Building and Evaluating an Orthodontic Natural Language Processing Model for Automated Clinical Note Information Extraction.

Journal: Orthodontics & craniofacial research
Published Date: Jun 14, 2025

Abstract

INTRODUCTION: Malocclusion presents functional and aesthetic challenges, necessitating accurate diagnosis and treatment. However, variability in orthodontic treatment planning persists due to subjective assessments, limiting consistency and objectivity. Electronic dental records (EDRs) contain vast patient data that could address these challenges, but much of the rich clinical information is documented as free text, complicating analysis. This study aims to develop an Orthodontic Natural Language Processing (ONLP) model to extract structured orthodontics-related information from unstructured EDRs and identify critical features influencing malocclusion using machine learning (ML).

Authors

  • Jay S Patel
    Department of Health Services Administration and Policy College of Public Health, Temple University, Philadelphia, PA.
  • Divakar Karanth
    Department of Orthodontics, University of Florida College of Dentistry, Gainesville, Florida, USA.

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