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Related Concept Videos

Teeth01:15

Teeth

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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.
In the bud stage, the tooth germ (an aggregation of cells) starts to form in the developing jawbone. During the cap stage, the tooth germ differentiates into enamel organ, dental papilla, and dental sac, which will later develop into the tooth's enamel, dentin...
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Prediction of Lower Third Molar Eruption in Panoramic Radiography Using Artificial Intelligence (AI): PDApp.

Susana Santeiro-Hermida1, Manuel Fernández-Delgado2, Eva Cernadas2

  • 1Digital Dentistry Unit, Stomatology Area, Department of Surgery and Medical-Surgery Specialities, School of Dentistry, Faculty of Medicine and Odontology, Universidade de Santiago de Compostela, Rúa Entrerríos s/n, 15782 Santiago de Compostela, Spain.

Diagnostics (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) using machine learning (ML) algorithms can predict third molar eruption. The PDApp software demonstrated high performance in diagnosing mandibular third molar eruption from panoramic radiography.

Keywords:
artificial intelligencemachine learningoral radiologypanoramic radiographysoftwarethird molar

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Area of Science:

  • Dentistry
  • Oral Surgery
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Predicting third molar eruption preoperatively is a significant challenge in dentistry and oral surgery.
  • Current methods often lack precision, necessitating improved diagnostic tools.

Purpose of the Study:

  • To develop and validate an AI-based tool for the preoperative prediction of third molar eruption.
  • To assess the performance of machine learning algorithms in analyzing panoramic radiography for eruption diagnosis.

Main Methods:

  • Machine learning (ML) algorithms were employed to analyze M3 panoramic radiological images.
  • The study utilized retrospective data from patients with confirmed mandibular third molar retention.
  • A dataset of 383 panoramic radiographies was used to train and validate the PDApp software.

Main Results:

  • The PDApp software demonstrated high performance in differentiating between third molar eruption and retention.
  • Clinical exploration confirmed the eruption diagnoses used for validation.
  • The AI tool showed promising results in preoperative diagnostic accuracy.

Conclusions:

  • The PDApp software, utilizing AI and ML, offers a high-performance solution for predicting mandibular third molar eruption.
  • This AI-driven approach can aid in preoperative diagnosis, potentially improving treatment planning in oral surgery.