ArticleEuropean journal of ophthalmology2025
Artificial intelligence to automate assessment of ocular and periocular measurements.
Article in European journal of ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Simulated Workflow Feasibility Evaluation of a Web-Based Periorbital Measurement Platform: Development and Usability Study.JMIR human factors · 2026Article
- Artificial intelligence-driven virtual reality eye-tracking for the objective measurement of MRD1 and MRD2 in blepharoptosis.Scientific reports · 2026Article
- FaceFinder: A machine learning tool for identification of facial images from heterogenous datasets.AJO international · 2024Article
- Development and Validation of a Semiautomated Tool for Measuring Periorbital Distances.Ophthalmology scienceArticle
- Open-Source Periorbital Segmentation Dataset for Ophthalmic Applications.Ophthalmology scienceArticle
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7 authors.
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Abstract
purposeTo develop and validate a deep learning facial landmark detection network to automate the assessment of periocular anthropometric measurements.
methodsPatients presenting to the ophthalmology clinic were prospectively enrolled and had their images taken using a standardised protocol. Facial landmarks were segmented on the images to enable calculation of marginal reflex distance (MRD) 1 and 2, palpebral fissure height (PFH), inner intercanthal distance (IICD), outer intercanthal distance (OICD), interpupillary distance (IPD) and horizontal palpebral aperture (HPA). These manual segmentations were used to train a machine learning algorithm to automatically detect facial landmarks and calculate these measurements. The main outcomes were the mean absolute error and intraclass correlation coefficient.
resultsA total of 958 eyes from 479 participants were included. The testing set consisted of 290 eyes from 145 patients. The AI algorithm demonstrated close agreement with human measurements, with mean absolute errors ranging from 0.22 mm for IPD to 0.88 mm for IICD. The intraclass correlation coefficients indicated excellent reliability (ICC > 0.90) for MRD1, MRD2, PFH, OICD, IICD, and IPD, while HPA showed good reliability (ICC 0.84). The landmark detection model was highly accurate and achieved a mean error rate of 0.51% and failure rate at 0.1 of 0%.
conclusionThe automated facial landmark detection network provided accurate and reliable periocular measurements. This may help increase the objectivity of periocular measurements in the clinic and may facilitate remote assessment of patients with tele-health.
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