Early Diagnosis and Prediction of Skeletal Class III Malocclusion from Profile Photos Using Artificial Intelligence

Demircan, Gül Sude; Kiliç, Banu; Önal-Süzek, Tuğba

doi:10.1007/978-3-030-64610-3_50

“…Demircan et al predicted skeletal class III malocclusion using profile photographs by artificial intelligence [ 13 ].…”

Section: Reviewmentioning

confidence: 99%

Artificial Intelligence and Its Theranostic Applications in Dentistry

Mohan¹,

Fenn²

2023

Cureus

View full text Add to dashboard Cite

As new technologies emerge, they continue to have an impact on our daily lives, and artificial intelligence (AI) covers a wide range of applications. Because of the advancements in AI, it is now possible to analyse large amounts of data, which results in more accurate data and more effective decision-making. This article explains the fundamentals of AI and examines its development and present use. AI technology has had an impact on the healthcare sector as a result of the need for accurate diagnosis and improved patient care. An overview of the existing AI applications in clinical dentistry was provided. Comprehensive care involving artificial intelligence aims to provide cutting-edge research and innovations, as well as high-quality patient care, by enabling sophisticated decision support tools. The cornerstone of AI advancement in dentistry is creative inter-professional coordination among medical professionals, scientists, and engineers. Artificial intelligence will continue to be associated with dentistry from a wide angle despite potential misconceptions and worries about patient privacy. This is because precise treatment methods and quick data sharing are both essential in dentistry. Additionally, these developments will make it possible for patients, academicians, and healthcare professionals to exchange large data on health as well as provide insights that enhance patient care.

show abstract

“…Our previous study [13], implemented an unsupervised diagnostic method based on the angles computed for Turkish adult patients [14]. Our previous study's disadvantage was that it was only applicable to Turkish adult patients.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Three Machine Learning Models for Early Prediction of Skeletal Class-Iii Malocclusion From Profile Photos

Aksoy

¹

,

Kiliç

²

,

Önal-Süzek

³

2022

Mugla Journal of Science and Technology

2

0

View full text Add to dashboard Cite

Among skeletal deformities, Class III is the one that best time for the treatment is the pre-adolescent growth period. Diagnosis and treatment in this period continue to be a complex orthodontic problem. Class III malocclusion is especially difficult to treat with braces frequently requiring surgical intervention after pubertal growth spurt. In addition, delayed recognition of the problem will yield to significant functional, aesthetic and psychological concerns. In this study, we proposed a comparative analysis of three predictive models to predict Class III malocclusion: deep learning algorithm, machine learning algorithm and a rule-based algorithm. For this analysis, we collected a novel profile image data set along with their formal diagnosis from 435 orthodontics patients. The most successful method among the three was the machine learning method with an accuracy of %76.

show abstract

Comparative Analysis of three machine learning models for Early Prediction of Skeletal Class-III Malocclusion from Profile Photos

Aksoy

¹

,

Kiliç

²

,

Önal-Süzek

³

2022

Preprint

Self Cite

0

View full text Add to dashboard Cite

Among skeletal deformities, Class III is the one that best time for the treatment is the pre-adolescent growth period. Diagnosis and treatment in this period continue to be a complex orthodontic problem. Class III malocclusion is especially difficult to treat with braces frequently requiring surgical intervention after pubertal growth spurt. In addition, delayed recognition of the problem will yield to significant functional, aesthetic and psychological concerns. In this study, we proposed a comparative analysis of three predictive models to predict Class III malocclusion: deep learning algorithm, machine learning algorithm and a rule-based algorithm. For this analysis, we collected a novel profile image data set along with their formal diagnosis from 435 orthodontics patients. The most successful method among the three was the machine learning method with an accuracy of %76.

show abstract

Early Diagnosis and Prediction of Skeletal Class III Malocclusion from Profile Photos Using Artificial Intelligence

Cited by 4 publications

References 19 publications

Artificial Intelligence and Its Theranostic Applications in Dentistry

Artificial Intelligence and Its Theranostic Applications in Dentistry

Comparative Analysis of Three Machine Learning Models for Early Prediction of Skeletal Class-Iii Malocclusion From Profile Photos

Comparative Analysis of three machine learning models for Early Prediction of Skeletal Class-III Malocclusion from Profile Photos

Contact Info

Product

Resources

About