International journal of dentistry · 2025
Artificial Intelligence-Assisted Longitudinal Analysis Reveals a Novel Rare Fainting Wisdom Tooth Phenomenon in Fully Dentate Mandibles.
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Gracea Rellyca Sola, Chopra Shivi, Östgren Peter, Benchimol Daniel, Jacobs Reinhilde
Аннотация
OBJECTIVES: This study aims to characterise the rare 'fainting wisdom tooth' phenomenon, defined as the angular transition of a mandibular wisdom tooth from an initially upright position to mesial or horizontal inclination during adolescence in fully dentate mandibles.
MATERIALS AND METHODS: A retrospective longitudinal analysis was conducted using panoramic radiographs from a pooled database of 11,932 patients. Fifty cases of fainting wisdom teeth (0.4% prevalence) were identified using a validated deep learning (DL) convolutional neural network (CNN)-based artificial intelligence (AI) tool and compared to 50 matched controls with vertically erupted wisdom teeth. Radiographic variables were measured at two time points: T1 (ages 8-15) and T2 (ages 16-23). Angular changes and anatomical features, including space/crown width ratio, tooth depth, proximity to the second molar and mandibular canal, cemento-enamel junction (CEJ)/apical width ratio and gonial angle, were assessed. Logistic regression was used to determine predictive factors associated with the fainting phenomenon, with results reported as odds ratios (ORs).
RESULTS: Fainting wisdom teeth showed a significant angular shift from a median of 29° at T1 to 83° at T2, while controls showed minimal change. Logistic regression identified tooth depth as the strongest predictor (OR = 0.1, p < 0.001), with additional risk factors including reduced space/crown width ratio, increased proximity to the second molar, higher CEJ/apical width ratio and increasing gonial angle.
CONCLUSION: This is the first study to describe and analyse the rare fainting wisdom tooth phenomenon using AI-assisted longitudinal analysis of panoramic radiographs. The findings suggest that early tooth positioning and surrounding anatomical constraints may interfere with vertical eruption and increase the risk of fainting.
MATERIALS AND METHODS: A retrospective longitudinal analysis was conducted using panoramic radiographs from a pooled database of 11,932 patients. Fifty cases of fainting wisdom teeth (0.4% prevalence) were identified using a validated deep learning (DL) convolutional neural network (CNN)-based artificial intelligence (AI) tool and compared to 50 matched controls with vertically erupted wisdom teeth. Radiographic variables were measured at two time points: T1 (ages 8-15) and T2 (ages 16-23). Angular changes and anatomical features, including space/crown width ratio, tooth depth, proximity to the second molar and mandibular canal, cemento-enamel junction (CEJ)/apical width ratio and gonial angle, were assessed. Logistic regression was used to determine predictive factors associated with the fainting phenomenon, with results reported as odds ratios (ORs).
RESULTS: Fainting wisdom teeth showed a significant angular shift from a median of 29° at T1 to 83° at T2, while controls showed minimal change. Logistic regression identified tooth depth as the strongest predictor (OR = 0.1, p < 0.001), with additional risk factors including reduced space/crown width ratio, increased proximity to the second molar, higher CEJ/apical width ratio and increasing gonial angle.
CONCLUSION: This is the first study to describe and analyse the rare fainting wisdom tooth phenomenon using AI-assisted longitudinal analysis of panoramic radiographs. The findings suggest that early tooth positioning and surrounding anatomical constraints may interfere with vertical eruption and increase the risk of fainting.