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Updated: May 5, 2026

Robust Ligature-Induced Model of Murine Periodontitis for the Evaluation of Oral Neutrophils
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Artificial intelligence models for periodontitis classification: A systematic review.

Jiaming Zhang1, Shuzhi Deng1, Ting Zou1

  • 1Shenzhen Stomatology Hospital (Pingshan) of Southern Medical University, Shenzhen, Guangdong, China; Shenzhen Clinical College of Stomatology, School of Stomatology, Southern Medical University, Shenzhen, Guangdong, China.

Journal of Dentistry
|March 19, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) models show promise in classifying periodontitis, achieving over 70% accuracy. Further development is needed to address data quality, interpretability, and ethical concerns for clinical use.

Keywords:
Artificial intelligenceClassificationConvolutional neural networksDeep learningMachine learningPeriodontitis

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Area of Science:

  • Dental diagnostics
  • Artificial intelligence in medicine
  • Periodontology

Background:

  • Periodontitis diagnosis presents challenges for dental practitioners.
  • Accurate classification is crucial for effective treatment planning.

Purpose of the Study:

  • To systematically review the performance of artificial intelligence (AI) models in classifying periodontitis.
  • To assess the diagnostic accuracy and potential clinical utility of AI in periodontitis detection.

Main Methods:

  • A systematic literature search was conducted across major databases up to April 2024.
  • Included studies focused on AI applications for periodontitis classification.
  • Data from 28 selected articles were analyzed, evaluating 10 mapping parameters.

Main Results:

  • AI models demonstrate diagnostic accuracy comparable to dentists, exceeding 70% for periodontitis classification.
  • Some AI models achieved diagnostic accuracies between 80-90%.
  • Accuracy varied depending on the stage of periodontitis.

Conclusions:

  • AI offers a novel and reliable approach to periodontitis classification, aiding diagnostic efficiency.
  • Key challenges include data quality, model interpretability, generalizability, and ethical considerations.
  • AI holds potential for improving dental diagnostics, telemedicine, and home self-testing.