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The formation of teeth, also known as odontogenesis, is a complex process that begins in utero, around the sixth week of embryonic development. There are three stages to this process: the bud stage, the cap stage, and the bell stage.
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Artificial Intelligence-Assisted Clinical Decision Model for Managing Retained Second Deciduous Molars With No

Ozge Colak1, William Tanberg2, Mohammed H Elnagar3

  • 1Private Practice, Katy, Texas, USA.

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|January 16, 2026
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Summary

An artificial intelligence (AI) algorithm using machine learning accurately aids decisions for managing mandibular retained second deciduous molars (SDM) without permanent successors. Patient preference, crowding, and ankylosis were key factors influencing AI treatment accuracy.

Keywords:
artificial intelligencemachine learningorthodonticssecond deciduous molarstreatment planning

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Area of Science:

  • Dentistry
  • Artificial Intelligence
  • Machine Learning

Background:

  • Managing mandibular retained second deciduous molars (SDM) lacking permanent successors presents clinical challenges.
  • Artificial intelligence (AI) and machine learning offer potential solutions for optimizing treatment decisions.

Purpose of the Study:

  • To develop and apply an AI algorithm to assist in clinical decision-making for managing mandibular retained SDM.
  • To evaluate the efficacy of machine learning models in predicting optimal treatment strategies.

Main Methods:

  • A retrospective study analyzed patient records with congenitally missing mandibular permanent second premolars and retained SDM.
  • Input features included radiographic, photographic, and clinical data.
  • Four machine learning models (Multinomial Logistic Regression, Multilayer Perceptron, Decision Tree, Random Forest) were trained and evaluated.

Main Results:

  • The Random Forest classifier achieved the highest accuracy in treatment planning.
  • The Decision Tree model demonstrated the lowest accuracy.
  • Strongest predictors for treatment decision accuracy included patient preference for restoration, mandibular arch crowding, and ankylosis.

Conclusions:

  • The Random Forest classifier is highly accurate in aiding clinical decisions for managing retained SDM.
  • Patient preference, mandibular arch crowding, and ankylosis significantly influence treatment decision accuracy.