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Teeth

Teeth

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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Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities

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A Roadmap towards Precision Periodontics.

Mia Rakic¹, Natasa Pejcic², Neda Perunovic³

¹ETEP (Etiology and Therapy of Periodontal Diseases) Research Group, Faculty of Dentistry, University Complutense of Madrid, Pza. Ramón y Cajal s/n, 28040 Madrid, Spain.

Medicina (Kaunas, Lithuania)

|April 3, 2021

Summary

This summary is machine-generated.

Periodontitis, a common condition, requires better diagnostics. Biomarkers and machine learning are key to advancing precision periodontics for improved patient outcomes.

Keywords:

algorithms biomarkers diagnosis machine learning periodontitis precision medicine

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Area of Science:

Periodontology
Biomarker research
Precision medicine

Background:

Periodontitis is a prevalent global health issue with significant socioeconomic impact.
Current periodontal diagnostics rely on clinical and radiological parameters, offering limited therapeutic guidance.
Periodontitis-associated systemic inflammation necessitates advanced prevention and management strategies.

Purpose of the Study:

To review unmet diagnostic needs in periodontitis.
To explore the concept and potential of precision periodontics.
To discuss the role of biomarkers and machine learning in advancing periodontal diagnostics.

Main Methods:

This study is a narrative review.
It synthesizes current literature on periodontal diagnostics, biomarkers, and precision medicine.
It outlines a roadmap for implementing precision periodontics.

Main Results:

Biomarkers have been introduced in recent classifications as a step toward precision periodontics.
The integration of biomarkers and machine learning is crucial for precision medicine in periodontology.
Validated periodontal diagnostic markers are currently lacking, hindering precision medicine implementation.

Conclusions:

Precision periodontics, driven by biomarkers and machine learning, holds promise for improved diagnosis and treatment.
Addressing unmet diagnostic needs is essential for advancing periodontal care.
A strategic roadmap is proposed for integrating precision medicine into periodontal practice.