A machine learning model for orthodontic extraction/non-extraction decision in a racially and ethnically diverse patient population

Mason, T. F. D.; Kelly, Kevin C.; Eckert, George J.; Dean, Jeffrey A.; Dundar, M. Murat; Türkkahraman, Hakan

doi:10.1016/j.ortho.2023.100759

Cited by 10 publications

(9 citation statements)

References 29 publications

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“…For example, the five most recent publications originating from the United States on "Machine Learning in Orthodontics" have reported race in an inconsistent manner or have not reported it at all (Table 1). [33][34][35][36][37] In two of the five studies, information on race was missing in over 50% of subjects and information about ethnicity was missing for over 70% of subjects.…”

Section: Tr Aining Data S E Ts Us Ed For Ai/ Machine Le Arning Model Smentioning

confidence: 99%

“…There was also wide variation on how race was included or not included in the ML models. [33][34][35][36][37] Racial categories have been historically difficult to define and the inherent diversity within racial groups has not been well delineated and accounted for in prior research. 22 The effects of upstream factors such as social, behavioural, economical, educational and structural determinants of health that have a significant confounding effect on race have not been systematically studied and parsed out.…”

Section: Tr Aining Data S E Ts Us Ed For Ai/ Machine Le Arning Model Smentioning

confidence: 99%

“…24,29,30 Orthodontic datasets are hampered by considerable amounts of missing data, especially on race and socio-economic factors. [34][35][36][37] Researchers should consider using imputation strategies to account for missing information. Certain mix of variables such as insurance type, health status and socioeconomic status might be predictive of race and ethnicity and a two-staged model can be developed wherein a propensity scoring approach is developed to obtain a continuous or categorical score for missing race in the first stage.…”

Section: Reporting Of Ethnicity Race and Ethnicity Accounted For In T...mentioning

confidence: 99%

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Call for algorithmic fairness to mitigate amplification of racial biases in artificial intelligence models used in orthodontics and craniofacial health

Allareddy,

Oubaidin,

Rampa

et al. 2023

Orthod Craniofacial Res

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Machine Learning (ML), a subfield of Artificial Intelligence (AI), is being increasingly used in Orthodontics and craniofacial health for predicting clinical outcomes. Current ML/AI models are prone to accentuate racial disparities. The objective of this narrative review is to provide an overview of how AI/ML models perpetuate racial biases and how we can mitigate this situation. A narrative review of articles published in the medical literature on racial biases and the use of AI/ML models was undertaken. Current AI/ML models are built on homogenous clinical datasets that have a gross underrepresentation of historically disadvantages demographic groups, especially the ethno‐racial minorities. The consequence of such AI/ML models is that they perform poorly when deployed on ethno‐racial minorities thus further amplifying racial biases. Healthcare providers, policymakers, AI developers and all stakeholders should pay close attention to various steps in the pipeline of building AI/ML models and every effort must be made to establish algorithmic fairness to redress inequities.

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Section: Tr Aining Data S E Ts Us Ed For Ai/ Machine Le Arning Model Smentioning

confidence: 99%

Section: Tr Aining Data S E Ts Us Ed For Ai/ Machine Le Arning Model Smentioning

confidence: 99%

Section: Reporting Of Ethnicity Race and Ethnicity Accounted For In T...mentioning

confidence: 99%

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Call for algorithmic fairness to mitigate amplification of racial biases in artificial intelligence models used in orthodontics and craniofacial health

Allareddy,

Oubaidin,

Rampa

et al. 2023

Orthod Craniofacial Res

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“… 1 2 3 4 By augmenting the diagnostic process, AI empowers dentists to make informed decisions and devise personalized treatment plans, ultimately enhancing patient outcomes. 5 6 7 8 9 However, as we embark on this transformative journey, we must address concerns surrounding patient privacy, data security, and the responsible use of AI-generated insights.…”

Section: A Paradigm Shift In Diagnosis and Treatmentmentioning

confidence: 99%

Embracing the Unprecedented Pace of Change: Artificial Intelligence's Impact on Dentistry and Beyond

Türkkahraman

2023

Eur J Dent

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“…AI and ML are being increasingly applied in various areas of orthodontics to improve diagnostics, treatment planning, and patient care. Most current applications of this new technology have focused on image analysis and diagnosis [22][23][24][25][26], orthodontic/orthognathic decisionmaking processes and treatment planning [27][28][29][30][31][32][33][34][35][36][37][38], and growth prediction [39,40]. In an early study to test the ability of AI and ML to predict mandibular growth, Jiwa et al [41] sought to train a deep learning algorithm to predict mandibular growth.…”

Section: Introductionmentioning

confidence: 99%

Short- and Long-Term Prediction of the Post-Pubertal Mandibular Length and Y-Axis in Females Utilizing Machine Learning

Parrish,

O’Connell,

Eckert

et al. 2023

Diagnostics

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The aim of this study was to create a novel machine learning (ML) algorithm for predicting the post-pubertal mandibular length and Y-axis in females. Cephalometric data from 176 females with Angle Class I occlusion were used to train and test seven ML algorithms. For all ML methods tested, the mean absolute errors (MAEs) for the 2-year prediction ranged from 2.78 to 5.40 mm and 0.88 to 1.48 degrees, respectively. For the 4-year prediction, MAEs of mandibular length and Y-axis ranged from 3.21 to 4.00 mm and 1.19 to 5.12 degrees, respectively. The most predictive factors for post-pubertal mandibular length were mandibular length at previous timepoints, age, sagittal positions of the maxillary and mandibular skeletal bases, mandibular plane angle, and anterior and posterior face heights. The most predictive factors for post-pubertal Y-axis were Y-axis at previous timepoints, mandibular plane angle, and sagittal positions of the maxillary and mandibular skeletal bases. ML methods were identified as capable of predicting mandibular length within 3 mm and Y-axis within 1 degree. Compared to each other, all of the ML algorithms were similarly accurate, with the exception of multilayer perceptron regressor.

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A machine learning model for orthodontic extraction/non-extraction decision in a racially and ethnically diverse patient population

Cited by 10 publications

References 29 publications

Call for algorithmic fairness to mitigate amplification of racial biases in artificial intelligence models used in orthodontics and craniofacial health

Call for algorithmic fairness to mitigate amplification of racial biases in artificial intelligence models used in orthodontics and craniofacial health

Embracing the Unprecedented Pace of Change: Artificial Intelligence's Impact on Dentistry and Beyond

Short- and Long-Term Prediction of the Post-Pubertal Mandibular Length and Y-Axis in Females Utilizing Machine Learning

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