Artificial Intelligence‐Assisted Clinical Decision Model for Managing Retained Second Deciduous Molars With No Permanent Successors
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2026
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| author | Ozge Colak William Tanberg Mohammed H. Elnagar Thikriat Al‐Jewair |
| author_facet | Ozge Colak William Tanberg Mohammed H. Elnagar Thikriat Al‐Jewair Ozge Colak William Tanberg Mohammed H. Elnagar Thikriat Al‐Jewair |
| collection | Wiley Open Access |
| contents | 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 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. 10.1111/ocr.70100 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1111/ocr.70100 |
| format | Artículo Open Access |
| id | wiley_oa_10_1111_ocr_70100 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2026 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | 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 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 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. 10.1111/ocr.70100 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Artificial Intelligence‐Assisted Clinical Decision Model for Managing Retained Second Deciduous Molars With No Permanent Successors |
| topic | Orthodontics & Craniofacial Research |
| url | https://onlinelibrary.wiley.com/doi/10.1111/ocr.70100 |