Observational studyIndian journal of ophthalmology2023
Automatic segmentation and quantified analysis of meibomian glands from infrared images.
Observational study in Indian journal of ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 6 citations in OpenAlex.
- WRCANet: wavelet residual cross-attention network for meibomian gland segmentation.Biomedical optics express · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: An algorithm for automated segmentation of meibomian glands from infrared images obtained using a novel prototype infrared hand-held imager has been proposed in this study. Meibomian gland dysfunction (MGD) is quantified in terms of five clinically relevant metrics. A comparison of these metrics in patients with MGD has been presented against a sample of the normative healthy population. Methods: This is a prospective cross-sectional observational study. Patients presenting to the clinics were enrolled after written informed consent. The everted eyelids of 200 eyes of patients (of which 100 were healthy and 100 were diagnosed with MGD) were imaged using a prototype hand-held camera. The proposed algorithm was used to process the images using enhancement techniques and the glands were automatically segmented. A comparison of glands of normal eyes versus MGD-affected eyes is performed using five metrics presented in this study: (i) drop-out, (ii) length, (iii) width, (iv) the number of glands, and (v) the number of tortuous glands. Results: The 95% confidence interval for the metrics did not show any overlap between the two groups. In MGD patients, the drop-out ratio was higher than normal. The length and number of glands were significantly lesser than normal. A number of tortuous glands were more in the MGD group. The metrics for MGD versus healthy and cut-off ranges were computed in the results. Conclusion: The prototype infrared hand-held meibographer and the proposed automatic algorithm for gland segmentation and quantification are effective aids in MGD diagnosis. We present a set of five metrics, which are clinically relevant for guiding clinicians in the diagnosis of MGD.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.