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Yim TW, Pucker AD, Rueff E, Ngo W, Tichenor AA, Conto JE. LipiFlow for the treatment of dry eye disease: A Cochrane systematic review summary. Cont Lens Anterior Eye 2025; 48:102335. [PMID: 39562261 PMCID: PMC11911098 DOI: 10.1016/j.clae.2024.102335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2024] [Revised: 10/27/2024] [Accepted: 11/11/2024] [Indexed: 11/21/2024]
Abstract
PURPOSE To evaluate the effectiveness and the safety of LipiFlow for treating signs and symptoms of dry eye disease (DED) in adults. METHODS The following databases were searched for randomized trials: CENTRAL, MEDLINE Ovid, Embase.com, PubMed, LILACS, ClinicalTrials.gov, and WHO ICTRP on 24 October 2022. The included studies were conducted in adults (≥18 years) with DED or meibomian gland dysfunction (MGD) as defined by the investigators. Standard Cochrane methodology was applied. RESULTS This study included 13 trials that randomized a total of 1,155 participants (66 % female; age range = 19 to 86 years). Five trials compared LipiFlow with basic warm compresses. Analyzing symptom scores in these trials yielded conflicting evidence of a difference in symptoms between LipiFlow and basic warm compresses after 4 weeks. There was no evidence of a difference in meibomian gland expression, meibum quality, or tear breakup time when comparing LipiFlow with basic warm compresses. Another 5 trials compared LipiFlow with thermostatic devices. Analysis of symptom scores in these trials at 4 weeks showed that thermostatic devices had reduced Ocular Surface Disease Index (OSDI) scores by a mean difference of 4.59 as compared with LipiFlow. The remaining 3 included trials could not be grouped for comparisons. The overall evidence was of low or very low certainty, with most trials being assessed as having a high risk of bias. No trial reported any intervention-related, vision-threating adverse events. CONCLUSIONS LipiFlow performs similarly to other DED treatments. Further research with adequate masking and a standardized testing methodology is still needed.
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Affiliation(s)
- Tsz Wing Yim
- Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Erin Rueff
- Southern California College of Optometry at Marshall B Ketchum University, Fullerton, CA, USA
| | - William Ngo
- University of Waterloo School of Optometry & Vision Science, Waterloo, ON, CA, USA
| | - Anna A Tichenor
- Indiana University School of Optometry, Bloomington, IN, USA
| | - John E Conto
- Department of Ophthalmology, Medical College of Wisconsin, Milwaukee, WI, USA
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Ravichandran S, Pucker AD. Comparing meibomian gland visibility on optical coherence tomography and Keratograph 5M images using objective and subjective grading methods. Cont Lens Anterior Eye 2024; 47:102162. [PMID: 38565442 DOI: 10.1016/j.clae.2024.102162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 03/15/2024] [Accepted: 03/28/2024] [Indexed: 04/04/2024]
Abstract
PURPOSE To investigate if there is a visible difference in meibomian gland (MG) length between images captured with the Visante optical coherence tomography (OCT; wavelength = 1,310 nm) and the OCULUS Keratograph 5M (K5M; wavelength = 880 nm). METHODS Adults between 18 and 40 years were recruited. Baseline dry eye disease was evaluated with the Standard Patient Evaluation of Eye Dryness (SPEED) and tear meniscus height and tear breakup time with the K5M. Right upper and lower eyelid MGs were imaged with the K5M and Visante OCT. Each image was graded with the 0 to 3 meiboscore scale. The central 5 MGs were evaluated with ImageJ for percent gland length visibility. RESULTS Thirty participants were analyzed with a median (interquartile range [IQR]) age of 23.0 (5.0) years (53.3 % female). Overall, participants were asymptomatic and had normal tear films. Meiboscores based on K5M and Visante OCT was significantly different for the lower eyelid (0[1] vs 1[2]; p = 0.007) but not the upper eyelid (0[1] vs 0[1]; p = 1.00). The mean percent gland visibility of the upper eyelid (82.7[9.6] vs 75.2[13.5]; p < 0.001) and the lower eyelid (81.2[12.7] vs 64.1[17.6]; p < 0.001) were significantly greater on the Visante OCT than the K5M images, respectively. CONCLUSION OCT images had significantly greater percent visible MG lengths than the K5M images. This suggests viable segments of the MGs may be missed with typical imaging, which may explain how it is possible that studies have found less post-treatment MG atrophy.
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Affiliation(s)
- Swetha Ravichandran
- School of Optometry, University of Alabama at Birmingham, Alabama, United States
| | - Andrew D Pucker
- School of Optometry, University of Alabama at Birmingham, Alabama, United States; Lexitas Pharma Services, Durham, NC, United States.
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Li S, Wang Y, Yu C, Li Q, Chang P, Wang D, Li Z, Zhao Y, Zhang H, Tang N, Guan W, Fu Y, Zhao YE. Unsupervised Learning Based on Meibography Enables Subtyping of Dry Eye Disease and Reveals Ocular Surface Features. Invest Ophthalmol Vis Sci 2023; 64:43. [PMID: 37883092 PMCID: PMC10615148 DOI: 10.1167/iovs.64.13.43] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 10/03/2023] [Indexed: 10/27/2023] Open
Abstract
Purpose This study aimed to establish an image-based classification that can reveal the clinical characteristics of patients with dry eye using unsupervised learning methods. Methods In this study, we analyzed 82,236 meibography images from 20,559 subjects. Using the SimCLR neural network, the images were categorized. Data for each patient were averaged and subjected to mini-batch k-means clustering, and validated through consensus clustering. Statistical metrics determined optimal category numbers. Using a UNet model, images were segmented to identify meibomian gland (MG) areas. Clinical features were assessed, including tear breakup time (BUT), tear meniscus height (TMH), and gland atrophy. A thorough ocular surface evaluation was conducted on 280 cooperative patients. Results SimCLR neural network achieved clustering patients with dry eye into six image-based subtypes. Patients in different subtypes harbored significantly different noninvasive BUT, significantly correlated with TMH. Subtypes 1 and 5 had the most severe MG atrophy. Subtype 2 had the highest corneal fluorescent staining (CFS). Subtype 4 had the lowest TMH, whereas subtype 5 had the highest. Subtypes 3 and 6 had the largest MG areas, and the upper MG areas of a person's bilateral eyes were highly correlated. Image-based subtypes are related to meibum quality, CFS, and morphological characteristics of MG. Conclusions In this study, we developed an unsupervised neural network model to cluster patients with dry eye into image-based subtypes using meibography images. We annotated these subtypes with functional and morphological clinical characteristics.
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Affiliation(s)
- Siyan Li
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Eye Hospital of Wenzhou Medical University at Hangzhou, Hangzhou, China
| | - Yiyi Wang
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Chunyu Yu
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, China
| | - Qiyuan Li
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Pingjun Chang
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Eye Hospital of Wenzhou Medical University at Hangzhou, Hangzhou, China
| | - Dandan Wang
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Eye Hospital of Wenzhou Medical University at Hangzhou, Hangzhou, China
| | - Zhangliang Li
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Eye Hospital of Wenzhou Medical University at Hangzhou, Hangzhou, China
| | - Yinying Zhao
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Eye Hospital of Wenzhou Medical University at Hangzhou, Hangzhou, China
| | - Hongfang Zhang
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Eye Hospital of Wenzhou Medical University at Hangzhou, Hangzhou, China
| | - Ning Tang
- Eye Hospital of Wenzhou Medical University at Hangzhou, Hangzhou, China
| | - Weichen Guan
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Yana Fu
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Eye Hospital of Wenzhou Medical University at Hangzhou, Hangzhou, China
| | - Yun-e Zhao
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Eye Hospital of Wenzhou Medical University at Hangzhou, Hangzhou, China
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Feng J, Wang J, Wu B, Shao Q, Zang Y, Cao K, Tian L, Jie Y. Association of meibomian gland morphology with orifice plugging and lid margin thickening in meibomian gland dysfunction patients. Int Ophthalmol 2023:10.1007/s10792-023-02721-2. [PMID: 37140834 DOI: 10.1007/s10792-023-02721-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/09/2023] [Indexed: 05/05/2023]
Abstract
PURPOSE We sought to investigate the association of meibomian gland morphology with lid margin abnormalities in patients with meibomian gland dysfunction. METHODS This retrospective study included 368 eyes of 184 patients. Meibography was used to evaluate meibomian gland (MG) morphological features, such as dropout, distortion, thickened ratio and thinned ratio. Lid margin photography was used to evaluate lid margin abnormalities including orifice plugging, vascularity, irregularity and thickening. The association between MG morphological features and lid margin abnormalities was analyzed using a mixed linear model. RESULTS The study found a positive correlation between plugging of gland orifices grade and MG dropout grade in both the upper lids (B = 0.40, p = 0.007) and the lower lids (B = 0.55, p = 0.001). Plugging of gland orifices grade was also positively correlated to MG distortion grade in the upper lids (B = 0.75, p = 0.006). In the upper lids, MG thickened ratio increased first (B = 0.21, p = 0.003) and then decreased (B = - 0.14, p = 0.010) with a higher lid margin thickening grade. MG thinned ratio was negatively correlated with lid margin thickening (B = - 0.14, p = 0.002, B = - 0.13, p = 0.007). MG distortion grade decreased with lid margin thickening (B = - 0.61, p = 0.012). CONCLUSION Orifice plugging was correlated to meibomian gland distortion and dropout. Lid margin thickening was associated with meibomian gland thickened ratio, thinned ratio, and distortion. The study also suggested that distorted and thinned glands may be transitional phases between thickened glands and glands dropout.
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Affiliation(s)
- Jun Feng
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Science Key Lab, No. 1 Dong Jiao Min Xiang, Dong Cheng District, Beijing, 100730, China
| | - Jingyi Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Science Key Lab, No. 1 Dong Jiao Min Xiang, Dong Cheng District, Beijing, 100730, China
| | - Binge Wu
- Department of Ophthalmology, The Second Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou, China
| | - Qiyan Shao
- Department of Ophthalmology, The Second Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou, China
| | - Yunxiao Zang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Science Key Lab, No. 1 Dong Jiao Min Xiang, Dong Cheng District, Beijing, 100730, China
| | - Kai Cao
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Science Key Lab, No. 1 Dong Jiao Min Xiang, Dong Cheng District, Beijing, 100730, China
| | - Lei Tian
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Science Key Lab, No. 1 Dong Jiao Min Xiang, Dong Cheng District, Beijing, 100730, China
| | - Ying Jie
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Science Key Lab, No. 1 Dong Jiao Min Xiang, Dong Cheng District, Beijing, 100730, China.
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Galor A, Britten-Jones AC, Feng Y, Ferrari G, Goldblum D, Gupta PK, Merayo-Lloves J, Na KS, Naroo SA, Nichols KK, Rocha EM, Tong L, Wang MTM, Craig JP. TFOS Lifestyle: Impact of lifestyle challenges on the ocular surface. Ocul Surf 2023; 28:262-303. [PMID: 37054911 DOI: 10.1016/j.jtos.2023.04.008] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 04/07/2023] [Indexed: 04/15/2023]
Abstract
Many factors in the domains of mental, physical, and social health have been associated with various ocular surface diseases, with most of the focus centered on aspects of dry eye disease (DED). Regarding mental health factors, several cross-sectional studies have noted associations between depression and anxiety, and medications used to treat these disorders, and DED symptoms. Sleep disorders (both involving quality and quantity of sleep) have also been associated with DED symptoms. Under the domain of physical health, several factors have been linked to meibomian gland abnormalities, including obesity and face mask wear. Cross-sectional studies have also linked chronic pain conditions, specifically migraine, chronic pain syndrome and fibromyalgia, to DED, principally focusing on DED symptoms. A systematic review and meta-analysis reviewed available data and concluded that various chronic pain conditions increased the risk of DED (variably defined), with odds ratios ranging from 1.60 to 2.16. However, heterogeneity was noted, highlighting the need for additional studies examining the impact of chronic pain on DED signs and subtype (evaporative versus aqueous deficient). With respect to societal factors, tobacco use has been most closely linked to tear instability, cocaine to decreased corneal sensitivity, and alcohol to tear film disturbances and DED symptoms.
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Affiliation(s)
- Anat Galor
- Bascom Palmer Eye Institute, University of Miami, Miami, FL, USA; Surgical Services, Miami Veterans Administration, Miami, FL, USA.
| | - Alexis Ceecee Britten-Jones
- Department of Optometry and Vision Sciences, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Victoria, Australia
| | - Yun Feng
- Department of Ophthalmology, Peking University Eye Center, Peking University Third Hospital, Beijing, China
| | - Giulio Ferrari
- Cornea and Ocular Surface Unit, Eye Repair Lab, San Raffaele Scientific Institute, Milan, Italy
| | - David Goldblum
- Pallas-Kliniken, Olten, Bern, Zurich, Switzerland; University of Basel, Basel, Switzerland
| | - Preeya K Gupta
- Triangle Eye Consultants, Raleigh, NC, USA; Department of Ophthalmology, Tulane University, New Orleans, LA, USA
| | - Jesus Merayo-Lloves
- Instituto Universitario Fernandez-Vega, Universidad de Oviedo, Principality of Asturias, Spain
| | - Kyung-Sun Na
- Department of Ophthalmology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Shehzad A Naroo
- College of Health and Life Sciences, Aston University, Birmingham, UK
| | - Kelly K Nichols
- School of Optometry, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Eduardo M Rocha
- Department of Ophthalmology, Othorynolaringology and Head & Neck Surgery, Ribeirao Preto Medical School, University of Sao Paulo, Ribeirao Preto, Sao Paulo, Brazil
| | - Louis Tong
- Cornea and External Eye Disease Service, Singapore National Eye Center, Ocular Surface Research Group, Singapore Eye Research Institute, Eye Academic Clinical Program, Duke-National University of Singapore, Singapore
| | - Michael T M Wang
- Department of Ophthalmology, New Zealand National Eye Centre, The University of Auckland, Auckland, New Zealand
| | - Jennifer P Craig
- Department of Ophthalmology, New Zealand National Eye Centre, The University of Auckland, Auckland, New Zealand
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Saha RK, Chowdhury AMM, Na KS, Hwang GD, Eom Y, Kim J, Jeon HG, Hwang HS, Chung E. Automated quantification of meibomian gland dropout in infrared meibography using deep learning. Ocul Surf 2022; 26:283-294. [PMID: 35753666 DOI: 10.1016/j.jtos.2022.06.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 06/18/2022] [Accepted: 06/20/2022] [Indexed: 11/25/2022]
Abstract
PURPOSE Develop a deep learning-based automated method to segment meibomian glands (MG) and eyelids, quantitatively analyze the MG area and MG ratio, estimate the meiboscore, and remove specular reflections from infrared images. METHODS A total of 1600 meibography images were captured in a clinical setting. 1000 images were precisely annotated with multiple revisions by investigators and graded 6 times by meibomian gland dysfunction (MGD) experts. Two deep learning (DL) models were trained separately to segment areas of the MG and eyelid. Those segmentation were used to estimate MG ratio and meiboscores using a classification-based DL model. A generative adversarial network was implemented to remove specular reflections from original images. RESULTS The mean ratio of MG calculated by investigator annotation and DL segmentation was consistent 26.23% vs 25.12% in the upper eyelids and 32.34% vs. 32.29% in the lower eyelids, respectively. Our DL model achieved 73.01% accuracy for meiboscore classification on validation set and 59.17% accuracy when tested on images from independent center, compared to 53.44% validation accuracy by MGD experts. The DL-based approach successfully removes reflection from the original MG images without affecting meiboscore grading. CONCLUSIONS DL with infrared meibography provides a fully automated, fast quantitative evaluation of MG morphology (MG Segmentation, MG area, MG ratio, and meiboscore) which are sufficiently accurate for diagnosing dry eye disease. Also, the DL removes specular reflection from images to be used by ophthalmologists for distraction-free assessment.
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Affiliation(s)
- Ripon Kumar Saha
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - A M Mahmud Chowdhury
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - Kyung-Sun Na
- Department of Ophthalmology, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Gyu Deok Hwang
- Department of Ophthalmology, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Youngsub Eom
- Department of Ophthalmology, Korea University College of Medicine, Seoul, South Korea
| | - Jaeyoung Kim
- Department of Ophthalmology, Chungnam National University School of Medicine, Daejeon, South Korea
| | - Hae-Gon Jeon
- AI Graduate School, Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - Ho Sik Hwang
- Department of Ophthalmology, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea.
| | - Euiheon Chung
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, South Korea; AI Graduate School, Gwangju Institute of Science and Technology, Gwangju, South Korea.
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