GlaukomAI: Clinical Validation of an AI System for Early Glaucoma Screening
ConditionGlaucoma
Trial statusNot yet recruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18+
SponsorFondazione G.B. Bietti, IRCCS
About this trial
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.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
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
Absence of ocular pathologies
Disqualifiers
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
Trial design
Design model
Single group
Treatments tested in this trial
GlaukomAI (Sens-vue GlaukomAI)
DeviceGlaukomAI 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.
Treatment groups
1,200 Participants
are divided into 2 treatment groupsGroup A: Glaucoma PatientsOther 1 intervention
Group B: Healthy ControlsOther 1 intervention
Trial outcomes
Primary outcomes
1
Diagnostic Accuracy of GlaukomAI - Sensitivity and Specificity
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.
Time frame
At enrollment visit (single visit, or two consecutive visits within 1 week)Secondary outcomes
1
Referral Accuracy of GlaukomAI vs. Non-Specialist Ophthalmologists
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.
Time frame
At enrollment visit2
Diagnostic Agreement - Cohen's Kappa
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.
Time frame
At enrollment visitLocations
Sponsors and contacts
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Source ClinicalTrials.gov
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