Evidence map›Paper›PMID 41981030›Full record

ArticleScientific reports2026

Artificial intelligence-driven virtual reality eye-tracking for the objective measurement of MRD1 and MRD2 in blepharoptosis.

Reenette Savant, Swati Parida, Callum Hunt, Irem Karaer, Mario Frank Farrugia, Agni Mokka, Steven Isherwood, Vanessa Rodwell, Gail D E Maconachie, Seema Teli and 8 more

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Reenette Savant *Ulverscroft Eye Unit, School of Psychology and Vision Sciences, University of Leicester, Leicester, LE2 7LX, UK.
Swati Parida *Department of Ophthalmology, Leicester Royal Infirmary, University Hospitals of Leicester NHS Trust, Infirmary Square, Leicester, LE1 5WW, UK.
Callum Hunt *Ulverscroft Eye Unit, School of Psychology and Vision Sciences, University of Leicester, Leicester, LE2 7LX, UK.
Irem KaraerUlverscroft Eye Unit, School of Psychology and Vision Sciences, University of Leicester, Leicester, LE2 7LX, UK.
Mario Frank FarrugiaDepartment of Ophthalmology, Leicester Royal Infirmary, University Hospitals of Leicester NHS Trust, Infirmary Square, Leicester, LE1 5WW, UK.
Agni MokkaDepartment of Ophthalmology, Leicester Royal Infirmary, University Hospitals of Leicester NHS Trust, Infirmary Square, Leicester, LE1 5WW, UK.
Steven IsherwoodDepartment of Ophthalmology, Leicester Royal Infirmary, University Hospitals of Leicester NHS Trust, Infirmary Square, Leicester, LE1 5WW, UK.
Vanessa RodwellUlverscroft Eye Unit, School of Psychology and Vision Sciences, University of Leicester, Leicester, LE2 7LX, UK.
Gail D E MaconachieUlverscroft Eye Unit, School of Psychology and Vision Sciences, University of Leicester, Leicester, LE2 7LX, UK.
Seema TeliUlverscroft Eye Unit, School of Psychology and Vision Sciences, University of Leicester, Leicester, LE2 7LX, UK.
Michael HisaundUlverscroft Eye Unit, School of Psychology and Vision Sciences, University of Leicester, Leicester, LE2 7LX, UK.
Zhanhan TuUlverscroft Eye Unit, School of Psychology and Vision Sciences, University of Leicester, Leicester, LE2 7LX, UK.
Antonella Berry-BrincatDepartment of Ophthalmology, Leicester Royal Infirmary, University Hospitals of Leicester NHS Trust, Infirmary Square, Leicester, LE1 5WW, UK.
Ian De SilvaDepartment of Ophthalmology, Leicester Royal Infirmary, University Hospitals of Leicester NHS Trust, Infirmary Square, Leicester, LE1 5WW, UK.
Joyce BurnsDepartment of Ophthalmology, Leicester Royal Infirmary, University Hospitals of Leicester NHS Trust, Infirmary Square, Leicester, LE1 5WW, UK.
Mary AwadDepartment of Ophthalmology, Leicester Royal Infirmary, University Hospitals of Leicester NHS Trust, Infirmary Square, Leicester, LE1 5WW, UK.
Raghavan SampathDepartment of Ophthalmology, Leicester Royal Infirmary, University Hospitals of Leicester NHS Trust, Infirmary Square, Leicester, LE1 5WW, UK.
Mervyn G ThomasUlverscroft Eye Unit, School of Psychology and Vision Sciences, University of Leicester, Leicester, LE2 7LX, UK. mt350@le.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate measurement of eyelid position is essential for diagnosis and surgical planning in blepharoptosis, but traditional manual assessment using pen torch and ruler is subject to inter-observer variability and lack of standardisation. We evaluated an artificial intelligence-enabled virtual reality eye-tracking system to provide an objective alternative to current manual methods. In 101 patients with suspected blepharoptosis, the BulbiCAM device employed high-speed infrared video-oculography with convolutional neural network algorithms to automatically detect the pupil centre, corneal glint, and eyelid margins. When benchmarked against standard clinical assessment to establish validity, automated measurements showed high agreement, with mean differences of + 0.29 mm for upper eyelid position and + 0.01 mm for lower eyelid position. Critically, the device demonstrated high precision, with test-retest repeatability in 36 patients showing mean differences of - 0.03 mm and + 0.27 mm between visits. Successful acquisition was achieved in all participants with a median of one attempt, and 98% reported the device as comfortable and easy to use. This study demonstrates that AI-enabled VR eye-tracking matches the clinical accuracy of current standards while offering the repeatability and objectivity required to overcome the limitations of manual assessment.

Indexed as

Artificial IntelligenceBlepharoptosisEye-Tracking TechnologyVirtual RealityAdultAgedAlgorithmsEyelidsFemaleHumansMaleMiddle AgedReproducibility of ResultsArtificial intelligenceBlepharoptosisFeasibilityPtosisReproducibilityVirtual reality

Identifiers

PMID41981030
PMCPMC13237289

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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.