Integrating machine learning for treatment decisions in anterior open bite orthodontic cases: A retrospective study.

Journal: Journal of the World federation of orthodontists

Published Date: Jan 31, 2025

Abstract

INTRODUCTION: This article explores the integration of machine learning (ML) algorithms to aid in treatment planning and extraction decisions for anterior open bite cases, leveraging demographic, clinical, and radiographic data to predict treatment outcomes and informed decision-making.

Authors

Matthew Rhee

Department of Orthodontics, College of Dentistry, University of Illinois Chicago, Chicago, Illinois.
Mohammed H Elnagar
Veerasathpurush Allareddy

Department Head and Brodie Craniofacial Endowed Chair, Department of Orthodontics - University of Illinois at Chicago College of Dentistry, Chicago, IL, USA.
Omid Halimi Milani

Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, IL, USA.
Ahmet Enis Cetin

Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois, United States of America.
Flavio Jose Sanchez

Department of Orthodontics, College of Dentistry, University of Illinois Chicago, Chicago, Illinois. Electronic address: flavio1@uic.edu.

Keywords

Adolescent Adult Algorithms Cephalometry Child Clinical Decision-Making Female Humans Machine Learning Male Neural Networks, Computer Open Bite Patient Care Planning Retrospective Studies Support Vector Machine Tooth Extraction Treatment Outcome Young Adult

External Resources

View on PubMed Access via DOI PubMed (39893152)

Integrating machine learning for treatment decisions in anterior open bite orthodontic cases: A retrospective study.

Abstract

Authors

Keywords

External Resources

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