Applications of Artificial Intelligence (AI) for Diagnosis of Periodontal/Peri-Implant Diseases: A Narrative Review

IF 4 3区医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE

Journal of oral rehabilitation Pub Date : 2025-06-04 DOI:10.1111/joor.14045

Rupanjan Roy, Aditi Chopra, Shaswata Karmakar, Subraya Giliyar Bhat

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引用次数: 0

Abstract

Background

Artificial intelligence (AI) and various subunits of AI such as artificial neural networks (ANN), Convolutional neural networks (CNNs), machine learning (ML), deep learning (DL) and deep neural networks (DNN) are being tried to diagnose and plan treatment for periodontal diseases.

Aim

This narrative review aims to discuss the current evidence on the applications of AI for the diagnosis and risk prediction of periodontal/peri-implant diseases.

Method

A search strategy with the following keywords: (Artificial intelligence [MeSH Terms]) AND (Periodontal disease [MeSH Terms]) was used to search for articles from 2000 to 2024.

Results

AI models using patient-related data, signs and symptoms of the disease, immunological biomarkers and microbial profiles aid in effective diagnosis and planning treatment. AI is also used in periodontal diagnosis of pathological and anatomical landmarks such as cementoenamel junction, bone levels, furcation defects, nature and system of dental implants placed, degree of implant or tooth fractures and periapical pathology, assessing the severity and grading of periodontal or peri-implant disease/conditions, assessing the signs and symptoms of periodontal/peri-implant disease and determining the prognosis of implant and periodontal treatment. Studies have compared the diagnosis made by dentists and AI-based models and found AI models to be more effective and quicker in diagnosis than dentists.

Discussion

AI-based tools such as DL, ML, CNN, and ANN are more effective and quicker for timely diagnosis, risk assessment, and treatment plans for periodontal and peri-implant disease diagnosis. DL and CNN are the most commonly used tools for the diagnosis of bone levels around teeth or implants, periodontal disease staging and severity, and location of anatomical structures and teeth.

Conclusion

AI and its subsets are promising tools for the diagnosis/risk prediction and treatment planning for periodontal and peri-implant diseases.

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人工智能（AI）在牙周/种植周疾病诊断中的应用综述

背景：人工智能（AI）和人工神经网络（ANN）、卷积神经网络（cnn）、机器学习（ML）、深度学习（DL）和深度神经网络（DNN）等人工智能的各种亚单位正在被尝试用于牙周病的诊断和治疗计划。目的：本文综述了人工智能在牙周/种植周疾病诊断和风险预测中的应用。方法：采用关键词：（人工智能[MeSH Terms]）和（牙周病[MeSH Terms]）的检索策略，对2000 - 2024年的文献进行检索。结果：使用患者相关数据、疾病体征和症状、免疫生物标志物和微生物谱的人工智能模型有助于有效诊断和规划治疗。人工智能还用于牙周病理和解剖标志的诊断，如牙髓-牙釉质交界处、骨水平、分叉缺陷、牙种植体放置的性质和系统、种植体或牙齿骨折的程度和根尖周围病理，评估牙周或种植体周围疾病/状况的严重程度和分级，评估牙周/种植体周围疾病的体征和症状，并确定种植体和牙周治疗的预后。有研究将牙医的诊断与基于人工智能的模型进行了比较，发现人工智能模型在诊断方面比牙医更有效、更快。讨论：基于人工智能的工具，如DL、ML、CNN和ANN在牙周和种植周疾病诊断的及时诊断、风险评估和治疗计划方面更有效、更快速。DL和CNN是诊断牙齿或种植体周围骨水平、牙周病分期和严重程度以及解剖结构和牙齿位置的最常用工具。结论：人工智能及其亚群是牙周和种植周疾病的诊断/风险预测和治疗计划的有前途的工具。

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来源期刊

Journal of oral rehabilitation 医学-牙科与口腔外科

CiteScore

5.60

自引率

10.30%

发文量

116

审稿时长

4-8 weeks

期刊介绍： Journal of Oral Rehabilitation aims to be the most prestigious journal of dental research within all aspects of oral rehabilitation and applied oral physiology. It covers all diagnostic and clinical management aspects necessary to re-establish a subjective and objective harmonious oral function. Oral rehabilitation may become necessary as a result of developmental or acquired disturbances in the orofacial region, orofacial traumas, or a variety of dental and oral diseases (primarily dental caries and periodontal diseases) and orofacial pain conditions. As such, oral rehabilitation in the twenty-first century is a matter of skilful diagnosis and minimal, appropriate intervention, the nature of which is intimately linked to a profound knowledge of oral physiology, oral biology, and dental and oral pathology. The scientific content of the journal therefore strives to reflect the best of evidence-based clinical dentistry. Modern clinical management should be based on solid scientific evidence gathered about diagnostic procedures and the properties and efficacy of the chosen intervention (e.g. material science, biological, toxicological, pharmacological or psychological aspects). The content of the journal also reflects documentation of the possible side-effects of rehabilitation, and includes prognostic perspectives of the treatment modalities chosen.