Cephalometric Variables Prediction from Lateral Photographs Between Different Skeletal Patterns Using Regression Artificial Neural Networks

Ali, Saif Mauwafak; Saloom, Hayder F.; Tawfeeq, Mohammed Ali

doi:10.5152/turkjorthod.2022.21087

Cited by 2 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several studies on ANN in orthodontics have reported their results on automated cephalometric landmark identification 12,18–25 . Furthermore, commercial orthodontic products for orthodontic diagnosis and treatment evaluation are already in clinical use.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies on ANN in orthodontics have reported their results on automated cephalometric landmark identification. 12,[18][19][20][21][22][23][24][25] Furthermore, commercial orthodontic products for orthodontic diagnosis and treatment evaluation are already in clinical use. Clinicians can choose these AI products and benefit from orthodontic treatment and clinical practice.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-based Assessment of Facial Asymmetry Using U-Net Deep Convolutional Neural Network Algorithm

Jeon,

Kim,

Lee

2023

Journal of Craniofacial Surgery

View full text Add to dashboard Cite

Objectives: This study aimed to evaluate the diagnostic performance of a deep convolutional neural network (DCNN)-based computer-assisted diagnosis (CAD) system to detect facial asymmetry on posteroanterior (PA) cephalograms and compare the results of the DCNN with those made by the orthodontist. Materials and Methods: PA cephalograms of 1020 patients with orthodontics were used to train the DCNN-based CAD systems for autoassessment of facial asymmetry, the degree of menton deviation, and the coordinates of its regarding landmarks. Twenty-five PA cephalograms were used to test the performance of the DCNN in analyzing facial asymmetry. The diagnostic performance of the DCNN-based CAD system was assessed using independent t-tests and Bland-Altman plots. Results: Comparison between the DCNN-based CAD system and conventional analysis confirmed no significant differences. Bland-Altman plots showed good agreement for all the measurements. Conclusions: The DCNN-based CAD system might offer a clinically acceptable diagnostic evaluation of facial asymmetry on PA cephalograms.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Deep Learning-based Assessment of Facial Asymmetry Using U-Net Deep Convolutional Neural Network Algorithm

Jeon,

Kim,

Lee

2023

Journal of Craniofacial Surgery

View full text Add to dashboard Cite

show abstract

A Comparative Study of Deep Learning and Manual Methods for Identifying Anatomical Landmarks through Cephalometry and Cone-Beam Computed Tomography: A Systematic Review and Meta-Analysis

Lee,

Pyeon,

Han

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Background: Researchers have noted that the advent of artificial intelligence (AI) heralds a promising era, with potential to significantly enhance diagnostic and predictive abilities in clinical settings. The aim of this meta-analysis is to evaluate the discrepancies in identifying anatomical landmarks between AI and manual approaches. Methods: A comprehensive search strategy was employed, incorporating controlled vocabulary (MeSH) and free-text terms. This search was conducted by two reviewers to identify published systematic reviews. Three major electronic databases, namely, Medline via PubMed, the Cochrane database, and Embase, were searched up to May 2024. Results: Initially, 369 articles were identified. After conducting a comprehensive search and applying strict inclusion criteria, a total of ten studies were deemed eligible for inclusion in the meta-analysis. The results showed that the average difference in detecting anatomical landmarks between artificial intelligence and manual approaches was 0.35, with a 95% confidence interval (CI) ranging from −0.09 to 0.78. Additionally, the overall effect between the two groups was found to be insignificant. Upon further analysis of the subgroup of cephalometric radiographs, it was determined that there were no significant differences between the two groups in terms of detecting anatomical landmarks. Similarly, the subgroup of cone-beam computed tomography (CBCT) revealed no significant differences between the groups. Conclusions: In summary, the study concluded that the use of artificial intelligence is just as effective as the manual approach when it comes to detecting anatomical landmarks, both in general and in specific contexts such as cephalometric radiographs and CBCT evaluations.

show abstract

Cephalometric Variables Prediction from Lateral Photographs Between Different Skeletal Patterns Using Regression Artificial Neural Networks

Cited by 2 publications

References 21 publications

Deep Learning-based Assessment of Facial Asymmetry Using U-Net Deep Convolutional Neural Network Algorithm

Deep Learning-based Assessment of Facial Asymmetry Using U-Net Deep Convolutional Neural Network Algorithm

A Comparative Study of Deep Learning and Manual Methods for Identifying Anatomical Landmarks through Cephalometry and Cone-Beam Computed Tomography: A Systematic Review and Meta-Analysis

Contact Info

Product

Resources

About