Machine learning and orthodontics, current trends and the future opportunities: A scoping review

Mohammad‐Rahimi, Hossein; Nadimi, Mohadeseh; Rohban, Mohammad Hossein; Shamsoddin, Erfan; Lee, Victor Y.; Motamedian, Saeed Reza

doi:10.1016/j.ajodo.2021.02.013

Cited by 63 publications

(46 citation statements)

References 75 publications

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“…Automatic diagnosis based on AI technology is gaining extensive attention as a practical clinical auxiliary diagnosis tool, and it is a developing trend in orthodontics [ 26 ]. In order to achieve a better application of CNNs, we trained four representative CNNs and compared their model performance in this research.…”

Section: Discussionmentioning

confidence: 99%

“…The black-box nature of CNNs has restricted their clinical use, as they do not provide any explanation or knowledge as to why or how they make predictions [ 26 ]. Recent studies on medical imaging analyses have placed a greater emphasis on the interpretability of AI models because a medical diagnosis system needs to be transparent, understandable, and explainable to gain the trust of physicians [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

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Automatic Classification for Sagittal Craniofacial Patterns Based on Different Convolutional Neural Networks

Lei

et al. 2022

Diagnostics

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(1) Background: The present study aims to evaluate and compare the model performances of different convolutional neural networks (CNNs) used for classifying sagittal skeletal patterns. (2) Methods: A total of 2432 lateral cephalometric radiographs were collected. They were labeled as Class I, Class II, and Class III patterns, according to their ANB angles and Wits values. The radiographs were randomly divided into the training, validation, and test sets in the ratio of 70%:15%:15%. Four different CNNs, namely VGG16, GoogLeNet, ResNet152, and DenseNet161, were trained, and their model performances were compared. (3) Results: The accuracy of the four CNNs was ranked as follows: DenseNet161 > ResNet152 > VGG16 > GoogLeNet. DenseNet161 had the highest accuracy, while GoogLeNet possessed the smallest model size and fastest inference speed. The CNNs showed better capabilities for identifying Class III patterns, followed by Classes II and I. Most of the samples that were misclassified by the CNNs were boundary cases. The activation area confirmed the CNNs without overfitting and indicated that artificial intelligence could recognize the compensatory dental features in the anterior region of the jaws and lips. (4) Conclusions: CNNs can quickly and effectively assist orthodontists in the diagnosis of sagittal skeletal classification patterns.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Automatic Classification for Sagittal Craniofacial Patterns Based on Different Convolutional Neural Networks

Lei

et al. 2022

Diagnostics

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“…Artificial intelligence (AI) is defined as "the science and engineering of making intelligent machines" which can solve problems instead of humans [1]. Machine learning is one of the main subcategories of AI, enabling machines to learn and improve from experience without being explicitly programmed for a single task [2]. Typically, computers use example data that is extracted discriminant features, which are mostly handcrafted, from images in order to train machine learning systems [3].…”

Section: Introductionmentioning

confidence: 99%

Deep learning for caries detection: A systematic review

Mohammad‐Rahimi¹,

Motamedian²,

Rohban³

et al. 2022

Journal of Dentistry

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112

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“…The recent advances in machine learning and a large amount of available data have laid the foundations to apply the machine learning methodology to various orthodontic aspects, including automated landmark detection on lateral cephalograms and photography images, facial attractiveness, and skeletal classification, as well as determining the degree of cervical vertebra maturation, providing orthodontic tooth extraction decisions, and predicting the need for orthodontic treatment or orthognathic surgery. Based on current studies, the most promising applications have been focused on predicting the need for treatment and decision making for tooth extractions before orthodontic treatment ( Mohammad-Rahimi et al, 2021 ). However, the application to evaluate the craniodentofacial morphological harmony after orthodontic treatment attracts rare attention.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Evaluation on Craniodentofacial Morphological Harmony of Patients After Orthodontic Treatment

et al. 2022

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Objectives: Machine learning is increasingly being used in the medical field. Based on machine learning models, the present study aims to improve the prediction performance of craniodentofacial morphological harmony judgment after orthodontic treatment and to determine the most significant factors.Methods: A dataset of 180 subjects was randomly selected from a large sample of 3,706 finished orthodontic cases from six top orthodontic treatment centers around China. Thirteen algorithms were used to predict the value of the cephalometric morphological harmony score of each subject and to search for the optimal model. Based on the feature importance ranking and by removing features, the regression models of machine learning (including the Adaboost, ExtraTree, XGBoost, and linear regression models) were used to predict and compare the score of harmony for each subject from the dataset with cross validations. By analyzing the prediction values, the most optimal model and the most significant cephalometric characteristics were determined.Results: When nine features were included, the performance of the XGBoost regression model was MAE = 0.267, RMSE = 0.341, and Pearson correlation coefficient = 0.683, which indicated that the XGBoost regression model exhibited the best fitting and predicting performance for craniodentofacial morphological harmony judgment. Nine cephalometric features including L1/NB (inclination of the lower central incisors), ANB (sagittal position between the maxilla and mandible), LL-EP (distance from the point of the prominence of the lower lip to the aesthetic plane), SN/OP (inclination of the occlusal plane), SNB (sagittal position of the mandible in relation to the cranial base), U1/SN (inclination of the upper incisors to the cranial base), L1-NB (protrusion of the lower central incisors), Ns-Prn-Pos (nasal protrusion), and U1/L1 (relationship between the protrusions of the upper and lower central incisors) were revealed to significantly influence the judgment.Conclusion: The application of the XGBoost regression model enhanced the predictive ability regarding the craniodentofacial morphological harmony evaluation by experts after orthodontic treatment. Teeth position, teeth alignment, jaw position, and soft tissue morphology would be the most significant factors influencing the judgment. The methodology also provided guidance for the application of machine learning models to resolve medical problems characterized by limited sample size.

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Machine learning and orthodontics, current trends and the future opportunities: A scoping review

Cited by 63 publications

References 75 publications

Automatic Classification for Sagittal Craniofacial Patterns Based on Different Convolutional Neural Networks

Automatic Classification for Sagittal Craniofacial Patterns Based on Different Convolutional Neural Networks

Deep learning for caries detection: A systematic review

Machine Learning-Based Evaluation on Craniodentofacial Morphological Harmony of Patients After Orthodontic Treatment

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