ArticleScientific reports2024
Development of a neural network-based automated eyelid measurement system.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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.
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Who cites it
11 citing papers in PubMed, 10 citations in OpenAlex.
- Symmetry of the Upper Eyelid Following Bilateral Blepharoptosis Repair Using a Minimally Invasive Conjoint Fascial Sheath Suspension Technique.Aesthetic plastic surgery · 2026Article
- Marker-Free, Automated Eyelid Assessment in Thyroid Eye Disease Using Artificial Intelligence: A Multicenter Validation Study.Ophthalmology science · 2026Article
- VisionMetric Suite for periocular measurement in ophthalmic plastic surgery: a preliminary single-center validation study.Frontiers in medicine · 2026Article
- STB-Net: a Siamese architecture-based reconstruction-segmentation network for ocular surface image segmentation.Quantitative imaging in medicine and surgery · 2025Article
- Oculoplastics and Augmented Intelligence: A Literature Review.Journal of clinical medicine · 2025Article
- Deep learning based adaptive and automatic measurement of palpebral margin in eyelid morphology.Scientific reports · 2025Article
- The Usefulness of AI-Based Cornea Exposure Rate (CER) Analysis Utilizing the Anigma View System in Evaluating Ptosis Surgery Outcomes.Journal of clinical medicine · 2025Article
- Dual U-Net with multi-task attention for automated eyelid curvature quantification.Frontiers in medicine · 2025Article
- AI-driven Eyeball Exposure Rate (EER) analysis: A useful tool for assessing ptosis surgery effectiveness.PloS one · 2025Article
- Effective encoder-decoder network for pupil light reflex segmentation in facial photographs of ptosis patients.Scientific reports · 2024Article
- Development and Validation of a Semiautomated Tool for Measuring Periorbital Distances.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
The purpose of this study was to assess the clinical utility and reliability of an automated eyelid measurement system utilizing neural network (NN) technology. Digital images of the eyelids were taken from a total of 300 subjects, comprising 100 patients with Graves' orbitopathy (GO), 100 patients with ptosis, and 100 controls. An automated measurement system based on NNs was developed to measure margin-reflex distance 1 and 2 (MRD1 and MRD2), as well as the lengths of the upper and lower eyelids. The results were then compared with values measured using the manual technique. Automated measurements of MRD1, MRD2, upper eyelid length, and lower eyelid length yielded values of 3.2 ± 1.7 mm, 6.0 ± 1.4 mm, 32.9 ± 6.1 mm, and 29.0 ± 5.6 mm, respectively, showing a high level of agreement with manual measurements. To evaluate the morphometry of curved eyelids, the distance from the midpoint of the intercanthal line to the eyelid margin was measured. The minimum number of divisions for detecting eyelid abnormalities was determined to be 24 partitions (15-degree intervals). In conclusion, an automated NN-based measurement system could provide a straightforward and precise method for measuring MRD1 and MRD2, as well as detecting morphological abnormalities in the eyelids.
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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.