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

June 19, 2026 updated by: Fondazione G.B. Bietti, IRCCS

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

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

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).

Study Type

Interventional

Enrollment (Estimated)

1200

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: Non-Randomized
  • Interventional Model: Single Group Assignment
  • Masking: Double

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Other: Glaucoma Patients
Participants with diagnosed glaucoma (primary open-angle, primary angle-closure or secondary glaucoma) undergoing standard ophthalmological examination and AI-based image analysis.
Device: GlaukomAI (Sens-vue GlaukomAI)
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.
Other: Healthy Controls
Participants without ocular pathology and with normal ophthalmological examination undergoing standard ophthalmological examination and AI-based image analysis.
Device: GlaukomAI (Sens-vue GlaukomAI)
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
At enrollment visit (single visit, or two consecutive visits within 1 week)

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
At enrollment visit
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.
At enrollment visit

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Fondazione G.B. Bietti, IRCCS

Investigators

  • Principal Investigator: Francesco Oddone, MD, PhD, IRCCS Fondazione G. B. Bietti, Rome, Italy

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

June 1, 2026

Primary Completion (Estimated)

November 1, 2027

Study Completion (Estimated)

May 1, 2028

Study Registration Dates

First Submitted

June 19, 2026

First Submitted That Met QC Criteria

June 19, 2026

First Posted (Actual)

June 25, 2026

Study Record Updates

Last Update Posted (Actual)

June 25, 2026

Last Update Submitted That Met QC Criteria

June 19, 2026

Last Verified

June 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • GLC 02-26
  • 101095654 (Other Grant/Funding Number: EU Horizon Europe Research and Innovation Programme)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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