Diabetic RetinopathyNCT04132401
AI for the Detection of Retinal Disease and Glaucoma in Patients With Diabetes Mellitus in Primary Care
An observational study of Diabetic Retinopathy and Glaucoma, run by Fundacio d'Investigacio en Atencio Primaria Jordi Gol i Gurina at 1 site in Spain.
Trial summary
Background: Diabetic retinopathy (DR) is one of the most important causes of blindness worldwide, especially in developed countries. In diabetic patients, periodic examination of the back of the eye using a nonmydriatic camera has been widely demonstrated to be an effective system to control and prevent the onset of… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Diabetic Retinopathy, Glaucoma, Age-Related Macular Degeneration
- What is being tested?
- algorithm
- How many people
- 902 participants
- Where
- 1 siteSpain
- Study dates
- 1 May 2021to 26 Sep 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Adults with a clinical diagnosis of type 1 or type 2 diabetes mellitus attending primary care centres who underwent fundus photography as part of a diabetic retinopathy screening programme. The retinal images obtained were assessed for diabetic retinopathy and other central-involved retinal pathologies and glaucoma.
Who may be able to join
- Clinical diagnosis of type II diabetes mellitus
- Fundus photograph taken as part of the screening for diabetic retinopathy
Who cannot join
- patients with glaucoma under treatment
- patients with advanced dementia who do not collaborate in taking photographs
- patients with significant deafness who cannot follow the instructions for taking photographs
- patients with mobility problems (wheelchairs, important kyphosis) or tremor who cannot take photographs
- patients with pathologies that interfere with the quality of images such as cataracts, nystagmus, corneal leucoma or corneal transplants.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Artificial Intelligence for the Detection of Central Retinal Disease and Non-mydriatic Glaucoma in the Context of Patients With Diabetes Mellitus in Primary Care: A Prospective Study Comparing the Diagnostic Capacity of an AI Algorithm
Background: Diabetic retinopathy (DR) is one of the most important causes of blindness worldwide, especially in developed countries. In diabetic patients, periodic examination of the back of the eye using a nonmydriatic camera has been widely demonstrated to be an effective system to control and prevent the onset of DR. Convolutional neural networks have been used to detect DR, achieving very high sensitivities and specificities.
Hypothesis: It is possible to develop algorithms based on artificial intelligence that can demonstrate equal or superior performance and that constitute an alternative to the current screening of DR and other ophthalmic pathologies in diabetic patients.
Read more
Objectives:
- Development of an artificial intelligence system for the detection of signs of retinal pathology and other ophthalmic pathologies in diabetic patients.
- Scientific validation of the system to be used as a screening system in primary care.
Methods: This project consisted of carrying out two studies simultaneously:
- Development of an algorithm with artificial intelligence to detect signs of DR and other pathologies of the central retina in patients with diabetes.
- An observational, cross-sectional study comparing the diagnostic capacity of the algorithms with that of the family medicine specialists who read the fundus images. The reference was double-blind reading by ophthalmologists who specialize in retina.
The cession of the images began at the end of 2018. The images used for the validation were obtained during routine diabetic retinopathy screening between May and August 2021. The results have since been published.
The study allowed the development of an algorithm based on AI able to demonstrate an equal or superior performance, and to constitute a complement or an alternative to the current screening of DR in diabetic patients.
Read the detailed description
Study Design This project followed a methodology consisting of 2 concomitant studies: In the first study, an AI algorithm was developed to detect the signs of DR in patients with diabetes. The second part of the project consisted of an observational, cross-sectional study comparing the diagnostic capacity of the algorithm with that of the family medicine physicians and with retina specialists. The reference was a blinded double reading conducted by the retina specialists (with a blinded third reading in case of disagreement in the previous 2 readings). In this way, the results obtained, both by the AI algorithm and by family medicine specialists, were compared using the gold standard (accuracy, sensitivity, specificity, area under the curve, retina specialists (with a blinded third reading in case of disagreement in the previous 2 readings). In this way, the results obtained, both by the AI algorithm and by family medicine specialists, were compared using the gold standard (accuracy, sensitivity, specificity, area under the curve, etc). The inclusion of nurses who received training in fundus readings was considered to compare their diagnostic capacity.
Study Population, Site Participation, and Recruitment. Images for the development of the algorithm were ceded by the CHS and included images from the whole Catalan population. The study took place in the primary care centers managed by the Catalan Health Institute in Central Catalonia, which includes the counties of Bages, Osona, Berguedà, and Anoia. The reference population was the population assigned to these primary care centers. This population included about 512,000 people in 2017, with an estimated prevalence of diabetes of 7.1%. The study period included 2010-2017 for the development of the algorithm with AI. Once the algorithm had been developed, the study was conducted on fundus images obtained during routine diabetic retinopathy screening over a period of about 3-4 months, between May and August 2021.
Conduct of the Study. For the development of the AI algorithm, all fundus images labeled as DR of patients from primary care centers in Catalonia between 2010 and 2017 were included. For the study, all the images of patients who underwent an eye fundus examination were included until the adequate number of patients was reached. A high percentage of the fundus images had sufficient quality; that is, a 40-degree vision of the central retina where at least a three-fourth part of the optic nerve, a well-focused macula, and well-defined veins and arteries of the upper and lower arcs can be seen. Eye fundus images that did not have adequate technical quality (dark) or that could not be evaluated due to the opacity of the media (eg, for cataracts) were excluded.
Data Collection. For the development of the AI algorithm, anonymized images with the corresponding label that classifies each image (in one of the classes with which the algorithm was trained) were required. The personnel responsible for information technology (IT) of the CHS evaluated the best strategy for the anonymization and extraction of the images from the computer systems of the CHS, as well as the identification of each image with a unique identifier. A tabulated file type CSV or TXT was used to relate each image identifier with the corresponding classification. The person responsible for IT of the CHS, together with the technical manager of OPTretina, agreed on the best way to transfer these 2 sources of information, in a secure way, from the CHS servers to the OPTretina servers (SSH File Transfer Protocol, external hard disk), depending on the volume of data to be transferred and the internal policy of the CHS. OPTretina is experienced in developing AI models for automatic fundus image classification and is a Spanish Agency of Medicines and Health Products-certified medical device manufacturer. For the study, anonymized weekly fundus readings collected by family medicine physician readers of fundus images in Central Catalonia were collected. The images were transferred to the OPTretina servers to be first analyzed by the diagnostic algorithm and then by the retina specialists who made the definitive diagnosis. The person responsible for IT of the CHS, together with the technical manager of OPTretina, agreed on the best way to transfer these data in a secure manner.
- Conditions
- Diabetic Retinopathy, Glaucoma, Age-Related Macular Degeneration, Choroidal Nevus, Epiretinal Membrane
- Being tested
- algorithm
- Keywords
- fundus oculi, artificial intelligence, computer assisted diagnosis, neural network computer, Diabetic Retinopathy, Age-Related Macular Degeneration, Choroidal Nevus, Glaucoma
- Medical subject headings
- Diabetic Retinopathy, Glaucoma, Macular Degeneration, Epiretinal Membrane
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
algorithmDiagnostic test
The diagnostic capacity of the algorithm will be compared with that of the family medicine physicians and with retina specialists. The reference will be a blinded double reading conducted by the retina specialists
| Study group | Role | What they receive |
|---|---|---|
| family medicine physicians | Retina reading Receives: Diagnostic Test: algorithm | |
| retina specialists | Retina reading (gold standard) Receives: Diagnostic Test: algorithm |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity of the algorithm
True positive rate of the algorithm
Measured: 1 year
Specificity of the algorithm
True negative rate of the algorithm
Measured: 1 year
Accuracy of the algorithm
Ratio of number of correct predictions to the total number of input samples
Measured: 1 year
Area under the receiver operating characteristic curve of the algorithm
Diagnostic ability of the algorithm
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Oct 2019
- Study start1 May 2021
- Primary completion31 Mar 2022
- Study completion26 Sep 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (11)Papers the sponsor has linked to this study.
- Other trials for Diabetic RetinopathyStudies 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
- Observational
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Enrolment
- 902 participants (actual)
Sponsor and investigators
- Lead sponsor
- Fundacio d'Investigacio en Atencio Primaria Jordi Gol i Gurina
- Sponsor type
- University, hospital or other
- Collaborators
- OPTretina, Institut Català de la Salut, Department of Health, Generalitat de Catalunya
- Responsible party
- Sponsor
- Investigators
- Josep Vidal-Alaball, MD, PhD, MPH, Study chair, Institut Català de la Salut / IDIAP Jordi GolAlba Arocas Bonache, RN, Principal investigator, Institut Català de la Salut
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04132401
- Sponsor's protocol ID
- P18/109
- Status last verified
- Aug 2022
- First submitted
- 10 Oct 2019
- Last updated
- 9 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: The protocol has been published.
Publications
Bourne RR, Stevens GA, White RA, Smith JL, Flaxman SR, Price H, Jonas JB, Keeffe J, Leasher J, Naidoo K, Pesudovs K, Resnikoff S, Taylor HR; Vision Loss Expert Group. Causes of vision loss worldwide, 1990-2010: a systematic analysis. Lancet Glob Health. 2013 Dec;1(6):e339-49. doi: 10.1016/S2214-109X(13)70113-X. Epub 2013 Nov 11.
PubMed 25104599Sanchez Gonzalez S, Calvo Lozano J, Sanchez Gonzalez J, Pedregal Gonzalez M, Cornejo Castillo M, Molina Fernandez E, Barral FJ, Perez Espinosa JR. [Assessment of the use of retinography as a screening method for the early diagnosis of chronic glaucoma in Primary Care: Validation for screening in populations with open-angle glaucoma risk factors]. Aten Primaria. 2017 Aug-Sep;49(7):399-406. doi: 10.1016/j.aprim.2016.10.008. Epub 2017 Jan 23. Spanish.
PubMed 28126193Gomez-Ulla F, Fernandez MI, Gonzalez F, Rey P, Rodriguez M, Rodriguez-Cid MJ, Casanueva FF, Tome MA, Garcia-Tobio J, Gude F. Digital retinal images and teleophthalmology for detecting and grading diabetic retinopathy. Diabetes Care. 2002 Aug;25(8):1384-9. doi: 10.2337/diacare.25.8.1384.
PubMed 12145239Dankwa-Mullan I, Rivo M, Sepulveda M, Park Y, Snowdon J, Rhee K. Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here. Popul Health Manag. 2019 Jun;22(3):229-242. doi: 10.1089/pop.2018.0129. Epub 2018 Oct 2.
PubMed 30256722Quellec G, Charriere K, Boudi Y, Cochener B, Lamard M. Deep image mining for diabetic retinopathy screening. Med Image Anal. 2017 Jul;39:178-193. doi: 10.1016/j.media.2017.04.012. Epub 2017 Apr 28.
PubMed 28511066Usher D, Dumskyj M, Himaga M, Williamson TH, Nussey S, Boyce J. Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabet Med. 2004 Jan;21(1):84-90. doi: 10.1046/j.1464-5491.2003.01085.x.
PubMed 14706060Somfai GM, Tatrai E, Laurik L, Varga B, Olvedy V, Jiang H, Wang J, Smiddy WE, Somogyi A, DeBuc DC. Automated classifiers for early detection and diagnosis of retinopathy in diabetic eyes. BMC Bioinformatics. 2014 Apr 12;15:106. doi: 10.1186/1471-2105-15-106.
PubMed 24725911Abramoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M. Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning. Invest Ophthalmol Vis Sci. 2016 Oct 1;57(13):5200-5206. doi: 10.1167/iovs.16-19964.
PubMed 27701631Abramoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018 Aug 28;1:39. doi: 10.1038/s41746-018-0040-6. eCollection 2018.
PubMed 31304320Li Z, Keel S, Liu C, He Y, Meng W, Scheetz J, Lee PY, Shaw J, Ting D, Wong TY, Taylor H, Chang R, He M. An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs. Diabetes Care. 2018 Dec;41(12):2509-2516. doi: 10.2337/dc18-0147. Epub 2018 Oct 1.
PubMed 30275284Vidal-Alaball J, Arocas Bonache A, Sole-Casals J, Royo Fibla D, Marin-Gomez FX, Distefano LN, Boixadera A, Casado-Garcia A, Garcia-Dominguez M, Ines A, Heras J, Zapata MA. Clinical validation of artificial intelligence algorithms for the detection of different central-involved retinal pathologies and glaucoma from non-mydriatic images. Front Artif Intell. 2026 Mar 10;9:1754682. doi: 10.3389/frai.2026.1754682. eCollection 2026.
PubMed 41883753
Study at a glance
From the registry record, last updated by the sponsor on 9 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence for the Detection of Central Retinal Disease and Non-mydriatic Glaucoma in the Context of Patients With Diabetes Mellitus in Primary Care: A Prospective Study Comparing the Diagnostic Capacity of an AI Algorithm
- ClinicalTrials.gov ID
- NCT04132401
- Acronym / sponsor ID
- P18/109
- Conditions
- Diabetic Retinopathy, Glaucoma, Age-Related Macular Degeneration, Choroidal Nevus, Epiretinal Membrane
- Keywords
- fundus oculi, artificial intelligence, computer assisted diagnosis, neural network computer, Diabetic Retinopathy, Age-Related Macular Degeneration, Choroidal Nevus, Glaucoma
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 902 participants (actual)
- Lead sponsor
- Fundacio d'Investigacio en Atencio Primaria Jordi Gol i GurinaUniversity, hospital or other
- Collaborators
- OPTretina, Institut Català de la Salut, Department of Health, Generalitat de Catalunya
- Study start
- 1 May 2021 (actual)
- Primary completion
- 31 Mar 2022 (actual)
- Study completion
- 26 Sep 2023 (actual)
- First posted
- 18 Oct 2019 (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.
Background: Diabetic retinopathy (DR) is one of the most important causes of blindness worldwide, especially in developed countries. In diabetic patients, periodic examination of the back of the eye using a nonmydriatic camera has been widely demonstrated to be an effective system to control and prevent the onset of DR. Convolutional neural networks have been used to detect DR, achieving very high sensitivities and specificities.
Hypothesis: It is possible to develop algorithms based on artificial intelligence that can demonstrate equal or superior performance and that constitute an alternative to the current screening of DR and other ophthalmic pathologies in diabetic patients.
Objectives:
- Development of an artificial intelligence system for the detection of signs of retinal pathology and other ophthalmic pathologies in diabetic patients.
- Scientific validation of the system to be used as a screening system in primary care.
Methods: This project consisted of carrying out two studies simultaneously:
- Development of an algorithm with artificial intelligence to detect signs of DR and other pathologies of the central retina in patients with diabetes.
- An observational, cross-sectional study comparing the diagnostic capacity of the algorithms with that of the family medicine specialists who read the fundus images. The reference was double-blind reading by ophthalmologists who specialize in retina.
The cession of the images began at the end of 2018. The images used for the validation were obtained during routine diabetic retinopathy screening between May and August 2021. The results have since been published.
The study allowed the development of an algorithm based on AI able to demonstrate an equal or superior performance, and to constitute a complement or an alternative to the current screening of DR in diabetic patients.
Detailed description
Study Design This project followed a methodology consisting of 2 concomitant studies: In the first study, an AI algorithm was developed to detect the signs of DR in patients with diabetes. The second part of the project consisted of an observational, cross-sectional study comparing the diagnostic capacity of the algorithm with that of the family medicine physicians and with retina specialists. The reference was a blinded double reading conducted by the retina specialists (with a blinded third reading in case of disagreement in the previous 2 readings). In this way, the results obtained, both by the AI algorithm and by family medicine specialists, were compared using the gold standard (accuracy, sensitivity, specificity, area under the curve, retina specialists (with a blinded third reading in case of disagreement in the previous 2 readings). In this way, the results obtained, both by the AI algorithm and by family medicine specialists, were compared using the gold standard (accuracy, sensitivity, specificity, area under the curve, etc). The inclusion of nurses who received training in fundus readings was considered to compare their diagnostic capacity.
Study Population, Site Participation, and Recruitment. Images for the development of the algorithm were ceded by the CHS and included images from the whole Catalan population. The study took place in the primary care centers managed by the Catalan Health Institute in Central Catalonia, which includes the counties of Bages, Osona, Berguedà, and Anoia. The reference population was the population assigned to these primary care centers. This population included about 512,000 people in 2017, with an estimated prevalence of diabetes of 7.1%. The study period included 2010-2017 for the development of the algorithm with AI. Once the algorithm had been developed, the study was conducted on fundus images obtained during routine diabetic retinopathy screening over a period of about 3-4 months, between May and August 2021.
Conduct of the Study. For the development of the AI algorithm, all fundus images labeled as DR of patients from primary care centers in Catalonia between 2010 and 2017 were included. For the study, all the images of patients who underwent an eye fundus examination were included until the adequate number of patients was reached. A high percentage of the fundus images had sufficient quality; that is, a 40-degree vision of the central retina where at least a three-fourth part of the optic nerve, a well-focused macula, and well-defined veins and arteries of the upper and lower arcs can be seen. Eye fundus images that did not have adequate technical quality (dark) or that could not be evaluated due to the opacity of the media (eg, for cataracts) were excluded.
Data Collection. For the development of the AI algorithm, anonymized images with the corresponding label that classifies each image (in one of the classes with which the algorithm was trained) were required. The personnel responsible for information technology (IT) of the CHS evaluated the best strategy for the anonymization and extraction of the images from the computer systems of the CHS, as well as the identification of each image with a unique identifier. A tabulated file type CSV or TXT was used to relate each image identifier with the corresponding classification. The person responsible for IT of the CHS, together with the technical manager of OPTretina, agreed on the best way to transfer these 2 sources of information, in a secure way, from the CHS servers to the OPTretina servers (SSH File Transfer Protocol, external hard disk), depending on the volume of data to be transferred and the internal policy of the CHS. OPTretina is experienced in developing AI models for automatic fundus image classification and is a Spanish Agency of Medicines and Health Products-certified medical device manufacturer. For the study, anonymized weekly fundus readings collected by family medicine physician readers of fundus images in Central Catalonia were collected. The images were transferred to the OPTretina servers to be first analyzed by the diagnostic algorithm and then by the retina specialists who made the definitive diagnosis. The person responsible for IT of the CHS, together with the technical manager of OPTretina, agreed on the best way to transfer these data in a secure manner.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| family medicine physicians | – | Diagnostic Test: algorithmRetina reading |
| retina specialists | – | Diagnostic Test: algorithmRetina reading (gold standard) |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Adults with a clinical diagnosis of type 1 or type 2 diabetes mellitus attending primary care centres who underwent fundus photography as part of a diabetic retinopathy screening programme. The retinal images obtained were assessed for diabetic retinopathy and other central-involved retinal pathologies and glaucoma.
Key inclusion criteria (2 listed)
- Clinical diagnosis of type II diabetes mellitus
- Fundus photograph taken as part of the screening for diabetic retinopathy
Key exclusion criteria (5 listed)
- patients with glaucoma under treatment
- patients with advanced dementia who do not collaborate in taking photographs
- patients with significant deafness who cannot follow the instructions for taking photographs
- patients with mobility problems (wheelchairs, important kyphosis) or tremor who cannot take photographs
- patients with pathologies that interfere with the quality of images such as cataracts, nystagmus, corneal leucoma or corneal transplants.
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 |
|---|---|---|
| algorithmThe diagnostic capacity of the algorithm will be compared with that of the family medicine physicians and with retina specialists. The reference will be a blinded double reading conducted by the retina specialists | Diagnostic test | family medicine physicians, retina specialists |
Primary outcome
Sensitivity of the algorithm
Time frame: 1 year
True positive rate of the algorithm
Specificity of the algorithm
Time frame: 1 year
True negative rate of the algorithm
Accuracy of the algorithm
Time frame: 1 year
Ratio of number of correct predictions to the total number of input samples
Area under the receiver operating characteristic curve of the algorithm
Time frame: 1 year
Diagnostic ability of the algorithm
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Jul 2026.
- Results publication: PubMed 25104599Bourne RR, Stevens GA, White RA, Smith JL, Flaxman SR, Price H, Jonas JB, Keeffe J, Leasher J, Naidoo K, Pesudovs K, Resnikoff S, Taylor HR; Vision Loss Expert Group. Causes of vision loss worldwide, 1990-2010: a systematic analysis. Lancet Glob Health. 2013 Dec;1(6):e339-49. doi: 10.1016/S2214-109X(13)70113-X. Epub 2013 Nov 11.
- Results publication: PubMed 28126193Sanchez Gonzalez S, Calvo Lozano J, Sanchez Gonzalez J, Pedregal Gonzalez M, Cornejo Castillo M, Molina Fernandez E, Barral FJ, Perez Espinosa JR. [Assessment of the use of retinography as a screening method for the early diagnosis of chronic glaucoma in Primary Care: Validation for screening in populations with open-angle glaucoma risk factors]. Aten Primaria. 2017 Aug-Sep;49(7):399-406. doi: 10.1016/j.aprim.2016.10.008. Epub 2017 Jan 23. Spanish.
- Results publication: PubMed 12145239Gomez-Ulla F, Fernandez MI, Gonzalez F, Rey P, Rodriguez M, Rodriguez-Cid MJ, Casanueva FF, Tome MA, Garcia-Tobio J, Gude F. Digital retinal images and teleophthalmology for detecting and grading diabetic retinopathy. Diabetes Care. 2002 Aug;25(8):1384-9. doi: 10.2337/diacare.25.8.1384.
- Results publication: PubMed 30256722Dankwa-Mullan I, Rivo M, Sepulveda M, Park Y, Snowdon J, Rhee K. Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here. Popul Health Manag. 2019 Jun;22(3):229-242. doi: 10.1089/pop.2018.0129. Epub 2018 Oct 2.
- Results publication: PubMed 28511066Quellec G, Charriere K, Boudi Y, Cochener B, Lamard M. Deep image mining for diabetic retinopathy screening. Med Image Anal. 2017 Jul;39:178-193. doi: 10.1016/j.media.2017.04.012. Epub 2017 Apr 28.
- Results publication: PubMed 14706060Usher D, Dumskyj M, Himaga M, Williamson TH, Nussey S, Boyce J. Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabet Med. 2004 Jan;21(1):84-90. doi: 10.1046/j.1464-5491.2003.01085.x.
- Results publication: PubMed 24725911Somfai GM, Tatrai E, Laurik L, Varga B, Olvedy V, Jiang H, Wang J, Smiddy WE, Somogyi A, DeBuc DC. Automated classifiers for early detection and diagnosis of retinopathy in diabetic eyes. BMC Bioinformatics. 2014 Apr 12;15:106. doi: 10.1186/1471-2105-15-106.
- Results publication: PubMed 27701631Abramoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M. Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning. Invest Ophthalmol Vis Sci. 2016 Oct 1;57(13):5200-5206. doi: 10.1167/iovs.16-19964.
- Results publication: PubMed 31304320Abramoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018 Aug 28;1:39. doi: 10.1038/s41746-018-0040-6. eCollection 2018.
- Results publication: PubMed 30275284Li Z, Keel S, Liu C, He Y, Meng W, Scheetz J, Lee PY, Shaw J, Ting D, Wong TY, Taylor H, Chang R, He M. An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs. Diabetes Care. 2018 Dec;41(12):2509-2516. doi: 10.2337/dc18-0147. Epub 2018 Oct 1.
- Derived from this study: PubMed 41883753Vidal-Alaball J, Arocas Bonache A, Sole-Casals J, Royo Fibla D, Marin-Gomez FX, Distefano LN, Boixadera A, Casado-Garcia A, Garcia-Dominguez M, Ines A, Heras J, Zapata MA. Clinical validation of artificial intelligence algorithms for the detection of different central-involved retinal pathologies and glaucoma from non-mydriatic images. Front Artif Intell. 2026 Mar 10;9:1754682. doi: 10.3389/frai.2026.1754682. eCollection 2026.
- Individual participant dataSharing planned: Yes. The protocol has been published.
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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. This study is not recruiting at the moment.
Josep Vidal-Alaball, MD, PhD, MPH study chair
Institut Català de la Salut / IDIAP Jordi Gol
Alba Arocas Bonache, RN principal investigator
Institut Català de la Salut
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Diabetic Retinopathy and Glaucoma, run by Fundacio d'Investigacio en Atencio Primaria Jordi Gol i Gurina at 1 site in Spain.
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 2 inclusion and 5 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: finished, as of the sponsor's last update on 9 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Spain. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity of the algorithm (time frame: 1 year). The study lists 4 primary outcomes in all. 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 NCT04132401, which also lists 11 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 9 Jul 2026.