ArticleOphthalmology science
Development and Validation of a Semiautomated Tool for Measuring Periorbital Distances.
Article in Ophthalmology science. 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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Who cites it
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
- Objective quantification of chemotherapy-induced madarosis: a pilot study of an automated computer vision pipeline for eyebrow density assessment.Scientific reports · 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
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Purpose: To validate a custom FIJI (ImageJ) program for more reproducible, faster curvilinear periorbital measurements, as compared with 2 custom artificial intelligence-based tools. Design: Combined technical validation and method comparison study. Subjects: Front-facing photographs of 45 cleft palate syndromic patients. Methods: A FIJI (ImageJ)-based macro script tool, OrbitJ (semiautomated), was developed, requiring 15 user input steps to generate 38 measurements per photo. The user outlines the irises to set image scale and then selects points along the lid margins and brow lines. Linear interpolation and fourth-degree polynomial fit lines were used to generate periorbital measurements. This tool was compared against our previously developed deep learning algorithm for periorbital measurements, OrbitMap (automated), another open-source algorithm PeriOrbitAI (automated), and against manual measurements. Four human graders measured 45 photos once both manually and with OrbitJ. Intrarater and interrater measurements were performed with 10 photos manually, 3 in triplicate manually and 5 in triplicate with OrbitJ. Fourteen manual measurements were performed: time per image, iris diameter, margin reflex distance (MRD) 1 and 2, inferior scleral show (ISS), medial, central, and lateral brow height, canthal tilt, vertical dystopia, interpupillary distance, and inner and outer canthal distance (OCD). Main Outcome Measures: The mean absolute error, reliability, bias, and Pearson correlation of periorbital measurements. Results: Analysis was successful in all 45 images for all methods except PeriOrbitAI, which failed on 6 images. For manual and semiautomated intra- and interrater measurements, reliability was considered moderate or better (intraclass correlation coefficient [ICC] >0.5) for all measurements excluding elapsed time. Manual interrater mean absolute error was <1 mm all measures except OCD. Reliability and correlation were high (ICC, Pearson correlation coefficient >0.8) between all OrbitJ and manual measurements. Comparing OrbitMap to manual measurements, ICC and Pearson correlation coefficient were >0.5 except for ISS, borderline for OCD (ICC = 0.51). Reliability between PeriOrbitAI and manual measurements was low (ICC <0.5) except for MRD2 and OCD, and correlation was moderate (Pearson correlation coefficient = 0.49-0.75). The mean analysis time per image was 13.4 ± 4 minutes (manual measurements), 5.4 ± 1.9 minutes (semiautomated OrbitJ), 10.71 ± 1.65 seconds (automated PeriOrbitAI), and 1.45 ± 0.15 seconds (automated OrbitMap) ( Conclusions: Compared with manual measurements, all semiautomated OrbitJ measurements and most automated OrbitMap measurements were reliable. Notably, only 2 PeriOrbitAI measurements were reliable. All methods demonstrated significant time savings over manual measurements. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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