Roy Rupanjan, Chopra Aditi, Karmakar Shaswata, Bhat Subraya Giliyar
Department of Periodontology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Department of Preventive Dental Sciences, Division of Periodontology, College of Dental Surgery, Iman Abdulrahman Bin Faizal University, Dammam, Kingdom of Saudi Arabia.
J Oral Rehabil. 2025 Aug;52(8):1193-1219. doi: 10.1111/joor.14045. Epub 2025 Jun 4.
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.
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.
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.
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.
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.
AI and its subsets are promising tools for the diagnosis/risk prediction and treatment planning for periodontal and peri-implant diseases.
人工智能（AI）及其各个子单元，如人工神经网络（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），正被尝试用于牙周疾病的诊断和治疗规划。
本叙述性综述旨在讨论人工智能在牙周/种植体周围疾病诊断和风险预测应用方面的当前证据。
采用以下关键词的检索策略：（人工智能[医学主题词]） AND （牙周疾病[医学主题词]），检索2000年至2024年的文章。
使用与患者相关的数据、疾病的体征和症状、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制定治疗计划。人工智能还用于牙周诊断中的病理和解剖标志，如牙骨质釉质界、骨水平、根分叉病变、植入牙的性质和系统、植入物或牙齿骨折的程度以及根尖周病理，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。研究比较了牙医和基于人工智能的模型所做的诊断，发现人工智能模型在诊断方面比牙医更有效、更快速。
基于人工智能的工具，如深度学习、机器学习、卷积神经网络和人工神经网络，在牙周和种植体周围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。深度学习和卷积神经网络是诊断牙齿或种植体周围骨水平、牙周疾病分期和严重程度以及解剖结构和牙齿位置最常用的工具。
人工智能及其子集是牙周和种植体周围疾病诊断/风险预测和治疗规划的有前景的工具。
