Home > Archive>Volume 24, Issue 9, 2024 >1448-1452. DOI:10.3980/j.issn.1672-5123.2024.9.18
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Advances in tear-related indicators and techniques for patients with dry eye disease
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General Hospital of Xinjiang Military Command “HaR-Goolun” Talent Fund Training Program(No.2022QN004)

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    Abstract:

    The examinations of tear quality and volume are important indicators for the diagnosis of dry eye disease. Tests commonly used in clinical practice to diagnose dry eye disease include testing of tear volume and tear film stability to assess the severity of the condition, and analyzing and labeling of tear components under laboratory conditions to diagnose clinical staging and also to guide clinical supplementation of relevant components to target treatment. Accurate assessment of tear production and compositional changes enables clinicians to effectively monitor the severity of dry eye disease and evaluate the effectiveness of therapeutic measures. With the development of optical imaging technology and artificial intelligence in recent years, the combination of clinical tear indicators and examination techniques has enabled the application of new testing methods that provide more convenient, rapid and accurate testing, greatly improving the diagnosis and treatment of dry eye disease in the clinic.

    Reference
    \〖1\〗 亚洲干眼协会中国分会, 海峡两岸医药卫生交流协会眼科学专业委员会眼表与泪液病学组, 中国医师协会眼科医师分会眼表与干眼学组. 中国干眼专家共识:定义和分类(2020年).中华眼科杂志,2020,56(6):418-422.
    \〖2\〗 Holly FJ. Physical chemistry of the normal and disordered tear film. Trans Ophthalmol Soc U K(1962), 1985,104(Pt 4):374-380.
    \〖3\〗 Yang JR, Zhu XY, Liu YS, et al. TMIS: a new image-based software application for the measurement of tear meniscus height. Acta Ophthalmol, 2019,97(7):e973-e980.
    \〖4\〗 Arita R, Yabusaki K, Hirono T, et al. Automated measurement of tear Meniscus height with the kowa DR-1α tear interferometer in both healthy subjects and dry eye patients. Invest Ophthalmol Vis Sci, 2019,60(6):2092-2101.
    \〖5\〗 Deng XY, Tian L, Liu ZY, et al. A deep learning approach for the quantification of lower tear meniscus height.Biomedical Signal Processing and Control, 2021,68:102655.
    \〖6\〗 Arriola-Villalobos P, Fernández-Vigo JI, Díaz-Valle D, et al. Assessment of lower tear meniscus measurements obtained with Keratograph and agreement with Fourier-domain optical-coherence tomography. Br J Ophthalmol, 2015,99(8):1120-1125.
    \〖7\〗 Arriola-Villalobos P, Fernández-Vigo JI, Díaz-Valle D, et al. Lower tear Meniscus measurements using a new anterior segment swept-source optical coherence tomography and agreement with Fourier-domain optical coherence tomography. Cornea, 2017,36(2):183-188.
    \〖8\〗 Niedernolte B, Trunk L, Wolffsohn JS, et al. Evaluation of tear meniscus height using different clinical methods. Clin Exp Optom, 2021,104(5):583-588.
    \〖9\〗 Stegmann H, Werkmeister RM, Pfister M, et al. Deep learning segmentation for optical coherence tomography measurements of the lower tear meniscus. Biomed Opt Express, 2020,11(3):1539-1554.
    \〖10\〗 亚洲干眼协会中国分会, 海峡两岸医药卫生交流协会眼科学专业委员会眼表与泪液病学组, 中国医师协会眼科医师分会眼表与干眼学组. 中国干眼专家共识:检查和诊断(2020年). 中华眼科杂志,2020,56(10):741-747.
    \〖11\〗 Wang X, Fan XJ, Wu YY, et al. Rear 4-Min Schirmer test, a modified indicator of Schirmer test in diagnosing dry eye. Sci Rep, 2022,12(1):6272.
    \〖12\〗 Park JE, Kim YH, Jeong MJ, et al. Development of K-schirmer strip and evaluation of clinical availability. Korean J Vis Sci, 2018,20(1):37-47.
    \〖13\〗 Dogru M, Ishida K, Matsumoto Y, et al. Strip meniscometry: a new and simple method of tear meniscus evaluation. Invest Ophthalmol Vis Sci, 2006,47(5):1895-1901.
    \〖14\〗 Nascimento FF, Passareli JVGC, Zulim LFDC, et al. Comparison of strip meniscometry and Schirmer tear test results and tear film breakup time between healthy dogs and dogs with dry eye disease. Arq Bras Oftalmol, 2023,86(4):314-321.
    \〖15\〗 Schulze K, Großjohann R, Paul S, et al. Schirmer's test and strip meniscometry: Comparative consideration in the diagnostics of dry eye. Ophthalmologe, 2021,118(6):561-568.
    \〖16\〗 Zhou WY, Yu HZ, Feng Y. Decrease in tear film lipid layer thickness in patients with keratoconus. J Clin Med, 2022,11(18):5252.
    \〖17\〗 García-Marqués JV, Talens-Estarelles C, García-Lázaro S, et al. Validation of a new objective method to assess lipid layer thickness without the need of an interferometer. Albrecht Von Graefes Arch Fur Klin Und Exp Ophthalmol, 2022,260(2):655-676.
    \〖18\〗 Lee JM, Jeon YJ, Kim KY, et al. Ocular surface analysis: a comparison between the LipiView® II and IDRA®. Eur J Ophthalmol, 2021,31(5):2300-2306.
    \〖19\〗 Fu PI, Fang PC, Ho RW, et al. Determination of tear lipid film thickness based on a reflected placido disk tear film analyzer. Diagnostics, 2020,10(6):353.
    \〖20\〗 da Cruz LB, Souza JC, de Paiva AC, et al. Tear film classification in interferometry eye images using phylogenetic diversity indexes and Ripley's K function. IEEE J Biomed Health Inform, 2020,24(12):3491-3498.
    \〖21\〗 da Cruz LB, Souza JC, de Sousa JA, et al. Interferometer eye image classification for dry eye categorization using phylogenetic diversity indexes for texture analysis. Comput Methods Programs Biomed, 2020,188:105269.
    \〖22\〗 Yabusaki K, Arita R, Yamauchi T. Automated classification of dry eye type analyzing interference fringe color images of tear film using machine learning techniques. Model Artif Intell Ophthalmol, 2019,2(3):28-35.
    \〖23\〗 Bai YQ, Ngo W, Gu BY, et al. An imaging system integrating optical coherence tomography and interferometry for in vivo measurement of the thickness and dynamics of the tear film. Biomed Eng Online, 2018,17(1):164.
    \〖24\〗 Bai YQ, Ngo W, Nichols JJ. Characterization of the thickness of the tear film lipid layer using high resolution microscopy. Ocul Surf, 2019,17(2):356-359.
    \〖25\〗 Bai YQ, Ngo W, Khanal S, et al. Human precorneal tear film and lipid layer dynamics in meibomian gland dysfunction. Ocul Surf, 2021,21:250-256.
    \〖26\〗 Cohen Y, Epshtein S, Harris A, et al. Tear film imager for dynamic mapping of the human tear film. Appl Opt, 2019,58(29):7987-7995.
    \〖27\〗 Perumal N, Funke S, Pfeiffer N, et al. Proteomics analysis of human tears from aqueous-deficient and evaporative dry eye patients. Sci Rep, 2016,6:29629.
    \〖28\〗 Jung JH, Ji YW, Hwang HS, et al. Proteomic analysis of human lacrimal and tear fluid in dry eye disease. Sci Rep, 2017,7(1):13363.
    \〖29\〗 王晓伟, 李正日, 金花, 等. 干眼患者泪液中Nod样受体1表达水平及其与临床指标的相关性. 眼科新进展, 2022,42(10):820-823.
    \〖30\〗 Roda M, Corazza I, Bacchi Reggiani ML, et al. Dry eye disease and tear cytokine levels-a meta-analysis. Int J Mol Sci, 2020,21(9):3111.
    \〖31\〗 Fan NW, Dohlman TH, Foulsham W, et al. The role of Th17 immunity in chronic ocular surface disorders. Ocul Surf, 2021,19:157-168.
    \〖32\〗 Kang MJ, Kim HS, Kim MS, et al. The correlation between matrix metalloproteinase-9 point-of-care immunoassay, tear film osmolarity, and ocular surface parameters. J Ophthalmol, 2022,2022:6132016.
    \〖33\〗 Yang S, Lee HJ, Kim DY, et al. The use of conjunctival staining to measure ocular surface inflammation in patients with dry eye. Cornea, 2019,38(6):698-705.
    \〖34\〗 Ponzini E, Santambrogio C, de Palma A, et al. Mass spectrometry-based tear proteomics for noninvasive biomarker discovery. Mass Spectrom Rev, 2022,41(5):842-860.
    \〖35\〗 孙晓雯, 黄悦, 张晓敏. 眼表微生物群及其与眼表疾病的关系. 中华实验眼科杂志, 2023,41(10):1038-1042.
    \〖36\〗 Du CX, Huang Z. Analysis of amino acids in human tears by hydrophilic interaction liquid chromatography and quadrupole orbitrap mass spectrometry. RSC Adv, 2019,9(63):36539-36545.
    \〖37\〗 Jones G, Lee TJ, Glass J, et al. Comparison of different mass spectrometry workflows for the proteomic analysis of tear fluid. Int J Mol Sci, 2022,23(4):2307.
    \〖38\〗 姚亚男. Schirmer纸喷雾质谱法用于人体泪液成分的快速分析. 暨南大学, 2021.
    \〖39\〗 刘康成, 师艺丹, 邵毅. 基于泪膜破裂方式的干眼诊断新思路. 眼科学报, 2021,36(3):227-232.
    \〖40\〗 艾丽珍, 罗红豆, 余兰慧, 等. 不同浓度荧光素钠液对检测泪膜破裂时间的稳定性比较. 国际眼科杂志, 2022,22(10):1702-1706.
    \〖41\〗 Singh S, Srivastav S, Modiwala Z, et al. Repeatability, reproducibility and agreement between three different diagnostic imaging platforms for tear film evaluation of normal and dry eye disease. Eye, 2023,37(10):2042-2047.
    \〖42\〗 Bandlitz S, Peter B, Pflugi T, et al. Agreement and repeatability of four different devices to measure non-invasive tear breakup time(NIBUT). Cont Lens Anterior Eye, 2020,43(5):507-511.
    \〖43\〗 Kikukawa Y, Tanaka S, Kosugi T, et al. Non-invasive and objective tear film breakup detection on interference color images using convolutional neural networks. PLoS One, 2023,18(3):e0282973.
    \〖44\〗 Shimizu E, Ishikawa T, Tanji M, et al. Artificial intelligence to estimate the tear film breakup time and diagnose dry eye disease. Sci Rep, 2023,13(1):5822.
    \〖45\〗 Jiang L, Sun SS, Chen J, et al. Random forest algorithm-based ultrasonic image in the diagnosis of patients with dry eye syndrome and its relationship with tear osmotic pressure. Comput Math Methods Med, 2022,2022:9437468.
    \〖46\〗 程验, 刘焱焱, 魏苗, 等. 泪液渗透压在干眼发病机制中的作用及诊疗进展. 国际眼科杂志, 2023,23(1):84-89.
    \〖47\〗 Cartes C, López D, Salinas D, et al. Dry eye is matched by increased intrasubject variability in tear osmolarity as confirmed by machine learning approach. Arch Soc Esp Oftalmol, 2019,94(7):337-342.
    \〖48\〗 Tashbayev B, Utheim TP, Utheim ØA, et al. Utility of tear osmolarity measurement in diagnosis of dry eye disease. Sci Rep, 2020,10:5542.
    \〖49\〗 孙龙格, 马忠旭, 马林, 等. 基于OQAS-Ⅱ视觉质量参数对轻中度干眼泪膜稳定性动态变化的评估. 中华实验眼科杂志, 2017,35(4):344-348.
    \〖50\〗 周黎纹, 谢玉秀, 袁非. 角膜地形图对干眼症患者泪膜稳定性的评价分析. 中国临床医学, 2019,26(1):69-71.
    \〖51\〗 Kaido M. Functional Visual Acuity. Invest Ophthalmol Vis Sci,2018,59(14):DES29-DES35.
    \〖52\〗 苏远东, 梁庆丰, 王宁利, 等. 泪膜客观散射指数对干眼诊断价值的研究. 中华眼科杂志, 2017,53(9):668-674.
    \〖53\〗 Pan AP, Ma YJ, Hu RL, et al. Simultaneous real-time analysis of tear film optical quality dynamics and functional visual acuity in dry eye disease. Eye Vis, 2023,10(1):16.
    \〖54\〗 李佳馨. I.profiler Plus测量轻度干眼患者生物学参数的临床研究. 中国医科大学,2023.
    \〖55\〗 Ozulken K, Aksoy Aydemir G, Tekin K, et al. Correlation of non-invasive tear break-up time with tear osmolarity and other invasive tear function tests. Semin Ophthalmol, 2020,35(1):78-85.
    \〖56\〗 Okumura Y, Inomata T, Midorikawa-Inomata A, et al. DryEyeRhythm: a reliable and valid smartphone application for the diagnosis assistance of dry eye. Ocul Surf, 2022,25:19-25.
    \〖57\〗 Kasetsuwan N, Suwan-Apichon O, Lekhanont K, et al. Assessing the risk factors for diagnosed symptomatic dry eye using a smartphone app: cross-sectional study. JMIR Mhealth Uhealth, 2022,10(6):e31011.
    \〖58\〗 Nagino K, Okumura Y, Yamaguchi M, et al. Diagnostic ability of a smartphone app for dry eye disease: protocol for a multicenter, open-label, prospective, and cross-sectional study. JMIR Res Protoc, 2023,12:e45218.
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Qian Jinmei, Cai Yan. ,/et al.Advances in tear-related indicators and techniques for patients with dry eye disease. Guoji Yanke Zazhi( Int Eye Sci) 2024;24(9):1448-1452

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Publication History
  • Received:September 06,2023
  • Revised:July 15,2024
  • Online: August 16,2024
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