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Related Concept Videos

Teeth01:15

Teeth

448
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...
448
Tooth Anatomy01:21

Tooth Anatomy

507
The human tooth enables us to eat a variety of foods, speak clearly, and even aid in shaping our faces. Teeth are composed of various elements that work together. Here's a detailed look at the anatomy of a human tooth.
The Crown, Neck, and Root
The visible part of the tooth is referred to as the crown. It's covered by enamel, the hardest substance in the human body. The crown is uniquely shaped for each type of tooth, allowing for different functions such as cutting, tearing, or...
507

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Related Experiment Video

Updated: Jul 12, 2025

Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment
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Predicting Dental Caries Outcomes in Young Adults Using Machine Learning Approach.

Chukwuebuka Ogwo1, Brown Grant2, John Warren3

  • 1Temple University Kornberg School of Dentistry.

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Summary
This summary is machine-generated.

Machine learning accurately predicts dental caries in young adults using longitudinal data. Previous caries experience and sugar-sweetened beverage intake are key predictors for targeted interventions.

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Area of Science:

  • Oral health research
  • Biostatistics
  • Public health

Background:

  • Dental caries remains a significant public health issue, particularly among young adults.
  • Predicting caries development requires understanding complex interactions of various risk and protective factors over time.

Conclusions:

  • Machine learning models can accurately predict caries in young adults using longitudinal data.
  • Identified predictors like prior caries and beverage intake can inform targeted public health interventions.
  • Further validation with diverse populations is necessary to enhance model generalizability and clinical utility.