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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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Artificial Intelligence in Periodontology: A Systematic Review.

Antonin Tichy^1,2, Nils Werner¹, Helena Dujic¹

¹Department of Conservative Dentistry, Periodontology and Digital Dentistry, LMU University Hospital, LMU Munich, Munich, Germany.

Journal of Periodontal Research

|March 30, 2026

Summary

This summary is machine-generated.

Artificial intelligence (AI) shows promise in periodontology for diagnosing periodontitis from images and non-image data. However, generalizability is limited by data diversity and inconsistent reporting.

Keywords:

clinical decision support deep learning dental imaging diagnostic accuracy machine learning periodontitis risk prediction

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Area of Science:

Periodontology
Artificial Intelligence
Machine Learning
Radiology

Background:

Artificial intelligence (AI) is increasingly explored for applications in periodontology.
This review focuses on deep learning for image-based diagnosis and other AI applications in periodontal care.

Purpose of the Study:

To comprehensively review AI applications in periodontology.
To assess deep learning for image-based periodontitis diagnosis.
To explore non-image-based AI applications in periodontal care.

Main Methods:

Systematic review adhering to PRISMA guidelines.
Searched six major scientific databases.
Two parts: PICO 1 (deep learning on imaging data) with QUADAS-2 RoB assessment, and PICO 2 (non-imaging data) with narrative synthesis.

Main Results:

PICO 1: 29 studies, deep learning achieved high accuracy (81%-99%) for periodontitis classification on radiographs.
PICO 2: 65 studies, AI demonstrated effectiveness in diagnosis, risk stratification, progression modeling, and clinical data mining.
AI applications showed promising metrics, but risk of bias and applicability concerns were noted, particularly regarding dataset diversity.

Conclusions:

Generalizability is a key limitation due to data diversity, inconsistent metrics, and lack of external testing.
Future research should focus on multicenter evaluations and transparent reporting.
Prospective studies assessing workflow impact and patient outcomes are needed.