AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning—A Comprehensive Review

Kazimierczak, Natalia; Kazimierczak, Wojciech; Serafin, Zbigniew; Nowicki, Paweł; Nożewski, Jakub; Janiszewska-Olszowska, Joanna

doi:10.3390/jcm13020344

Cited by 14 publications

(11 citation statements)

References 175 publications

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“…With the increasing availability of smart mobile devices capable of capturing 3D images, we expect the utilization of 3D measurements, such as area and volume, to become more widespread for facial analysis and, in turn, for facial surgeries [65]. The Face Analyzer web-based software is well suited for this purpose as it provides a reliable and accurate means of measuring the facial area and volume, which are essential parameters for many facial surgical procedures [66].…”

Section: Discussionmentioning

confidence: 99%

Reliability and Agreement of Free Web-Based 3D Software for Computing Facial Area and Volume Measurements

Topsakal,

Sawyer,

Akinci

et al. 2024

BioMedInformatics

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Background: Facial surgeries require meticulous planning and outcome assessments, where facial analysis plays a critical role. This study introduces a new approach by utilizing three-dimensional (3D) imaging techniques, which are known for their ability to measure facial areas and volumes accurately. The purpose of this study is to introduce and evaluate a free web-based software application designed to take area and volume measurements on 3D models of patient faces. Methods: This study employed the online facial analysis software to conduct ten measurements on 3D models of subjects, including five measurements of area and five measurements of volume. These measurements were then compared with those obtained from the established 3D modeling software called Blender (version 3.2) using the Bland–Altman plot. To ensure accuracy, the intra-rater and inter-rater reliabilities of the web-based software were evaluated using the Intraclass Correlation Coefficient (ICC) method. Additionally, statistical assumptions such as normality and homoscedasticity were rigorously verified before analysis. Results: This study found that the web-based facial analysis software showed high agreement with the 3D software Blender within 95% confidence limits. Moreover, the online application demonstrated excellent intra-rater and inter-rater reliability in most analyses, as indicated by the ICC test. Conclusion: The findings suggest that the free online 3D software is reliable for facial analysis, particularly in measuring areas and volumes. This indicates its potential utility in enhancing surgical planning and evaluation in facial surgeries. This study underscores the software’s capability to improve surgical outcomes by integrating precise area and volume measurements into facial surgery planning and assessment processes.

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Section: Discussionmentioning

confidence: 99%

Reliability and Agreement of Free Web-Based 3D Software for Computing Facial Area and Volume Measurements

Topsakal,

Sawyer,

Akinci

et al. 2024

BioMedInformatics

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“…The need for an enhanced analytical tool has gotten to the point where traditional tools are no longer adequate. 6 A three-dimensional subject is depicted in two dimensions in radiography. The overlapping of structures makes it difficult for the clinician to understand.…”

Section: Role Of Fem In Orthodonticsmentioning

confidence: 99%

Finite element analysis-A biomechanical tool in orthodontics

Gandhi,

Singh,

Fatima

et al. 2024

IJODR

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In orthodontic research, the finite element method (FEM) has been widely used as an engineering resource for calculating the stress and deformation of complex structures. Applying the FEM can predict the graphic representation of these tissue responses through the observation of areas of stress created by applied orthodontic mechanics. This method has the advantage of being non-invasive, accurate, and providing quantitative and detailed data on the physiological reactions possible to occur in tissues. The purpose of this article is to review and discuss the steps involved in applying the concept of finite elements and how they can be used in orthodontics. The stress distribution at the interface between the alveolar bone and the periodontal ligament, as well as the shifting trend in different tooth movement types while employing different kinds of orthodontic devices, may both be assessed using FEM. For this reason, expertise with certain software is required. Despite the drawbacks of other experimental techniques, FEM is a crucial technique for addressing inquiries regarding tooth movement.

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“…The recent boom in the utilization of artificial intelligence (AI) tools in medicine has not bypassed dentistry, finding particular relevance in the field of dentomaxillofacial radiology [27][28][29]. The ever-increasing number of radiological examinations [30], coupled with the increasing work burden on practitioners, has spurred the development of tools to facilitate radiological diagnostics.…”

Section: Introductionmentioning

confidence: 99%

Periapical Lesions in Panoramic Radiography and CBCT Imaging—Assessment of AI’s Diagnostic Accuracy

Kazimierczak,

Wajer,

Wajer

et al. 2024

JCM

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Background/Objectives: Periapical lesions (PLs) are frequently detected in dental radiology. Accurate diagnosis of these lesions is essential for proper treatment planning. Imaging techniques such as orthopantomogram (OPG) and cone-beam CT (CBCT) imaging are used to identify PLs. The aim of this study was to assess the diagnostic accuracy of artificial intelligence (AI) software Diagnocat for PL detection in OPG and CBCT images. Methods: The study included 49 patients, totaling 1223 teeth. Both OPG and CBCT images were analyzed by AI software and by three experienced clinicians. All the images were obtained in one patient cohort, and findings were compared to the consensus of human readers using CBCT. The AI’s diagnostic accuracy was compared to a reference method, calculating sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Results: The AI’s sensitivity for OPG images was 33.33% with an F1 score of 32.73%. For CBCT images, the AI’s sensitivity was 77.78% with an F1 score of 84.00%. The AI’s specificity was over 98% for both OPG and CBCT images. Conclusions: The AI demonstrated high sensitivity and high specificity in detecting PLs in CBCT images but lower sensitivity in OPG images.

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AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning—A Comprehensive Review

Cited by 14 publications

References 175 publications

Reliability and Agreement of Free Web-Based 3D Software for Computing Facial Area and Volume Measurements

Reliability and Agreement of Free Web-Based 3D Software for Computing Facial Area and Volume Measurements

Finite element analysis-A biomechanical tool in orthodontics

Periapical Lesions in Panoramic Radiography and CBCT Imaging—Assessment of AI’s Diagnostic Accuracy

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