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Updated: Jan 21, 2026

The Establishment of a Murine Mandibular Molar Extraction Socket Healing Model
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Un modelo predictivo basado en aprendizaje automático para la dificultad de extracción del tercer molar mandibular:

Piaopiao Qiu1, Jiaqi Huang1, Huasheng Zhang1

  • 1Shanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology & Department of Oral and Maxillofacial Surgery, Shanghai Tongji Stomatological Hospital and Dental School, Tongji University, 399 Yanchang Middle Road, Jing'an District, Shanghai, Asia, 200092, China.

BMC oral health
|January 19, 2026
PubMed
Resumen

Los modelos de aprendizaje automático predicen con precisión la dificultad de extracción del tercer molar mandibular utilizando datos de tomografía computarizada de haz cónico (CBCT). Las características morfológicas, como la angulación del diente, fueron predictores clave, superando a los médicos jóvenes.

Palabras clave:
dificultad de extracción del tercer molar mandibularevaluación preoperatoriaaprendizaje automáticoparámetros multimodales

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Área de la Ciencia:

  • Cirugía Oral y Maxilofacial
  • Imagenología Dental
  • Aprendizaje Automático en Medicina

Sus antecedentes:

  • La predicción de la dificultad de extracción del tercer molar mandibular (MM3) es crucial para la planificación quirúrgica.
  • Los métodos actuales a menudo se basan en el juicio clínico subjetivo, lo que genera variabilidad.

Objetivo del estudio:

  • Desarrollar un modelo predictivo rápido y preciso para la dificultad de extracción de MM3.
  • Integrar el aprendizaje automático con parámetros multimodales, incluidas las imágenes de CBCT.

Principales métodos:

  • Se creó un conjunto de datos que combina datos clínicos y características morfológicas automatizadas de CBCT.
  • Se entrenaron y optimizaron seis modelos de aprendizaje automático (SVM, ANN, XGBoost, RF, KNN, Regresión Logística).
  • Se utilizaron análisis SHAP y RFE para la importancia de las características y la validación del modelo.

Principales resultados:

  • El modelo XGBoost logró la mayor precisión predictiva (88,24%), superando a los médicos jóvenes (83,53%).
  • Las características morfológicas, en particular la angulación del diente adyacente, el área de contacto y el volumen de MM3, fueron predictores dominantes.
  • Los factores clínicos como el fibrinógeno y el tiempo de protrombina también contribuyeron a las predicciones.

Conclusiones:

  • La integración de características morfológicas y clínicas mejora significativamente la precisión predictiva de la dificultad de extracción de MM3.
  • La resistencia del diente adyacente surgió como el factor más influyente, seguido de la resistencia ósea y la proximidad del canal mandibular.