ArticleAnnals of medicine2021
Deep learning-based image analysis for automated measurement of eyelid morphology before and after blepharoptosis surgery.
Article in Annals of medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- A multi-category eye image dataset for AI-based segmentation and analysis of eyelid disorders.Scientific data · 2026Article
- 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
- Artificial Intelligence in Non-Surgical Cosmetic Procedures: A Multi-Stakeholder Revolution.Aesthetic plastic surgery · 2026Review
- A comprehensive system for eyelid analysis using deep learning: automated measurement of eyelid position and corneal exposure.Biomedical engineering letters · 2026Article
- Artificial intelligence-driven virtual reality eye-tracking for the objective measurement of MRD1 and MRD2 in blepharoptosis.Scientific reports · 2026Article
- From blink to care: smartphone video-based functional analysis and personalized management in pediatric blepharoptosis.NPJ digital medicine · 2026Article
- Deep Learning-based Assessment of Eyelid and Periorbital Parameters: Assisting Diagnosis and Treatment Planning in Blepharoptosis.Journal of medical systems · 2026Article
- VisionMetric Suite for periocular measurement in ophthalmic plastic surgery: a preliminary single-center validation study.Frontiers in medicine · 2026Article
- Deep-Learning Model for Iris and Eyebrow Segmentation and Automation of Eye Landmark Measurements in Acquired Ptosis.Biomedicines · 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
- AI-assisted facial analysis in healthcare: From disease detection to comprehensive management.Patterns (New York, N.Y.) · 2025Review
- AI-driven Eyeball Exposure Rate (EER) analysis: A useful tool for assessing ptosis surgery effectiveness.PloS one · 2025Article
- Multidimensional quantitative characterization of periocular morphology: distinguishing esotropia from epicanthus by deep learning network.Quantitative imaging in medicine and surgery · 2024Article
- Global research of artificial intelligence in eyelid diseases: A bibliometric analysis.Heliyon · 2024Article
- Quantitative analysis of the eyelid curvature in patients with blepharoptosis.BMC medical imaging · 2024Article
- Development of a neural network-based automated eyelid measurement system.Scientific reports · 2024Article
- A Narrative Review of Artificial Intelligence (AI) for Objective Assessment of Aesthetic Endpoints in Plastic Surgery.Aesthetic plastic surgery · 2023Review
Corrections and comments
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimEyelid position and contour abnormality could lead to various diseases, such as blepharoptosis, which is a common eyelid disease. Accurate assessment of eyelid morphology is important in the management of blepharoptosis. We aimed to proposed a novel deep learning-based image analysis to automatically measure eyelid morphological properties before and after blepharoptosis surgery.
methodsThis study included 135 ptotic eyes of 103 patients who underwent blepharoptosis surgery. Facial photographs were taken preoperatively and postoperatively. Margin reflex distance (MRD) 1 and 2 of the operated eyes were manually measured by a senior surgeon. Multiple eyelid morphological parameters, such as MRD1, MRD2, upper eyelid length and corneal area, were automatically measured by our deep learning-based image analysis. Agreement between manual and automated measurements, as well as two repeated automated measurements of MRDs were analysed. Preoperative and postoperative eyelid morphological parameters were compared. Postoperative eyelid contour symmetry was evaluated using multiple mid-pupil lid distances (MPLDs).
resultsThe intraclass correlation coefficients (ICCs) between manual and automated measurements of MRDs ranged from 0.934 to 0.971 (
conclusionThis technique had high accuracy and repeatability for automatically measuring eyelid morphology, which allows objective assessment of blepharoptosis surgical outcomes. Using only patients' photographs, this technique has great potential in diagnosis and management of other eyelid-related diseases.
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