Assessment of automatic cephalometric landmark identification using artificial intelligence

Булатова, Галина Алексеевна; Kusnoto, Budi; Grace, Viana; Tsay, T. Peter; Avenetti, David; Sanchez, Flavio

doi:10.1111/ocr.12542

Cited by 48 publications

(44 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It allows for automatic cephalometric point detection and tracing in seconds; however, the software requires that a lateral radiograph be obtained only on the Planmeca cephalometric imaging unit, where it is automatically calibrated, resized, and oriented [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

Cephalometric Analysis in Orthodontics Using Artificial Intelligence—A Comprehensive Review

Subramanian

Chen

Almalki

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

Artificial intelligence (AI) is a branch of science concerned with developing programs and computers that can gather data, reason about it, and then translate it into intelligent actions. AI is a broad area that includes reasoning, typical linguistic dispensation, machine learning, and planning. In the area of medicine and dentistry, machine learning is currently the most widely used AI application. This narrative review is aimed at giving an outline of cephalometric analysis in orthodontics using AI. Latest algorithms are developing rapidly, and computational resources are increasing, resulting in increased efficiency, accuracy, and reliability. Current techniques for completely automatic identification of cephalometric landmarks have considerably improved efficiency and growth prospects for their regular use. The primary considerations for effective orthodontic treatment are an accurate diagnosis, exceptional treatment planning, and good prognosis estimation. The main objective of the AI technique is to make dentists’ work more precise and accurate. AI is increasingly being used in the area of orthodontic treatment. It has been evidenced to be a time-saving and reliable tool in many ways. AI is a promising tool for facilitating cephalometric tracing in routine clinical practice and analyzing large databases for research purposes.

show abstract

Section: Discussionmentioning

confidence: 99%

Cephalometric Analysis in Orthodontics Using Artificial Intelligence—A Comprehensive Review

Subramanian

Chen

Almalki

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…Park et al (60,61) demonstrated a DL algorithm for the automatically identifying cephalometric landmarks on radiographs with a high accuracy. Bulatova (68) et al and Kunz et al (69) developed similar AI algorithms, with accuracies comparable with human examiners in identifying those landmarks. An automatic system for skeletal classification using lateral cephalometric radiographs was proposed by Yu et al (63).…”

Section: Ai In Orthodonticsmentioning

confidence: 99%

Artificial intelligence in dentistry—A review

Ding

Zhao

et al. 2023

Front. Dent. Med

View full text Add to dashboard Cite

Artificial Intelligence (AI) is the ability of machines to perform tasks that normally require human intelligence. AI is not a new term, the concept of AI can be dated back to 1950. However, it has not become a practical tool until two decades ago. Owing to the rapid development of three cornerstones of current AI technology—big data (coming through digital devices), computational power, and AI algorithm—in the past two decades, AI applications have been started to provide convenience to people's lives. In dentistry, AI has been adopted in all dental disciplines, i.e., operative dentistry, periodontics, orthodontics, oral and maxillofacial surgery, and prosthodontics. The majority of the AI applications in dentistry go to the diagnosis based on radiographic or optical images, while other tasks are not as applicable as image-based tasks mainly due to the constraints of data availability, data uniformity, and computational power for handling 3D data. Evidence-based dentistry (EBD) is regarded as the gold standard for the decision-making of dental professionals, while AI machine learning (ML) models learn from human expertise. ML can be seen as another valuable tool to assist dental professionals in multiple stages of clinical cases. This review narrated the history and classification of AI, summarised AI applications in dentistry, discussed the relationship between EBD and ML, and aimed to help dental professionals to understand AI as a tool better to assist their routine work with improved efficiency.

show abstract

“…Moreover, various AI models have been utilized to classify the normal and osteoporotic subjects, aiming to assist physicians in identifying patients with osteoporosis before implant treatment [24]. In terms of the clinical decision making, the AI-based clinical decision support systems have been developed for the diagnosis of oral and maxillofacial pain [14], the analysis of teeth extraction strategy in orthodontic treatment [25], the automatic identification of the landmark points in cephalometric [26], and the design of removable partial denture in oral prostheses [27].…”

Section: Related Workmentioning

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

Assessment of automatic cephalometric landmark identification using artificial intelligence

Cited by 48 publications

References 16 publications

Cephalometric Analysis in Orthodontics Using Artificial Intelligence—A Comprehensive Review

Cephalometric Analysis in Orthodontics Using Artificial Intelligence—A Comprehensive Review

Artificial intelligence in dentistry—A review

Assisting in Diagnosis of Temporomandibular Disorders: A Deep Learning Approach

Contact Info

Product

Resources

About