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2018年牙周炎分类:临床视角下的挑战

The 2018 Classification of Periodontitis: Challenges from Clinical Perspective.

作者信息

Chmielewski Marek, Pilloni Andrea, Cuozzo Alessandro, D'Albis Giuseppe, D'Elia Gerarda, Papi Piero, Marini Lorenzo

机构信息

Private Practice, 81-881 Sopot, Poland.

Section of Perioontics, Department of Oral and Maxillofacial Sciences, Sapienza University of Rome, 00161 Rome, Italy.

出版信息

Dent J (Basel). 2025 Aug 8;13(8):361. doi: 10.3390/dj13080361.

DOI:10.3390/dj13080361
PMID:40863064
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12385251/
Abstract

: The objective of this narrative review was to evaluate the clinical challenges encountered in the application of the 2018 AAP/EFP Classification of Periodontitis. : Electronic and manual searches were conducted to identify studies reporting diagnostic accuracy and inter- and intra-examiner agreement when using the 2018 Classification, both with and without the aid of implementation tools. : Eleven studies were included, encompassing a total of 459 clinical cases. Overall, 852 examiners participated, with 31 gold-standard examiners providing the reference diagnoses. General dentists often exhibited lower diagnostic accuracy and consistency compared to students and periodontal experts. Diagnostic challenges were observed in staging, particularly distinguishing between Stage III/IV and gingivitis/Stage I. Grading reliability was reduced in the absence of longitudinal data and high-level modifying factors. This review also explored difficulties in reporting 'hopeless teeth' and assigning the extent of periodontitis. Education, training, and implementation tools substantially improved diagnostic accuracy and consistency, increasing the agreement with reference diagnosis and strengthening inter- and intra-examiner agreement. : The application of the 2018 Classification showed notable variability in diagnostic accuracy and inter- and intra-examiner agreement. Improving clinician experience and training and incorporating diagnostic aids and emerging AI technologies has the potential to enhance diagnostic accuracy and consistency, which are essential for precisely estimating periodontitis prevalence, interpreting research findings, and determining prognosis and treatment needs.

摘要

本叙述性综述的目的是评估在应用2018年美国儿科学会/欧洲牙周病学联合会牙周炎分类时遇到的临床挑战。通过电子检索和手工检索,以识别在使用2018年分类时报告诊断准确性以及检查者间和检查者内一致性的研究,无论是否借助实施工具。纳入了11项研究,共计459例临床病例。总体而言,有852名检查者参与,其中31名金标准检查者提供参考诊断。与学生和牙周病专家相比,普通牙医的诊断准确性和一致性通常较低。在分期方面观察到诊断挑战,特别是区分III/IV期与牙龈炎/I期。在缺乏纵向数据和高级修正因素的情况下,分级可靠性降低。本综述还探讨了报告“无保留价值牙齿”和确定牙周炎范围方面的困难。教育、培训和实施工具显著提高了诊断准确性和一致性,增加了与参考诊断的一致性,并加强了检查者间和检查者内的一致性。2018年分类的应用在诊断准确性以及检查者间和检查者内一致性方面存在显著差异。提高临床医生的经验和培训水平,并纳入诊断辅助工具和新兴的人工智能技术,有可能提高诊断准确性和一致性,这对于准确估计牙周炎患病率、解释研究结果以及确定预后和治疗需求至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/091d/12385251/0f7386abb1fd/dentistry-13-00361-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/091d/12385251/b13c7f646345/dentistry-13-00361-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/091d/12385251/1ffd5c5d530c/dentistry-13-00361-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/091d/12385251/d713afac2d46/dentistry-13-00361-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/091d/12385251/cfda4c62421f/dentistry-13-00361-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/091d/12385251/42871a8c0d8e/dentistry-13-00361-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/091d/12385251/0f7386abb1fd/dentistry-13-00361-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/091d/12385251/b13c7f646345/dentistry-13-00361-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/091d/12385251/1ffd5c5d530c/dentistry-13-00361-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/091d/12385251/d713afac2d46/dentistry-13-00361-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/091d/12385251/cfda4c62421f/dentistry-13-00361-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/091d/12385251/42871a8c0d8e/dentistry-13-00361-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/091d/12385251/0f7386abb1fd/dentistry-13-00361-g006.jpg

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本文引用的文献

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PerioAI: A digital system for periodontal disease diagnosis from an intra-oral scan and cone-beam CT image.牙周病人工智能:一种通过口腔内扫描和锥形束CT图像诊断牙周病的数字系统。
Cell Rep Med. 2025 Jun 17;6(6):102186. doi: 10.1016/j.xcrm.2025.102186.
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Evaluating the Performance and Implementation of the 2018 Classification of Periodontal Diseases: A Systematic Review and Survey.评估2018年牙周疾病分类的性能与实施情况:一项系统综述与调查
J Clin Periodontol. 2025 Aug;52 Suppl 29(Suppl 29):34-57. doi: 10.1111/jcpe.14170. Epub 2025 May 7.
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Clinical knowledge-guided hybrid classification network for automatic periodontal disease diagnosis in X-ray image.
临床知识引导的混合分类网络用于 X 射线图像中的牙周病自动诊断。
Med Image Anal. 2025 Jan;99:103376. doi: 10.1016/j.media.2024.103376. Epub 2024 Oct 24.
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The 2018 Classification of Periodontal Diseases: An Observational Study on Inter-examiner Agreement among Undergraduate Students.2018 年牙周病分类:对本科生之间检查者间一致性的观察研究。
Oral Health Prev Dent. 2024 Oct 24;22:519-524. doi: 10.3290/j.ohpd.b5795649.
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Ease and practicability of the 2017 classification of periodontal diseases and conditions: a study of dental electronic health records.牙周病和状况分类 2017 年版的简易性和实用性:一项牙科电子健康记录研究。
BMC Oral Health. 2024 May 28;24(1):621. doi: 10.1186/s12903-024-04385-5.
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Deep learning for classifying the stages of periodontitis on dental images: a systematic review and meta-analysis.深度学习在牙片牙周炎分期分类中的应用:系统评价与荟萃分析。
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Periodontitis stage and grade modifies the benefit of regular supportive periodontal care in terms of need for retreatment and mean cumulative cost.牙周炎分期和分级会影响定期牙周支持治疗在需要再治疗和平均累积成本方面的获益。
J Clin Periodontol. 2024 Feb;51(2):167-176. doi: 10.1111/jcpe.13909. Epub 2023 Dec 12.
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Clin Oral Investig. 2023 Nov;27(11):6589-6596. doi: 10.1007/s00784-023-05264-z. Epub 2023 Sep 27.
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J Periodontol. 2023 Dec;94(12):1461-1474. doi: 10.1002/JPER.23-0129. Epub 2023 Jun 15.
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