An interventional study of GlaukomAI (Sens-vue GlaukomAI) in Glaucoma, sponsored by Fondazione G.B. Bietti, IRCCS. Not yet recruiting. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-09-04.
Sponsored by Fondazione G.B. Bietti, IRCCS · Not applicable, Interventional, and Diagnostic
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:
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.
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:
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).
1,419 studies on the registry are indexed under Glaucoma; 223 are open to participants now.
This study's planned enrollment of 1,200 is above the median of 61 across 964 interventional studies indexed under Glaucoma.
Browse Glaucoma studies →Fondazione G.B. Bietti, IRCCS is the lead sponsor of 19 studies on the registry; 7 are open to participants now.
Counted across the registry records on this site, refreshed daily.
Inclusion Criteria for all patients:
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:
Exclusion Criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
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)
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.
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)
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 visit
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 visit
No study locations are listed for this record.
Plan to share: No
Start is estimated for Oct 2026; primary completion for Nov 2027, 13 months later.
Of 229 non-industry interventional studies with no phase under Glaucoma, half reached primary completion within 31 months of starting (the median), counting those still running; a quarter within 12 and three quarters within 55. This study's planned 13 months is below that.
Counting every sponsor, half of 287 studies reached it within 30 months; a quarter within 12 and three quarters within 53.
How these figures are worked out, and which studies are left out of them.
This study is not yet recruiting, as verified in Sep 2026. You cannot join it, but the record below documents what was studied.
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Fondazione G.B. Bietti, IRCCS