{"title":"人工智能（AI）在牙周/种植周疾病诊断中的应用综述","authors":"Rupanjan Roy, Aditi Chopra, Shaswata Karmakar, Subraya Giliyar Bhat","doi":"10.1111/joor.14045","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Aim</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Method</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Discussion</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>AI and its subsets are promising tools for the diagnosis/risk prediction and treatment planning for periodontal and peri-implant diseases.</p>\n </section>\n </div>","PeriodicalId":16605,"journal":{"name":"Journal of oral rehabilitation","volume":"52 8","pages":"1193-1219"},"PeriodicalIF":4.0000,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/joor.14045","citationCount":"0","resultStr":"{\"title\":\"Applications of Artificial Intelligence (AI) for Diagnosis of Periodontal/Peri-Implant Diseases: A Narrative Review\",\"authors\":\"Rupanjan Roy, Aditi Chopra, Shaswata Karmakar, Subraya Giliyar Bhat\",\"doi\":\"10.1111/joor.14045\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Aim</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Method</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Discussion</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>AI and its subsets are promising tools for the diagnosis/risk prediction and treatment planning for periodontal and peri-implant diseases.</p>\\n </section>\\n </div>\",\"PeriodicalId\":16605,\"journal\":{\"name\":\"Journal of oral rehabilitation\",\"volume\":\"52 8\",\"pages\":\"1193-1219\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2025-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/joor.14045\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of oral rehabilitation\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/joor.14045\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"DENTISTRY, ORAL SURGERY & MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of oral rehabilitation","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/joor.14045","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}
Applications of Artificial Intelligence (AI) for Diagnosis of Periodontal/Peri-Implant Diseases: A Narrative Review
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.
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.
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