GlaucomaGlaukomAIcareNCT07668193
GlaukomAI: Clinical Validation of an AI System for Early Glaucoma Screening
A study testing GlaukomAI (Sens-vue GlaukomAI) in people with Glaucoma, run by Fondazione G.B. Bietti, IRCCS.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Glaucoma
- What is being tested?
- GlaukomAI (Sens-vue GlaukomAI)
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,200 participants
- Where
- No sites listed yet
- Study dates
- Oct 2026to May 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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
- Patients affected by any type of glaucoma (primary open-angle, primary angle-closure, secondary glaucoma) on pharmacological therapy
- Absence of ocular pathologies
- IOP <21 mmHg
- Visual field and OCT within normal limits
- Optic disc of normal appearance on clinical evaluation
Who cannot join
- 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 locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: 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.
Read more
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.
Read the 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).
- Conditions
- Glaucoma
- Being tested
- GlaukomAI (Sens-vue GlaukomAI)
- Keywords
- Glaucoma, Artificial Intelligence, Fundus Photography, Glaucoma Screening, Early Diagnosis, Optic Nerve, GlaukomAI
- Medical subject headings
- Glaucoma, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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 group | Role | What they receive |
|---|---|---|
| Glaucoma Patients | Other | Participants with diagnosed glaucoma (primary open-angle, primary angle-closure or secondary glaucoma) undergoing standard ophthalmological examination and AI-based image analysis. Receives: Device: GlaukomAI (Sens-vue GlaukomAI) |
| Healthy Controls | Other | Participants without ocular pathology and with normal ophthalmological examination undergoing standard ophthalmological examination and AI-based image analysis. Receives: Device: GlaukomAI (Sens-vue GlaukomAI) |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: At enrollment visit (single visit, or two consecutive visits within 1 week)
Secondary outcomes (2)
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.
Measured: 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.
Measured: At enrollment visit
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Jun 2026
- Study startOct 2026 (estimated)
- Primary completionNov 2027 (estimated)
- Study completionMay 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for GlaucomaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Single group
- Model description
- The study is structured in two sequential phases. In Phase 1, a case-control design is used to assess diagnostic accuracy: 100 participants with diagnosed glaucoma and 100 healthy controls all undergo fundus photography analysis with GlaukomAI, compared against a gold standard defined by a panel of three expert glaucoma specialists. In Phase 2, 1,000 consecutive outpatients attending a tertiary ophthalmological centre undergo the same AI-based fundus image analysis; results are compared both to the expert panel gold standard and to the independent assessment of non-glaucoma-specialist ophthalmologists evaluating the same images.
- Masking
- Double blind
- Who is masked
- Investigator, Outcomes assessor
- Enrolment
- 1,200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Fondazione G.B. Bietti, IRCCS
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Francesco Oddone, MD, PhD, Principal investigator, IRCCS Fondazione G. B. Bietti, Rome, Italy
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07668193
- Sponsor's protocol ID
- GLC 02-26
- Other IDs
- 101095654
- Acronym
- GlaukomAIcare
- Status last verified
- Sep 2026
- First submitted
- 19 Jun 2026
- Last updated
- 4 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Li Z, He Y, Keel S, Meng W, Chang RT, He M. Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. Ophthalmology. 2018 Aug;125(8):1199-1206. doi: 10.1016/j.ophtha.2018.01.023. Epub 2018 Mar 2.
PubMed 29506863Lemij HG, Vente C, Sanchez CI, Vermeer KA. Characteristics of a Large, Labeled Data Set for the Training of Artificial Intelligence for Glaucoma Screening with Fundus Photographs. Ophthalmol Sci. 2023 Mar 17;3(3):100300. doi: 10.1016/j.xops.2023.100300. eCollection 2023 Sep.
PubMed 37113471Michelessi M, Quaranta L, Riva I, Martini E, Figus M, Frezzotti P, Agnifili L, Manni G, Miglior S, Posarelli C, Fazio S, Oddone F. Exploring the gap between diagnostic research outputs and clinical use of OCT for diagnosing glaucoma. Br J Ophthalmol. 2020 Aug;104(8):1114-1119. doi: 10.1136/bjophthalmol-2019-314607. Epub 2019 Nov 15.
PubMed 31732524Oddone F, Lucenteforte E, Michelessi M, Rizzo S, Donati S, Parravano M, Virgili G. Macular versus Retinal Nerve Fiber Layer Parameters for Diagnosing Manifest Glaucoma: A Systematic Review of Diagnostic Accuracy Studies. Ophthalmology. 2016 May;123(5):939-49. doi: 10.1016/j.ophtha.2015.12.041. Epub 2016 Feb 15.
PubMed 26891880Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology. 2014 Nov;121(11):2081-90. doi: 10.1016/j.ophtha.2014.05.013. Epub 2014 Jun 26.
PubMed 24974815Quigley HA, Broman AT. The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol. 2006 Mar;90(3):262-7. doi: 10.1136/bjo.2005.081224.
PubMed 16488940
Study at a glance
From the registry record, last updated by the sponsor on 4 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- GlaukomAI: Clinical Validation of an Artificial Intelligence-Based System for Early Glaucoma Screening and Diagnosis - A Case-Control Study and Referral Accuracy Assessment
- ClinicalTrials.gov ID
- NCT07668193
- Acronym / sponsor ID
- GlaukomAIcare / GLC 02-26
- Conditions
- Glaucoma
- Keywords
- Glaucoma, Artificial Intelligence, Fundus Photography, Glaucoma Screening, Early Diagnosis, Optic Nerve, GlaukomAI
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,200 participants (estimated)
- Lead sponsor
- Fondazione G.B. Bietti, IRCCSUniversity, hospital or other
- Study start
- Oct 2026 (estimated)
- Primary completion
- Nov 2027 (estimated)
- Study completion
- May 2028 (estimated)
- First posted
- 25 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Single group
- Masking
- Double blind (investigator, outcomes assessor)
- Number of arms
- 2
- Model description
- The study is structured in two sequential phases. In Phase 1, a case-control design is used to assess diagnostic accuracy: 100 participants with diagnosed glaucoma and 100 healthy controls all undergo fundus photography analysis with GlaukomAI, compared against a gold standard defined by a panel of three expert glaucoma specialists. In Phase 2, 1,000 consecutive outpatients attending a tertiary ophthalmological centre undergo the same AI-based fundus image analysis; results are compared both to the expert panel gold standard and to the independent assessment of non-glaucoma-specialist ophthalmologists evaluating the same images.
| Arm | Type | Receives |
|---|---|---|
| Glaucoma Patients | Other | Device: GlaukomAI (Sens-vue GlaukomAI)Participants with diagnosed glaucoma (primary open-angle, primary angle-closure or secondary glaucoma) undergoing standard ophthalmological examination and AI-based image analysis. |
| Healthy Controls | Other | Device: GlaukomAI (Sens-vue GlaukomAI)Participants without ocular pathology and with normal ophthalmological examination undergoing standard ophthalmological examination and AI-based image analysis. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (8 listed)
- 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
- Patients affected by any type of glaucoma (primary open-angle, primary angle-closure, secondary glaucoma) on pharmacological therapy
- Absence of ocular pathologies
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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. | Device | Glaucoma Patients, Healthy Controls |
Primary outcome
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.
2 secondary outcomes
Referral Accuracy of GlaukomAI vs. Non-Specialist Ophthalmologists
Time frame: At enrollment visit
Diagnostic Agreement - Cohen's Kappa
Time frame: At enrollment visit
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Sep 2026.
- Results publication: PubMed 29506863Li Z, He Y, Keel S, Meng W, Chang RT, He M. Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. Ophthalmology. 2018 Aug;125(8):1199-1206. doi: 10.1016/j.ophtha.2018.01.023. Epub 2018 Mar 2.
- Results publication: PubMed 37113471Lemij HG, Vente C, Sanchez CI, Vermeer KA. Characteristics of a Large, Labeled Data Set for the Training of Artificial Intelligence for Glaucoma Screening with Fundus Photographs. Ophthalmol Sci. 2023 Mar 17;3(3):100300. doi: 10.1016/j.xops.2023.100300. eCollection 2023 Sep.
- Results publication: PubMed 31732524Michelessi M, Quaranta L, Riva I, Martini E, Figus M, Frezzotti P, Agnifili L, Manni G, Miglior S, Posarelli C, Fazio S, Oddone F. Exploring the gap between diagnostic research outputs and clinical use of OCT for diagnosing glaucoma. Br J Ophthalmol. 2020 Aug;104(8):1114-1119. doi: 10.1136/bjophthalmol-2019-314607. Epub 2019 Nov 15.
- Results publication: PubMed 26891880Oddone F, Lucenteforte E, Michelessi M, Rizzo S, Donati S, Parravano M, Virgili G. Macular versus Retinal Nerve Fiber Layer Parameters for Diagnosing Manifest Glaucoma: A Systematic Review of Diagnostic Accuracy Studies. Ophthalmology. 2016 May;123(5):939-49. doi: 10.1016/j.ophtha.2015.12.041. Epub 2016 Feb 15.
- Results publication: PubMed 24974815Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology. 2014 Nov;121(11):2081-90. doi: 10.1016/j.ophtha.2014.05.013. Epub 2014 Jun 26.
- Results publication: PubMed 16488940Quigley HA, Broman AT. The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol. 2006 Mar;90(3):262-7. doi: 10.1136/bjo.2005.081224.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Francesco Oddone, MD, PhD principal investigator
IRCCS Fondazione G. B. Bietti, Rome, Italy
Questions about this trial
What is this clinical trial studying?
A study testing GlaukomAI (Sens-vue GlaukomAI) in people with Glaucoma, run by Fondazione G.B. Bietti, IRCCS.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 8 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 4 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Francesco Oddone, MD, PhD (+39 06 84009442, francesco.oddone@fondazionebietti.it), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Accuracy of GlaukomAI - Sensitivity and Specificity (time frame: At enrollment visit (single visit, or two consecutive visits within 1 week)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07668193, which also lists 6 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Sep 2026.