Skip to main content
OpenTrials
Not Yet Recruiting

NCT Number: NCT07668193

GlaukomAI: Clinical Validation of an AI System for Early Glaucoma Screening

Glaucoma is one of the leading causes of irreversible blindness worldwide. Early diagnosis is crucial to prevent vision loss, but current diagnostic pathways require multiple specialist visits and tests, leading to long waiting times and delayed diagnosis.

This study aims to evaluate the accuracy of GlaukomAI, an artificial intelligence (AI)-based software that analyzes fundus photographs of the eye to detect glaucoma at an early stage.

The study is conducted at IRCCS Fondazione G. B. Bietti (Rome, Italy) and is structured in two phases:

* Phase 1 enrolls 200 participants (100 with diagnosed glaucoma and 100 healthy controls) to assess how accurately GlaukomAI can distinguish between glaucoma and healthy eyes, compared to the judgment of a panel of three expert glaucoma specialists. * Phase 2 enrolls 1,000 consecutive outpatients to evaluate whether GlaukomAI can correctly identify patients who need referral to a glaucoma specialist, and to compare its performance with that of non-specialist ophthalmologists.

Participants undergo a single study visit including standard ophthalmic examinations (visual acuity, eye pressure measurement, visual field test, OCT, and fundus photography). No investigational drugs or invasive procedures are involved.

The results of this study will provide evidence to support the integration of AI-based tools into routine glaucoma screening pathways, with the goal of reducing diagnostic delays and improving access to care.

Not Yet Recruiting

Trial opening soon.

Get Notified

Key information

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

About this study

Glaucoma is a chronic optic neuropathy representing one of the leading causes of irreversible blindness worldwide, with an estimated 111.8 million cases projected by 2040. Despite the availability of effective treatments, approximately 50% of affected individuals remain undiagnosed, as the disease progresses insidiously and symptoms often appear only when damage is already advanced and irreversible.

Current diagnostic limitations include high inter-operator variability in optic disc assessment, limited sensitivity of visual field testing in early stages, and suboptimal specificity of OCT (estimated at 72% in a Cochrane systematic review). No single examination provides sufficient diagnostic accuracy, accessibility, and cost-effectiveness for large-scale screening.

GlaukomAI (Sens-vue GlaukomAI) is an AI-based diagnostic software using deep learning with Convolutional Neural Network and Transformer architecture. It analyzes standard fundus photographs to detect key glaucoma biomarkers (neuroretinal rim appearance, inferior and superior sectors) and provides a diagnostic classification (Referable Glaucoma / Non-Referable Glaucoma) within 2-8 seconds per image. The system was trained on over 100,000 fundus images from diverse ethnicities, annotated by 30 eye care professionals and validated by 243 ophthalmologists and 208 optometrists across Europe.

Study Design

This is a prospective interventional clinical investigation with a non-CE-marked medical device, structured in two complementary phases:

  • Phase 1 - Case-Control Diagnostic Accuracy Study: 200 participants (100 with diagnosed glaucoma, 100 healthy controls) are enrolled to assess the sensitivity and specificity of GlaukomAI against a gold standard defined by the consensus of a panel of three expert glaucoma specialists, based on multimodal assessment (fundus photography, OCT, and visual field).
  • Phase 2 - Prospective Referral Accuracy Assessment: 1,000 consecutive outpatients attending IRCCS Fondazione Bietti for any clinical reason are enrolled to evaluate the referral accuracy of GlaukomAI (binary output: Referable / Non-Referable) in a real-world setting, and to compare its performance with that of non-glaucoma-specialist ophthalmologists evaluating the same pseudonymized fundus images.

All participants undergo a single study visit (or two visits within one week if needed) including: best-corrected visual acuity measurement, slit-lamp biomicroscopy, Goldmann applanation tonometry, Humphrey visual field testing (24-2 SITA Standard or SITA Faster), fundus examination with Cup-to-Disc Ratio assessment, fundus photography using a widefield TrueColor Confocal imaging system (iCare DRS Plus), and retinal nerve fiber layer (RNFL) and ganglion cell layer (GCL+IPL) thickness assessment via Cirrus HD-OCT (Carl Zeiss). No investigational drugs or invasive procedures beyond standard clinical practice are involved.

Statistical Analysis For Phase 1, sample size was calculated to detect an expected sensitivity and specificity of 88% with 95% confidence and ±8% precision, yielding 100 subjects per group. For Phase 2, enrollment of 1,000 patients allows estimation of real-world sensitivity and specificity with ±5% precision, assuming a 10% glaucoma prevalence in a tertiary referral center. Both eyes will be included in the analysis using generalized estimating equations (GEE) or mixed-effects models to account for intra-subject correlation. Diagnostic performance metrics (sensitivity, specificity, PPV, NPV, AUC) will be calculated with 95% confidence intervals. Agreement between methods will be assessed using Cohen's kappa; comparisons will use McNemar's test.

Funding This study is funded under the Transforming Health and Care Systems (THCS) partnership, co-funded by the EU Horizon Europe Research and Innovation Programme (Grant Agreement No. 101095654).

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

for all patients:

  • Age >18 years
  • Freely given informed consent obtained prior to study initiation
  • The participant has the capacity to understand and the willingness to follow study instructions and is likely to complete all required visits and procedures

Inclusion criteria

for glaucoma patients:

Patients affected by any type of glaucoma (primary open-angle, primary angle-closure, secondary glaucoma) on pharmacological therapy

Inclusion criteria

for healthy controls:

  • Absence of ocular pathologies
  • IOP <21 mmHg
  • Visual field and OCT within normal limits
  • Optic disc of normal appearance on clinical evaluation

Exclusion criteria

  • Presence of media opacities preventing the acquisition of adequate quality fundus imaging (e.g., advanced cataract, vitreous hemorrhage, severe corneal opacities)
  • Retinal or optic nerve pathologies that could confound the diagnosis (e.g., non-glaucomatous optic neuropathies (ischemic, inflammatory, compressive), moderate-to-severe diabetic retinopathy, advanced macular degeneration, retinal vascular occlusions)
  • Having undergone any ocular surgery in the past 3 months
  • Inability to cooperate with perimetric examination

Treatment and study plan

GlaukomAI (Sens-vue GlaukomAI)

Device

GlaukomAI is an AI-based diagnostic software (Sens-vue ApS) that analyzes standard fundus photographs to detect glaucomatous changes. The system uses deep learning with Convolutional Neural Network and Transformer architecture to evaluate key glaucoma biomarkers, including neuroretinal rim appearance in the inferior and superior sectors. It accepts standard fundus images acquired with conventional fundus cameras or portable devices and provides a diagnostic classification (Referable Glaucoma / Non-Referable Glaucoma) within 2-8 seconds per image. The system is not CE-marked. Fundus images are acquired using a widefield TrueColor Confocal fundus imaging system (iCare DRS Plus), pseudonymized, and uploaded to the GlaukomAI secure platform by an operator blinded to the clinical diagnosis.

Primary outcomes

  1. Diagnostic Accuracy of GlaukomAI - Sensitivity and Specificity

    Time frame: At enrollment visit (single visit, or two consecutive visits within 1 week)

    Sensitivity and specificity of GlaukomAI in the diagnosis of glaucoma, calculated against the gold standard defined by the consensus of a panel of three expert glaucoma specialists based on multimodal assessment (fundus photography, OCT, and visual field). Additional metrics include positive predictive value (PPV), negative predictive value (NPV), and area under the ROC curve (AUC) with 95% confidence intervals. The optimal diagnostic cut-off will be identified using the Youden index.

Secondary outcomes

  1. Referral Accuracy of GlaukomAI vs. Non-Specialist Ophthalmologists

    Time frame: At enrollment visit

    Sensitivity and specificity of GlaukomAI in recommending referral to a glaucoma specialist (binary output: Referable / Non-Referable), compared to the gold standard and to the independent referral decisions of non-glaucoma-specialist ophthalmologists evaluating the same pseudonymized fundus images.

  2. Diagnostic Agreement - Cohen's Kappa

    Time frame: At enrollment visit

    Diagnostic agreement between GlaukomAI and the gold standard, and between non-glaucoma-specialist ophthalmologists and the gold standard, assessed using Cohen's kappa coefficient (κ). Comparison between diagnostic methods on the same subjects will be performed using McNemar's test.

Study contacts

Contact information is provided by the study sponsor or research team.

Carmela Carnevale, MD

CONTACT

[email protected]

+39 06 84009442

Francesco Oddone, MD, PhD

CONTACT

[email protected]

+39 06 84009442

Sponsors and collaborators

Lead sponsor

Fondazione G.B. Bietti, IRCCS

Other

Registry information

Official study title

GlaukomAI: Clinical Validation of an Artificial Intelligence-Based System for Early Glaucoma Screening and Diagnosis - A Case-Control Study and Referral Accuracy Assessment

Acronym: GlaukomAIcare

Important dates

Study start
2026
Primary completion
2027
Study completion
2028
First posted
Jun 25, 2026
Registry last updated
Jun 25, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.