3D Auto-Segmentation of Mandibular Condyles

Brosset, Serge; Dumont, Maxime; Bianchi, Jonas; Ruellas, Antônio Carlos de Oliveira; Cevidanes, Lucia; Yatabe, Marília; Gonçalves, João Roberto; Benavides, Erika; Soki, Fabiana N.; Paniagua, Beatriz; Prieto, Juan Carlos; Najarian, Kayvan; Gryak, Jonathan; Soroushmehr, S. M. Reza

doi:10.1109/embc44109.2020.9175692

Cited by 10 publications

(8 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fully automated condylar segmentation method developed in this study is expected to significantly enhance the accuracy of condylar degeneration classification in TMJOA. 18 As temporomandibular joint osteoarthritis involves bone surfaces, CBCT seems to be the gold standard for diagnosis under current conditions. In addition, the use of artificial intelligence is increasing in areas such as magnetic resonance imaging (MR) and ultrasound, which are used in diseases related to soft tissues.…”

Section: Discussionmentioning

confidence: 99%

Classification of temporomandibular joint osteoarthritis on cone beam computed tomography images using artificial intelligence system

Eşer

Duman

Bayrakdar

et al. 2023

J of Oral Rehabilitation

View full text Add to dashboard Cite

Artificial intelligence is a field of computer science dedicated to the development of computer algorithms to perform tasks traditionally associated with human intelligence such as the ability to learn and solve problems. 1 This includes machine learning (ML), representational learning and deep learning. 2 Many researchers are currently interested in artificial intelligence, which has been used extensively in a wide range of industries recently. This trend also applies to the dental industry, and applications of artificial intelligence in the field of oral

show abstract

Section: Discussionmentioning

confidence: 99%

Classification of temporomandibular joint osteoarthritis on cone beam computed tomography images using artificial intelligence system

Eşer

Duman

Bayrakdar

et al. 2023

J of Oral Rehabilitation

View full text Add to dashboard Cite

show abstract

“…The medical application of AI has attracted growing attentions due to its high accuracy rate. As a major branch of medicine, AI has also been playing an increasingly important role in the diagnosis of oral diseases and the formulation of treatment plans [4], [22]. For example, scholars used the Deep Neural Network (DNN)-based image recognition model to diagnose caries, and the accuracy rate of molar region was as high as 88% [23].…”

Section: Related Workmentioning

confidence: 99%

“…People with TMD usually have the symptoms of TMJ clicking, pain, and limitations of opening mouth, as well as the comorbid with bruxism, tinnitus, snore, depression, and chronic fatigue [2]. The prevalence is about 31% in adults and 11% in children and adolescents [3], respectively, resulting in an estimated annual cost of $4 billions [4]. However, the etiology of TMD remains unclear, and its symptoms, signs, and progression are still complex.…”

Section: Introductionmentioning

confidence: 99%

Assisting in Diagnosis of Temporomandibular Disorders: A Deep Learning Approach

et al. 2022

View full text Add to dashboard Cite

The etiology of Temporomandibular disorders (TMD) is still unclear, and its symptoms, signs and progression are extremely complex. TMD require early diagnosis and treatments, especially for combinations with other oral diseases. The research targets at developing an artificial neural network (ANN) model for predicting TMD based on clinical-collected data including clinical features, systematic medical condition, and psychosocial state. The popular data mining-based ANN was utilized to predict TMD with all 18 variables collected from patients as the input. The total dataset consists of 88 cases which were reviewed by Board-certificated orthodontists. 75% (66) cases are randomly selected as the training dataset, while the remaining 25% (22) cases are for test. Among the considered 88 cases, 58 (65.9%) were with TMD, while the left 30 (34.1%) without TMD. The numbers of male and female were 21 and 67, respectively, while the average age was 27.63 years. The calculated average sensitivity and specificity of ANN-based TMD risk predictions through 10-fold-cross-validation analysis were 92.31% (95% confidence interval (CI), 62.09%-99.60%) and 88.89% (95% CI, 50.67%-99.42%), respectively. Moreover, the accuracy rate of ANN was 90.91% (95% CI, 78.90%-100.00%). The results show the proposed ANN model could predict the TMD risks with a high accuracy rate, indicating the potential of machine learning in oral and maxillofacial diseases screening and diagnosis, which was further illustrated in a comparison with two doctors. This study can help dental care providers to find individuals' risk of TMD by inputting patient's psychological factors, oral examinations, and systemic medical conditions to the developed artificial intelligence (AI) model.

show abstract

“…Furthermore, it possesses a tool for data processing (TMJseg) that allows automatic segmentation of the condyles from the CBCT scans. 72…”

Section: Tmj Decision Optimizationmentioning

confidence: 99%

“…magnetic resonance images, panoramic images, CT and CBCT scans). Furthermore, it possesses a tool for data processing (TMJseg) that allows automatic segmentation of the condyles from the CBCT scans 72 . Regarding the in‐depth analytics component of the CDSS, the DSCI’s statistical analysis, cross‐validation and machine learning (Light GBM and XGBoost) tools permit users to integrate patient‐specific multi‐source data and to obtain a diagnosis of the TMJ condition, that is, healthy or diseased.…”

Section: Data Storage For Computation and Integrationmentioning

confidence: 99%

Clinical decision support systems in orthodontics: A narrative review of data science approaches

Turkestani

Bianchi

Deleat‐Besson

et al. 2021

Orthod Craniofacial Res

Self Cite

View full text Add to dashboard Cite

Advancements in technology and data collection generated immense amounts of information from various sources such as health records, clinical examination, imaging, medical devices, as well as experimental and biological data. Proper management and analysis of these data via high-end computing solutions, artificial intelligence and machine learning approaches can assist in extracting meaningful information that enhances population health and well-being. Furthermore, the extracted knowledge can provide new avenues for modern healthcare delivery via clinical decision support systems. This manuscript presents a narrative review of data science approaches for clinical decision support systems in orthodontics. We describe the fundamental components of data science approaches including (a) Data collection, storage and management; (b) Data processing; (c) In-depth data analysis; and (d) Data communication. Then, we introduce a web-based data management platform, the Data Storage for Computation and Integration, for temporomandibular joint and dental clinical decision support systems.

show abstract

3D Auto-Segmentation of Mandibular Condyles

Cited by 10 publications

References 10 publications

Classification of temporomandibular joint osteoarthritis on cone beam computed tomography images using artificial intelligence system

Classification of temporomandibular joint osteoarthritis on cone beam computed tomography images using artificial intelligence system

Assisting in Diagnosis of Temporomandibular Disorders: A Deep Learning Approach

Clinical decision support systems in orthodontics: A narrative review of data science approaches

Contact Info

Product

Resources

About