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

Sponsor
Fondazione G.B. Bietti, IRCCS
Study ID
NCT07668193
Status
Not Yet Recruiting

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Conditions

Eligibility Criteria

Sex
ALL
Age
18 Years - N/A
Healthy Volunteers
Accepted

Interventions

  • 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.

Study Details

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.

Key Dates

First listed
Jun 25, 2026
Start date
Jun 30, 2026
Status verified
Jun 2026
Primary completion
Nov 30, 2027
Completion
May 31, 2028

Study Design

Enrollment
1,200 participants (estimated)
Allocation
NON_RANDOMIZED
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC

Arms

  • 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.
  • Other: Healthy Controls
    Participants without ocular pathology and with normal ophthalmological examination undergoing standard ophthalmological examination and AI-based image analysis.

Primary Outcome Measure

Diagnostic Accuracy of GlaukomAI - Sensitivity and Specificity [ Time Frame: At enrollment visit (single visit, or two consecutive visits within 1 week) ]

Central Contacts

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