Artificial intelligence models for periodontitis classification: A systematic review

IF 5.5 2区医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE Journal of dentistry Pub Date : 2025-05-01 Epub Date: 2025-03-17 DOI:10.1016/j.jdent.2025.105690

Jiaming Zhang , Shuzhi Deng , Ting Zou , Zuolin Jin , Shan Jiang

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Abstract

Objectives

The graded diagnosis of periodontitis has always been a difficulty for dentists. This systematic review aimed to investigate the performance of artificial intelligence (AI) models for periodontitis classification.

Data

This review includes original studies that explore the application of AI in periodontitis classification systems.

Sources

Two reviewers independently conducted a comprehensive search of literature published up to April 2024 in databases including PubMed, Web of Science, MEDLINE, Scopus, and Cochrane Library.

Study selection

A total of 28 articles were eventually included in this study, from which 10 mapping parameters were extracted and evaluated separately for each article.

Results

AI's diagnostic capabilities are comparable to those of a general dentist/periodontist, achieving an overall diagnostic accuracy rate of over 70 % for periodontitis classification, with some reaching 80–90 %. Variations in diagnosis accuracy rates were observed across different stages of periodontitis.

Conclusions

The AI model provides a novel and relatively reliable method for periodontitis classification. However, several key issues remain to be addressed, including access to and quality of data, interpretation of the decision-making process of the model, the ability of the model to generalize, and ethical and privacy considerations.

Clinical significance

The development of AI models for periodontitis classification is expected to assist dentists in improving diagnostic efficiency and enhancing diagnostic accuracy, and further development is expected to assist telemedicine and home self-testing.

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牙周炎分类的人工智能模型：系统综述。

目的：牙周炎的分级诊断一直是困扰牙医的难题。本系统综述旨在探讨人工智能（AI）模型在牙周炎分类中的性能。资料：本综述包括探索人工智能在牙周炎分类系统中的应用的原始研究。来源：两位审稿人独立地对截至2024年4月在PubMed、Web of Science、MEDLINE、Scopus和Cochrane Library等数据库中发表的文献进行了全面检索。研究选择：本研究最终共纳入28篇文章，从中提取10个映射参数，分别对每篇文章进行评价。结果：人工智能的诊断能力与普通牙医/牙周病医生相当，对牙周炎分类的总体诊断准确率超过70%，有的达到80-90%。不同牙周炎阶段的诊断准确率存在差异。结论：人工智能模型为牙周炎分类提供了一种新颖可靠的方法。然而，仍有几个关键问题有待解决，包括数据的访问和质量、模型决策过程的解释、模型的泛化能力以及道德和隐私考虑。临床意义：牙周炎分类人工智能模型的开发有望帮助牙医提高诊断效率和提高诊断准确性，并有望进一步发展辅助远程医疗和家庭自检。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of dentistry 医学-牙科与口腔外科

CiteScore

7.30

自引率

11.40%

发文量

349

审稿时长

35 days

期刊介绍： The Journal of Dentistry has an open access mirror journal The Journal of Dentistry: X, sharing the same aims and scope, editorial team, submission system and rigorous peer review. The Journal of Dentistry is the leading international dental journal within the field of Restorative Dentistry. Placing an emphasis on publishing novel and high-quality research papers, the Journal aims to influence the practice of dentistry at clinician, research, industry and policy-maker level on an international basis. Topics covered include the management of dental disease, periodontology, endodontology, operative dentistry, fixed and removable prosthodontics, dental biomaterials science, long-term clinical trials including epidemiology and oral health, technology transfer of new scientific instrumentation or procedures, as well as clinically relevant oral biology and translational research. The Journal of Dentistry will publish original scientific research papers including short communications. It is also interested in publishing review articles and leaders in themed areas which will be linked to new scientific research. Conference proceedings are also welcome and expressions of interest should be communicated to the Editor.