2026-02-24 バッファロー大学(UB)
<関連情報>
- https://www.buffalo.edu/news/releases/2026/02/Al-Jewair-study-AI-missing-teeth.html
- https://onlinelibrary.wiley.com/doi/abs/10.1111/ocr.70100
永久歯のない第二乳臼歯遺残の管理のための人工知能支援臨床意思決定モデル Artificial Intelligence-Assisted Clinical Decision Model for Managing Retained Second Deciduous Molars With No Permanent Successors
Ozge Colak, William Tanberg, Mohammed H. Elnagar, Thikriat Al-Jewair
Orthodontics & Craniofacial Research Published: 16 January 2026
DOI:https://doi.org/10.1111/ocr.70100
ABSTRACT
Introduction
The aim of this study was to develop and apply an artificial intelligence (AI) algorithm to aid the clinical decision-making process for managing mandibular retained second deciduous molars (SDM) with no permanent successors using machine learning.
Methods
This retrospective study consisted of patients who were diagnosed with at least one congenitally missing (agenic) mandibular permanent second premolar with a retained SDM. Pretreatment clinical records from each patient were collected and three sets of input features (radiographic, photographic and clinical) were used. The sample was divided into three groups, each representing a distinct treatment decision: (1) extraction of the SDM with space closure; (2) extraction of the SDM with space maintenance; and (3) retention of the SDM. The treatment decisions were based on majority treatment determination by three experienced clinicians. Four machine learning models were built and evaluated: Multinomial Logistic Regression, Multilayer Perceptron, Decision Tree and Random Forest classifier.
Results
Random Forest classifier showed the highest accuracy in treatment planning while Decision Tree showed the lowest accuracy. Features such as patient preference for restoration, amount of mandibular arch crowding and ankylosis were the strongest predictors, having the greatest influence on treatment decision accuracy in the Random Forest classifier model.
Conclusions
The Random Forest classifier demonstrated the highest accuracy in aiding the clinical decision-making process for managing retained SDM with no permanent successors. Key factors influencing treatment decision accuracy included patient preference for restoration, mandibular arch crowding and ankylosis.
