Infrared Imaging of Meibomian Gland Structure Using a Novel Keratograph
Bibliographic record
Abstract
ABSTRACT Purpose. To examine the ability of a novel non‐contact device (Keratograph 4) to image the meibomian gland (MG) structures and their morphological changes in the upper and lower eyelids. Methods. Thirty‐seven participants (mean age 57.8 ± 8.5 years; 3 males and 34 females) completed the Ocular Surface Disease Index questionnaire to assess dryness symptoms. Meibum secretion quality score, number of blocked gland orifices, and meibum expressibility scores were assessed. The lower lid (LL) and upper lid (UL) of all subjects were everted and images of the MGs were taken using the Keratograph 4 (OCULUS). A MG dropout score (MGDS) due to complete or partial gland loss of both lids was obtained using a subjective 4‐grade scoring system, and digital analysis of the images using ImageJ was performed. Presence of tortuosity and visible acinar changes of the MGs were also noted. Results. MGDS for both lids was significantly positively correlated with the Ocular Surface Disease Index score (r = 0.51; p < 0.05). The MGDS determined using the digital grading was also significantly positively correlated (UL: r = 0.68, p < 0.05; LL: r = 0.42, p < 0.05). The sum of the MGDS for both lids using the subjective grading scale was significantly different between the non‐MGD and MGD group (1.3 ± 1.0 vs. 3.1 ± 1.1; p = 0.0004). MGDS assessment using the digital grading was significantly different between non‐MGD (UL = 6%, LL = 8%) and MGD group (UL = 32%, LL = 42%; p = 0.001). Tortuous MG was observed only on the UL in 6% of the participants. Visible acinar changes were noted in 40% of the study participants. Conclusions. Infrared meibography is now possible in a clinical setting using commercially available devices, and meibography can help determine differences in MG structure in subjects symptomatic of dry eye.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".