Artificial neural network (ANN) modeling and analysis for the prediction of change in the lip curvature following extraction and non-extraction orthodontic treatment

Nanda, Sarita; Kalha, Anmol S; Jena, Ashok Kumar; Bhatia, Virag; Mishra, Sumita

doi:10.5958/2393-9834.2015.00002.9

Cited by 10 publications

(11 citation statements)

References 10 publications

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“…[47] Recently, ANN was used to predict the change in lip curvature after orthodontic treatment with or without extractions. [48] Its prediction of change and the actual change that occurred differed by 29.6% and 7% for the upper and lower lips, respectively. Both the predictions were much better than those based on linear regression.…”

Section: Ai and Orthodontic Treatment Outcomesmentioning

confidence: 94%

Introduction to artificial intelligence and machine learning into orthodontics: A review

Kondody

Patil

Devika

et al. 2022

APOS

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Over the past few years, artificial intelligence (AI) and machine learning (ML) have revolutionized different healthcare branches, including dentistry. AI in a wider aspect means computers that mimic or behave like human intelligence whereas ML forms a part of AI and enables machines to increase their capabilities by the process of self-adapting algorithms. AI models’ basic principles or fundamentals are purely based on artificial neural networks or convolutional neural networks. This review focuses on giving a comprehensive and detailed explanation about AI and ML technology and their wide range of applications in various sections of orthodontic practice.

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Section: Ai and Orthodontic Treatment Outcomesmentioning

confidence: 94%

Introduction to artificial intelligence and machine learning into orthodontics: A review

Kondody

Patil

Devika

et al. 2022

APOS

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show abstract

“…Recently, ANN was used to predict the change in lip curvature after orthodontic treatment with or without extractions. [46] Its prediction of change and the actual change that occurred differed by 29.6% and 7% for the upper and lower lips, respectively. Both predictions were much better than those based on linear regression.…”

Section: And Treatment Outcomesmentioning

confidence: 94%

Applications of artificial intelligence and machine learning in orthodontics

Asiri

Tadlock

Schneiderman

et al. 2020

APOS

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Over the past two decades, artificial intelligence (AI) and machine learning (ML) have undergone considerable development. There have been various applications in medicine and dentistry. Their application in orthodontics has progressed slowly, despite promising results. The available literature pertaining to the orthodontic applications of AI and ML has not been adequately synthesized and reviewed. This review article provides orthodontists with an overview of AI and ML, along with their applications. It describes state-of-the-art applications in the areas of orthodontic diagnosis, treatment planning, growth evaluations, and in the prediction of treatment outcomes. AI and ML are powerful tools that can be utilized to overcome some of the clinical problems that orthodontists face daily. With the availability of more data, better AI and ML systems should be expected to be developed that will help orthodontists practice more efficiently and improve the quality of care.

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“…Many reports have highlighted the usefulness and potential of predictive electronic systems in clinical and research orthodontics [ 23 , 24 , 25 , 26 , 27 ]. The potential identified in these reports is similar to what has been hypothesized in medical practice—the records of the best clinical decisions made by thousands of professionals must be exploited to optimize patient care [ 28 , 29 , 30 ].…”

Section: Challenging Interface Between Machine Learning Models and Or...mentioning

confidence: 99%

“…Given these premises, one may suspect that the procedural ML logic applied to orthodontics may pose more than one interpretive problem. However, sufficient evidence has accumulated that ML tools, when applied to orthodontics, perform well [ 22 , 23 , 24 , 25 , 26 ].…”

Section: Challenging Interface Between Machine Learning Models and Or...mentioning

confidence: 99%

“…Concerns have arisen regarding the possibility of incorporating “dirty” data and spurious correlations based on uncertain clinical interpretations [ 22 ]. Some practitioners believe that using computational systems could lead to the establishment of a new, problematic medical empiricism, based not on concrete facts and data relating to patients but rather on interconnections of data, meaning that the most straightforward clinical situations can become complicated and confusing [ 23 ]. Is there any truth to these claims?…”

Section: Introductionmentioning

confidence: 99%

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The Validity of Machine Learning Procedures in Orthodontics: What Is Still Missing?

Auconi¹,

Gili

Capuani

et al. 2022

JPM

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Artificial intelligence (AI) models and procedures hold remarkable predictive efficiency in the medical domain through their ability to discover hidden, non-obvious clinical patterns in data. However, due to the sparsity, noise, and time-dependency of medical data, AI procedures are raising unprecedented issues related to the mismatch between doctors’ mentalreasoning and the statistical answers provided by algorithms. Electronic systems can reproduce or even amplify noise hidden in the data, especially when the diagnosis of the subjects in the training data set is inaccurate or incomplete. In this paper we describe the conditions that need to be met for AI instruments to be truly useful in the orthodontic domain. We report some examples of computational procedures that are capable of extracting orthodontic knowledge through ever deeper patient representation. To have confidence in these procedures, orthodontic practitioners should recognize the benefits, shortcomings, and unintended consequences of AI models, as algorithms that learn from human decisions likewise learn mistakes and biases.

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Artificial neural network (ANN) modeling and analysis for the prediction of change in the lip curvature following extraction and non-extraction orthodontic treatment

Cited by 10 publications

References 10 publications

Introduction to artificial intelligence and machine learning into orthodontics: A review

Introduction to artificial intelligence and machine learning into orthodontics: A review

Applications of artificial intelligence and machine learning in orthodontics

The Validity of Machine Learning Procedures in Orthodontics: What Is Still Missing?

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