Barcelona Esquerra Glaucoma Artificial Intelligence-based Screening Program (BEGAS)
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Software analysis, Ophthalmologist examination.
- Who it may be relevant to
- Registry conditions: Glaucoma. Basic parameters: 40 years — 80 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Spain
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Barcelona Esquerra Glaucoma Artificial Intelligence-based Screening Program (BEGAS): Artificial Intelligence Applied to Optic Nerve Retinographies for a Glaucoma Screening Program in a Primary-care Setting
Overview
Two primary care-based screening systems will be tested to identify subjects with referrable glaucoma to hospital care. Subjects between 45 to 64 years old living in the metropolitan area of Barcelona will be invited to participate in a one-time visit, with an optic disc examination and intraocular pressure (IOP). The criteria for referring a patient will be the detection of glaucoma but with two different approaches depending on which Integrated Practice Unit (IPU) the patients will be allocated to: one arm using an Artificial Intelligence (AI) reading software of the optic disc picture; and the other one will base their referral after an ophthalmic examination performed by an ophthalmologist. In both circuits, an optic nerve head photography will be obtained, and a masked reading center will be established to determine the ground truth for diagnosis. This screening trial will explore the level of agreement between both systems and the cost-effectiveness of each of them. Secondary analyses will include potential diagnostic composite scores (including other ancillary tests, such as optical coherence tomography images, that could maximize the screening process); the identification of population and disease characteristics (type of glaucoma, intraocular pressure) that could increase the effectivity and adherence to the screening process.
Detailed description
The purpose of this study is twofold: to validate in our population an Artificial Intelligence (AI) reading software of the optic disc picture, after comparing the estimated result (glaucoma/suspect/normal) to the ground truth; and to conduct a clinical trial where the level of agreement between both systems and the cost-effectiveness of each of them will be tested
In the first phase, a set of patients from our reference population will be selected. A standard-of-care ophthalmic examination with the usual ancillary tests to confirm or rule out the presence of glaucoma (including an optic disc retinography), will be performed. The patient (and the test) will be examined by a glaucoma specialist who will determine the status of the patient.
Then, the retinography will be analyzed by the AI software, providing the estimated result (glaucoma/suspect/normal). The level of agreement between the ground truth and the casted result will confirm the diagnostic accuracy.
In the second phase, a second set of patients will be recruited. In this case, the patients will be randomly allocated to either of the two arms of the study: In arm A the ancillary tests (including the retinography) will be performed, and the software will analyze the retinography, therefore providing the glaucoma status result. In arm B, the patients (and the test) will be examined by a glaucoma specialist who will then determine the status of the patient.
All the patients, irrespective of the diagnosis and the arm of the study will be then explored by another glaucoma specialist (reading center), who will be blinded to where the diagnosis comes from (AI software or glaucoma specialist), to the determine the level of agreement between the two screening systems
Interventions
- Diagnostic test Software analysis
The tested AI software analyzes the optic disc retinography to determine if the patient is healthy, a glaucoma suspect, or a glaucoma case - Diagnostic test Ophthalmologist examination
The ophthalmologist (a glaucoma specialist) will analyze the tests and will examine the patient to determine if the patient is healthy, a glaucoma suspect or a glaucoma case
Primary outcome measures
- Diagnostic agreement between the AI software and the ophthalmic examination [Time frame: 18 months]
- Health-Related Quality of Life (HRQoL) [Time frame: 18 months]
- Demographics [Time frame: 18 months]
Secondary outcome measures (5)
- Intraocular pressure [Time frame: 18 months]
- Optical coherence tomography (OCT) [Time frame: 18 months]
- Visual field [Time frame: 18 months]
- Cost-effective analysis of both screening methods [Time frame: 18 months]
- Risk score with parameters associated with positive screening of glaucoma [Time frame: 6 months]
Eligibility criteria
Inclusion criteria
- Patients aged 40 to 80 years old from our reference population
- Family history of glaucoma
- Willingness to participate
- Signed written informed consent
Exclusion criteria
- Not signing the informed consent
- Patients that had a previous diagnosis of glaucoma or any ophthalmic disease that required a regular ophthalmic examination and/or treatment
- Congenital or childhood glaucoma
- History of strabismus or amblyopia
- Known ophthalmic diseases which imply media opacity (cataract, cornea opacities) that might preclude from taking fundus retinographies
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Double blind
- Primary purpose
- Screening
Study locations
Spain · 1 center
- Hospital Clínic - ICOF — Barcelona
Publications
- Hemelings R, Elen B, Barbosa-Breda J, Lemmens S, Meire M, Pourjavan S, Vandewalle E, Van de Veire S, Blaschko MB, De Boever P, Stalmans I. Accurate prediction of glaucoma from colour fundus images with a convolutional neural network that relies on active and transfer learning. Acta Ophthalmol. 2020 Feb;98(1):e94-e100. doi: 10.1111/aos.14193. Epub 2019 Jul 25. PMID 31344328
- US Preventive Services Task Force; Davidson KW, Barry MJ, Mangione CM, Cabana M, Caughey AB, Davis EM, Donahue KE, Doubeni CA, Krist AH, Kubik M, Li L, Ogedegbe G, Owens DK, Pbert L, Silverstein M, Stevermer J, Tseng CW, Wong JB. Screening for Colorectal Cancer: US Preventive Services Task Force Recommendation Statement. JAMA. 2021 May 18;325(19):1965-1977. doi: 10.1001/jama.2021.6238. PMID 34003218
- Tan NYQ, Friedman DS, Stalmans I, Ahmed IIK, Sng CCA. Glaucoma screening: where are we and where do we need to go? Curr Opin Ophthalmol. 2020 Mar;31(2):91-100. doi: 10.1097/ICU.0000000000000649. PMID 31904596
Identifiers
NCT: NCT06353542 · HCB/2023/1206 · PI23/01856