Trends and Application of Artificial Intelligence Technology in Orthodontic Diagnosis and Treatment Planning—A Review

Albalawi, Farraj; Alamoud, Khalid

doi:10.3390/app122211864

Cited by 10 publications

(2 citation statements)

References 83 publications

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“…Based on information from treatment plans, imaging data, and patient profiles, ML can help orthodontists diagnose orthodontic characteristics and predict treatment result [5]. Using ML in orthodontics has various benefits, such as creating individualized treatment plans for each patient based on their features, bone structure, and dental records [7]. As a consequence, therapy results are more consistent and efficient, giving patients improved outcomes.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in Orthodontics: A Bibliometric Analysis

Dashti,

Zare,

Tajbakhsh

et al. 2024

AAIML

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Background: Machine learning (ML), a facet of artificial intelligence, utilizes algorithms to learn from data without explicit programming. In orthodontics, ML offers advantages like tailoring personalized treatment plans for patients. Despite its potential, there hasn’t been a bibliometric analysis of ML studies in orthodontics. This study aims to fill that gap. Types of studies reviewed: Articles on ML in orthodontics were reviewed from Web of Science Core Collection, Embase, Scopus, and PubMed. Data on journal details, country of origin, publication month, citations, keywords, and co-authorship were extracted. Results: The search retrieved a total of 1478 articles, of which 701 were excluded. American Journal of Orthodontics and Dentofacial Orthopedics has published the most articles (3.6%), followed by the seminars in Orthodontics Journal (1.6%), and Orthodontics and Craniofacial Research Journal (1.6%). Most of the articles were from researchers from China (n = 156), the United States (n = 107), and South Korea (n = 70). The number of citations of the published articles ranged from 0 to 702, with most articles (75.54%) having at least one citation. Science Mapping analysis revealed that the most used keywords were Human(s) (n = 484), Artificial intelligence (n = 194), Female (n=169), Male (n = 161), and Cephalometry (n = 151). Clinical implications: Clinicians should be aware of the emerging global collaborative landscape in machine learning trends, stay informed about technological advancements, and consider the potential impact of ML on patient care and treatment outcomes in their practices.

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Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in Orthodontics: A Bibliometric Analysis

Dashti,

Zare,

Tajbakhsh

et al. 2024

AAIML

View full text Add to dashboard Cite

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“…In orthodontics, AI was initially utilised for radiographic landmark tracing, as well as for the determination of the patient’s skeletal classification from lateral cephalograms (Mohammad-Rahimi et al, 2021). Nowadays, new machine models have been used as a tool for treatment decision making, such as the determination of extraction/non-extraction decisions, extraction patterns and anchorage patterns (Albalawi and Alamoud, 2022; Etemad et al, 2021; Hung et al, 2020; Li et al, 2019; Khanagar et al, 2021b). Recently, promising applications of AI have emerged for patients receiving orthognathic treatment (OGT).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for treatment planning and soft tissue outcome prediction of orthognathic treatment: A systematic review

Salazar,

Rossouw,

Javed

et al. 2023

J Orthod

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Background: The accuracy of artificial intelligence (AI) in treatment planning and outcome prediction in orthognathic treatment (OGT) has not been systematically reviewed. Objectives: To determine the accuracy of AI in treatment planning and soft tissue outcome prediction in OGT. Design: Systematic review. Data sources: Unrestricted search of indexed databases and reference lists of included studies. Data selection: Clinical studies that addressed the focused question ‘Is AI useful for treatment planning and soft tissue outcome prediction in OGT?’ were included. Data extraction: Study screening, selection and data extraction were performed independently by two authors. The risk of bias (RoB) was assessed using the Cochrane Collaboration’s RoB and ROBINS-I tools for randomised and non-randomised clinical studies, respectively. Data synthesis: Eight clinical studies (seven retrospective cohort studies and one randomised controlled study) were included. Four studies assessed the role of AI for treatment decision making; and four studies assessed the accuracy of AI in soft tissue outcome prediction after OGT. In four studies, the level of agreement between AI and non-AI decision making was found to be clinically acceptable (at least 90%). In four studies, it was shown that AI can be used for soft tissue outcome prediction after OGT; however, predictions were not clinically acceptable for the lip and chin areas. All studies had a low to moderate RoB. Limitations: Due to high methodological inconsistencies among the included studies, it was not possible to conduct a meta-analysis and reporting biases assessment. Conclusion: AI can be a useful aid to traditional treatment planning by facilitating clinical treatment decision making and providing a visualisation tool for soft tissue outcome prediction in OGT. Registration: PROSPERO CRD42022366864.

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Artificial intelligence-based automated model for prediction of extraction using neural network machine learning: A scope and performance analysis

Trehan,

Bhanotia,

Shaikh

et al. 2023

JCO

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To compare the Artificial Intelligence based model & conventional technique for prediction of extraction in orthodontic treatment plan. A comparative study was conducted on total 700 patients, who were divided into training set and testing set based on simple random sampling by means of computer generated random numbers. The photographs of the 630 patients [training set] along with the treatment plan finalized for them based on Arch Perimeter & Carey’s Analysis, was fed in the AI model [convolutional neural network (ResNet-50)] in order to train it for the stipulated function of eventually predicting the treatment plan in the testing set [70 patients], based on the input of the right profile photographs. The accuracy of measurement of the parameters of these seventy test set patients by the machine learning model relative to the manual method was compared eventually. Using the Statistical Package for Social Sciences, the acquired data was statistically analyzed, and p <0.05 was deemed statistically significant. The normality of the data was examined using the Shapiro-Wilks test and the Kolmogorov-Smirnov test. Depending on the collected data and normality assessed, appropriate reliability was estimated. The analysis of 70 test patients showed that 65.12% of the total extraction cases and 62.96% of the total non-extraction cases (as predicted by the AI model) were in agreement with the results of the model analysis.It is suggested that the present AI model can further be developed in order to improve the accuracy of prediction.

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Trends and Application of Artificial Intelligence Technology in Orthodontic Diagnosis and Treatment Planning—A Review

Cited by 10 publications

References 83 publications

Application of Machine Learning in Orthodontics: A Bibliometric Analysis

Application of Machine Learning in Orthodontics: A Bibliometric Analysis

Artificial intelligence for treatment planning and soft tissue outcome prediction of orthognathic treatment: A systematic review

Artificial intelligence-based automated model for prediction of extraction using neural network machine learning: A scope and performance analysis

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