Analysis of the Differences in Ocular Surface Damage and Inflammatory Signs between Healthy and Evaporative Dry Eye Participants

Garcia-Queiruga, Jacobo; Pena-Verdeal, Hugo; Sabucedo-Villamarin, Belén; García-Resúa, Carlos; Giráldez, María Jesús; Yebra‐Pimentel, Eva

doi:10.1080/09273948.2022.2075398

Cited by 10 publications

(12 citation statements)

References 42 publications

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“…These key parameters were shown to be increased in severe DED. 28 The adopted approach integrated both objective parameters and subjective assessments, such as the OSDI and DEQ-5 to encompass the full spectrum of clinically available tools. Intriguingly, the DEQ-5 questionnaire proved to be a valuable parameter for osmolarity prediction, whereas the OSDI did not exhibit the same level of predictive utility (Tables 1-3).…”

Section: Discussionmentioning

confidence: 99%

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Machine learning‐based prediction of tear osmolarity for contact lens practice

Garaszczuk,

Romanos‐Ibanez,

Consejo

2024

Ophthalmic Physiologic Optic

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PurposeThis study addressed the utilisation of machine learning techniques to estimate tear osmolarity, a clinically significant yet challenging parameter to measure accurately. Elevated tear osmolarity has been observed in contact lens wearers and is associated with contact lens‐induced dry eye, a common cause of discomfort leading to discontinuation of lens wear.MethodsThe study explored machine learning, regression and classification techniques to predict tear osmolarity using routine clinical parameters. The data set consisted of 175 participants, primarily healthy subjects eligible for soft contact lens wear. Various clinical assessments were performed, including symptom assessment with the Ocular Surface Disease Index and 5‐Item Dry Eye Questionnaire (DEQ‐5), tear meniscus height (TMH), tear osmolarity, non‐invasive keratometric tear film break‐up time (NIKBUT), ocular redness, corneal and conjunctival fluorescein staining and Meibomian glands loss.ResultsThe results revealed that simple linear regression was insufficient for accurate osmolarity prediction. Instead, more advanced regression models achieved a moderate level of predictive power, explaining approximately 32% of the osmolarity variability. Notably, key predictors for osmolarity included NIKBUT, TMH, ocular redness, Meibomian gland coverage and the DEQ‐5 questionnaire. In classification tasks, distinguishing between low (<299 mOsmol/L), medium (300–307 mOsmol/L) and high osmolarity (>308 mOsmol/L) levels yielded an accuracy of approximately 80%. Key parameters for classification were similar to those in regression models, emphasising the importance of NIKBUT, TMH, ocular redness, Meibomian glands coverage and the DEQ‐5 questionnaire.ConclusionsThis study highlights the potential benefits of integrating machine learning into contact lens research and practice. It suggests the clinical utility of assessing Meibomian glands and NIKBUT in contact lens fitting and follow‐up visits. Machine learning models can optimise contact lens prescriptions and aid in early detection of conditions like dry eye, ultimately enhancing ocular health and the contact lens wearing experience.

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Section: Discussionmentioning

confidence: 99%

“…26 Lastly, the Meibomian glands were visualised in infrared light with the Meibo-Scan K5M tool. 27,28 Default settings were used with 0.5× magnification. Images of both the lower and upper everted eyelids were acquired and saved for further processing.…”

Section: Study Protocolmentioning

confidence: 99%

Machine learning‐based prediction of tear osmolarity for contact lens practice

Garaszczuk,

Romanos‐Ibanez,

Consejo

2024

Ophthalmic Physiologic Optic

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“…Meibography images were captured by the Keratograph 5 M topographer (OCULUS Optikgeräte GmbH, Wetzlar, Germany) which uses an infrared camera for the MG in-vivo observation [ 25 ]. MGLA was measured by the difference between the total tarsus area and the MG area measured with the ImageJ open-source software (National Institutes of Health, Bethesda, Maryland, USA) [ 25 – 27 ]. Lower tarsus images were taken by everting lower eyelids while the participants were instructed to look to the ceiling.…”

Section: Methodsmentioning

confidence: 99%

Meibomian gland secretion quality association with ocular parameters in university students during COVID- 19 restrictions

et al. 2023

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Purpose To determine if the Meibomian Gland (MG) secretion quality is associated with symptoms of ocular discomfort, hours of Video Display Terminals (VDT) use, eyelid margin abnormalities, conjunctival hyperemia, and Meibomian Gland Loss Area (MGLA) in a sample of university students. Methods An online survey that included an Ocular Surface Disease Index (OSDI) questionnaire and an extra question about hours of VDT use recruited an initial sample of 183 participants. Only 120 participants that fulfilled the inclusion criteria were scheduled for a battery of ocular surface and MG specific exam. The tests include: 1) meibometry, 2) slit lamp exploration of eyelid margin abnormalities (irregularity, hyperemia and MG orifices plugging), MG secretion quality and conjunctival hyperemia, and 3) Meibography. Results Significant positive correlations between the MG secretion quality and eyelid margin hyperemia, MG orifices plugging, MGLA, nasal conjunctival hyperemia, and temporal conjunctival hyperemia (Spearman Rho; all r>0.186, p<0.042) were found. Multivariate regression found association between OSDI with hours of VDT use (B=0.316, p=0.007), and eyelid hyperemia (B=0.434, p≤ 0.001). A statistical association between MG secretion quality and eyelid margin hyperemia, MG orifices plugging, MGLA and conjunctival hyperemia (Fisher’s exact; all p<0.039) were found. Multivariate regression found association between MG secretion quality with MG orifices plugging (B=0.295, p=0.004) and meibometry (B=-0.001, p=0.029). Conclusion Participants with higher values in MG secretion quality have higher values in eyelid margin hyperemia, MG plugging, MGLA, and conjunctival hyperemia. No direct relationship between MG secretion quality and hours of VDT use or OSDI were found.

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“…Meibography was performed using the OCULUS Keratograph 5M topographer that have and infra-red camera that allows to observe the MG [3,6,8,12]. Upper and lower eyelids were everted for the meibography image capturing.…”

Section: Meibography Images Evaluationmentioning

confidence: 99%

“…Also, MG status can be examined with infra-red illumination obtaining meibography images [2]. Meibography images allow clinicians to calculate the Meibomian Gland Loss Area (MGLA) to grade the disease severity [6][7][8]. Although the meibomian gland dysfunction signs were identified there is not clear how they influence each other.…”

Section: Introductionmentioning

confidence: 99%

Meibomian gland loss area and its relationship with eyelid margin hyperemia and meibomian gland orifice plugging

Garcia-Queiruga

Sabucedo-Villamarin

Pena-Verdeal

et al. 2022

J. Phys.: Conf. Ser.

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Purpose: The aim of the present study was to state a relationship between the meibomian gland loss area (MGLA), eyelid hyperemia and meibomian gland (MG) orifices plugging in a sample of university students. Material and methods: A total of 74 participants were recruited. Meibography images were obtained with the OCULUS® Keratograph 5M and MGLA was calculated using the ImageJ software; also, MGLA was categorized following the Meiboscale into 4 groups: group 1 (<25%), group 2 (25-50%), groups 3 (50-75%), and group 4 (>75%). An exhaustive slit lamp examination of both eyelids was performed. Eyelid margin hyperemia and MG orifices plugging of each eyelid were categorized following Arita et. al grading scales. Results: A significant statistical relationship was found between MG orifices plugging and MGLA for both eyelids (Fisher’s exact test; both p < 0.019). Also, correlations were obtained between lower MGLA and lower MG orifices plugging (Cramer-V = 0.583, p ≤ 0.001); and between upper MGLA and upper eyelid margin hyperemia (Cramer-V = 0.418, p = 0.023), and upper MG orifices plugging (Cramer-V = 0.413, Fisher’s exact test: p = 0.042). Conclusion: MGLA varies depending on MG orifices plugging in upper and lower eyelids; also, in upper eyelids MGLA was correlated with eyelid hyperemia.

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Analysis of the Differences in Ocular Surface Damage and Inflammatory Signs between Healthy and Evaporative Dry Eye Participants

Cited by 10 publications

References 42 publications

Machine learning‐based prediction of tear osmolarity for contact lens practice

Machine learning‐based prediction of tear osmolarity for contact lens practice

Meibomian gland secretion quality association with ocular parameters in university students during COVID- 19 restrictions

Meibomian gland loss area and its relationship with eyelid margin hyperemia and meibomian gland orifice plugging

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