: A comprehensive review

Carrillo-Pérez

Pecho

Morales

et al. 2021

J Esthet Restor Dent

120

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Objective To perform a comprehensive review of the use of artificial intelligence (AI) and machine learning (ML) in dentistry, providing the community with a broad insight on the different advances that these technologies and tools have produced, paying special attention to the area of esthetic dentistry and color research. Materials and methods The comprehensive review was conducted in MEDLINE/PubMed, Web of Science, and Scopus databases, for papers published in English language in the last 20 years. Results Out of 3871 eligible papers, 120 were included for final appraisal. Study methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other ML techniques (n = 32), which were mainly applied to disease identification, image segmentation, image correction, and biomimetic color analysis and modeling. Conclusions The insight provided by the present work has reported outstanding results in the design of high‐performance decision support systems for the aforementioned areas. The future of digital dentistry goes through the design of integrated approaches providing personalized treatments to patients. In addition, esthetic dentistry can benefit from those advances by developing models allowing a complete characterization of tooth color, enhancing the accuracy of dental restorations. Clinical significance The use of AI and ML has an increasing impact on the dental profession and is complementing the development of digital technologies and tools, with a wide application in treatment planning and esthetic dentistry procedures.

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Locating Anatomical Landmarks on 2D Lateral Cephalograms Through Adversarial Encoder-Decoder Networks

Cited by 20 publications

References 38 publications

Effectiveness of Human–Artificial Intelligence Collaboration in Cephalometric Landmark Detection

Effectiveness of Human–Artificial Intelligence Collaboration in Cephalometric Landmark Detection

Automated landmarking for insects morphometric analysis using deep neural networks

Applications of artificial intelligence in dentistry: A comprehensive review

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