<p class="MsoNormal" align="center" style="text-align:left;">
	牙结石新兴检测技术及其临床应用

摘要/Abstract

摘要：

牙结石是导致牙周病的重要致病因素之一，其精准识别对牙周病的防治至关重要。传统牙结石识别方法存在主观性强、准确性低的问题，尤其难以检测龈下隐蔽部位的结石，难以满足现代临床需求。近年来，光学检测技术因其非侵入性和高灵敏度等特点受到广泛关注。多种光学技术为牙结石识别提供了新的思路，如偏振光检测、光学相干断层扫描(optical coherence tomography, OCT)、差示反射法（differential reflectometry)、高光谱成像(hyperspectral imaging, HSI)和荧光光谱系统等。同时，人工智能技术，特别是机器学习和深度学习与光学技术相结合，显著提高了牙结石识别的自动化和智能化程度。本文综述了目前主流的牙结石识别方法，对比分析了各种技术的优缺点，并对未来发展方向进行了展望，旨在为牙结石识别的研究和临床应用提供参考。

关键词:

牙结石, 光学成像, 人工智能, 牙周病诊断

Abstract:

Dental calculus is a significant contributor to periodontal disease, making its accurate identification crucial for effective prevention and treatment. Traditional methods for identifying dental calculus have the problems of strong subjectivity and low accuracy, especially when it comes to detecting calculus in the concealed subgingival areas, which cannot meet the demands of modern clinical practice. Recently, optical detection technology has attracted widespread attention due to its non-invasive and high-sensitivity characteristics. A variety of optical techniques have provided new ideas for the identification of dental calculus, such as polarization detection, optical coherence tomography (OCT), differential reflectometry, hyperspectral imaging (HSI), and fluorescence spectroscopy systems. Furthermore, artificial intelligence technology, particularly the combination of machine learning and deep learning with optical techniques, has significantly enhanced the level of automation and intelligence in the identification of dental calculus. This review provides the current mainstream methods for identifying dental calculus, compares and analyzes the advantages and disadvantages of each technology, and looks forward to the future development direction. This work aims to guide research and clinical application of dental calculus detection technologies.

Key words:

dental calculus, , optical imaging, artificial intelligence, , periodontal diagnosis

中图分类号:

R782

张龙杰, 王绪泉, 周敏.

牙结石新兴检测技术及其临床应用[J]. 《口腔颌面外科杂志》.

ZHANG Longjie, WANG Xuquan, ZHOU Min.

Emerging Technologies for dental calculus detection and their clinical applications[J]. 《Journal of Oral and Maxillofacial Surgery》.

推荐 / 导出引用

参考文献

[1] Friskopp J. Ultrastructure of nondecalcified supragingival and subgingival Calculus[J]. J Periodontol, 1983, 54(9): 542-550.

[2] Chang JJ, Chen C, Chang J, et al. A narrative review of imaging tools for imaging subgingival Calculus[J]. Front Oral Maxillofac Med, 2023, 5: 4.

[3] Kamath DG, Umesh Nayak S. Detection, removal and prevention of Calculus: literature review[J]. Saudi Dent J, 2014, 26(1): 7-13.

[4] Partido BB, Webb CA, Carr MP. Comparison of Calculus detection among dental hygienists using an explorer and ultrasonic insert[J]. Int J Dent Hyg, 2019, 17(2): 192-198.

[5] Datta DD. Disclosing solutions used in dentistry[J]. World J Pharm Res, 2017: 1648-1656.

[6] Büttner M, Schneider L, Krasowski A, et al. Impact of noisy labels on dental deep learning-Calculus detection on bitewing radiographs[J]. J Clin Med, 2023, 12(9): 3058.

[7] Kripal, Krishna, and Aiswarya Dileep. ‘Role of Radiographic Evolution: An Aid to Diagnose Periodontal Disease’. Periodontal Disease - Diagnostic and Adjunctive Non-Surgical Considerations, IntechOpen, 5 Feb. 2020. Crossref, doi:10.5772/intechopen.88035.

[8] Hsiao TY, Lee SY, Sun CW. Optical polarimetric detection for dental hard tissue diseases characterization[J]. Sensors (Basel), 2019, 19(22): 4971.

[9] Hsiao TY, Ho YC, Lee SY, et al. Degree of polarization uniformity for dental Calculus visualization[J]. J Biophotonics, 2022, 15(6): e202200011.

[10] Hsiao TY, Ho YC, Chen MR, et al. Disease activation maps for subgingival dental Calculus identification based on intelligent dental optical coherence tomography[J]. Transl Biophotonics, 2021, 3(3): e202100001.

[11] Wang TA, Trung NH, Lee HC, et al. Quantitative evaluation of caries and Calculus with ultrahigh-resolution optical coherence tomography[J]. Bioengineering (Basel), 2023, 10(11): 1317.

[12] Kao MC, Lin CL, Kung CY, et al. Miniature endoscopic optical coherence tomography for Calculus detection[J]. Appl Opt, 2015, 54(24): 7419-7423.

[13] Shakibaie F, Law K, Walsh LJ. Improved detection of subgingival Calculus by laser fluorescence over differential reflectometry[J]. Lasers Med Sci, 2019, 34(9): 1807-1811.

[14] 胡杰, 杨扬, 秦艳利, 等. 激光诱导荧光牙结石诊断研究[J]. 沈阳理工大学学报, 2013, 32(2): 53-55, 58.

[15] 杨扬. 牙结石专用激光诱导荧光诊断系统研究 [D]: 沈阳理工大学; 2014.

[16] Wang C, Zhang RJ, Wei XL, et al. Machine learning-based automatic identification and diagnosis of dental caries and Calculus using hyperspectral fluorescence imaging[J]. Photodiagnosis Photodyn Ther, 2023, 41: 103217.

[17] Garcia Peraza Herrera LC, Horgan C, Ourselin S, et al. Hyperspectral image segmentation: a preliminary study on the Oral and Dental Spectral Image Database (ODSI-DB)[J]. Comput Meth Biomech Biomed Eng Imag Vis, 2023, 11(4): 1290-1298.

[18] Abdel Gawad AL, El-Sharkawy Y, Ayoub HS, et al. Classification of dental diseases using hyperspectral imaging and laser induced fluorescence[J]. Photodiagnosis Photodyn Ther, 2019, 25: 128-135.

[19] Satpathy A, Mohanty G, Bose A, et al. Fourier transfer infra-red spectroscopic analysis of supragingival and subgingival human dental Calculus[J]. Indian Jour Foren Med & Toxicol, 2019, 13(4): 1870.

[20] Lee YK. Fluorescence properties of human teeth and dental Calculus for clinical applications[J]. J Biomed Opt, 2015, 20(4): 040901.

[21] Fried WA, Chan KH, Darling CL, et al. Image-guided ablation of dental Calculus from root surfaces using a DPSS Er: YAG laser[J]. Lasers Surg Med, 2020, 52(3): 247-258.

[22] Agoob Alfergany M, Nasher R, Gutknecht N. Calculus removal and root surface roughness when using the Er: YAG or Er, Cr: YSGG laser compared with conventional instrumentation method: a literature review[J]. Photobiomodul Photomed Laser Surg, 2019, 37(4): 197-226.

[23] Shakibaie F, Walsh LJ. Dental Calculus detection using the VistaCam[J]. Clin Exp Dent Res, 2016, 2(3): 226-229.

[24] Shakibaie F, Walsh LJ. Performance differences in the detection of subgingival Calculus by laser fluorescence devices[J]. Lasers Med Sci, 2015, 30(9): 2281-2286.

[25] Archana V. Calculus detection technologies: where do we stand now?[J]. J Med Life, 2014, 7 Spec No. 2(Spec Iss 2): 18-23.

[26] Mothé D, de Oliveira K, Rotti A, et al. The micro from mega: Dental Calculus description and the first record of fossilized oral bacteria from an extinct proboscidean[J]. Int J Paleopathol, 2021, 33: 55-60.

[27] Patini R, Zunino B, Foti R, et al. Clinical evaluation of the efficacy of Perioscan® on plaque-induced gingivitis in pediatric age. [J].Senses and Sciences,2015, 2:98-103.

[28] Figueredo CA, Catunda RQ, Gibson MP, et al. Use of ultrasound imaging for assessment of the periodontium: a systematic review[J]. J Periodontal Res, 2024, 59(1): 3-17.

[29] Renaud M, Delpierre A, Becquet H, et al. Intraoral ultrasonography for periodontal tissue exploration: a review[J]. Diagnostics (Basel), 2023, 13(3): 365.

[30] Karhade AV, Schwab JH. Introduction to The Spine Journal special issue on artificial intelligence and machine learning[J]. Spine J, 2021, 21(10): 1601-1603.

[31] Wang C, Zhang RJ, Wei XL, et al. Deep learning and sub-band fluorescence imaging-based method for caries and Calculus diagnosis embeddable on different smartphones[J]. Biomed Opt Express, 2023, 14(2): 866-882.

[32] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.

[33] Venkatesan R., Li B.（2017）. Convolutional Neural Networks in Visual Computing: A Concise Guide (1st ed.). Boca Raton:CRC Press.

[34] Park S, Erkinov H, Al Mehedi Hasan M, et al. Periodontal disease classification with color teeth images using convolutional neural networks[J]. Electronics, 2023, 12(7): 1518.

选择文件类型/文献管理软件名称

选择包含的内容

牙结石新兴检测技术及其临床应用

Emerging Technologies for dental calculus detection and their clinical applications

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

编辑推荐

Metrics

本文评价

[1]	向文祉, 崔玮懿, 陶乐然, 于洪波. 基于人工智能的多模态颌面部三维测量分析方法及进展[J]. 《口腔颌面外科杂志》, 2025, 35(1): 43-47.
[2]	刘志凯, 许春炜, 朱照琨, 刘瑶, 罗恩. 人工智能正颌手术板的设计与精确性评价[J]. 《口腔颌面外科杂志》, 2024, 34(2): 115-121.
[3]	毛映, 赵绿扬, 龙洁. 人工智能技术在口腔颌面部X线及计算机断层扫描影像图像处理中的应用[J]. 《口腔颌面外科杂志》, 2022, 32(2): 125-128.

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

牙结石新兴检测技术及其临床应用

Emerging Technologies for dental calculus detection and their clinical applications

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

编辑推荐

Metrics

本文评价