AI for the Detection of Retinal Disease and Glaucoma in Patients With Diabetes Mellitus in Primary Care
NCT ID: NCT04132401
Last Updated: 2025-11-25
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
NA
900 participants
INTERVENTIONAL
2021-05-01
2023-09-26
Brief Summary
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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 RD 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 will consist of carrying out two studies simultaneously:
1. Development of an algorithm with artificial intelligence to detect signs of DR, other pathologies of the central retina and glaucoma in patients with diabetes.
2. Carrying out a prospective study that will make it possible to compare the diagnostic capacity of the algorithms with that of the family medicine specialists who read the background images. The reference will be double-blind reading by ophthalmologists who specialize in retina.
Cession of the images began at the end of 2018. The development of the AI algorithm is calculated to last about 3 to 4 months. Inclusion of patients in the cohort will start in early 2019 and is expected to last 3 to 4 months. Preliminary results are expected to be published by the end of 2019.
The study will allow the development of an algorithm based on AI that can demonstrate an equal or superior performance, and that constitutes a complement or an alternative, to the current screening of DR in diabetic patients
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Detailed Description
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The second part of the project will consist of the elaboration of a prospective study that will allow comparing the diagnostic capacity of the algorithm with that of the family medicine physicians and with retina specialists. The reference will be 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, will be compared using the gold standard (accuracy, sensitivity, specificity, area under the curve, etc). The inclusion of nurses who received training in fundus readings will be considered to compare their diagnostic capacity.
Study Population, Site Participation, and Recruitment Images for the development of the algorithm will be ceded by the CHS and will include images from the whole Catalan population. The prospective study will take 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 will be 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 will include 2010-2017 for the development of the algorithm with AI. The prospective study will begin once the algorithm is developed and will run until the number of readings needed is obtained (about 3-4 months).
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 will be included. For the prospective study, all the images of patients who underwent an eye fundus examination will be included from the study start period until the adequate number of patients is reached. A high percentage of fundus images must have 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 do not have adequate technical quality (dark) or that cannot be evaluated due to the opacity of the media (eg, for cataracts) will be excluded
Data Collection For the development of the AI algorithm, it is necessary to have the anonymized images with the corresponding label that classifies each image (in one of the classes with which the algorithm is to be trained). The personnel responsible for information technology (IT) of the CHS will evaluate 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. On the other hand, a tabulated file type CSV or TXT will be required to relate each image identifier with the corresponding classification. The person responsible for IT of the CHS, together with the technical manager of OPTretina, will agree 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 prospective study, anonymized weekly fundus data readings collected by family medicine physician readers of fundus images in Central Catalonia will be collected. The images will be transferred to the OPTretina servers to be first analyzed by the diagnostic algorithm and then by the retina specialists who will make the definitive diagnosis. The person responsible for IT of the CHS, together with the technical manager of OPTretina, will agree on the best way to transfer these data in a secure manner.
Conditions
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Study Design
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NON_RANDOMIZED
PARALLEL
OTHER
NONE
Study Groups
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family medicine physicians
Retina reading
algorithm
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
retina specialists
Retina reading (gold standard)
algorithm
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
Interventions
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algorithm
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
Eligibility Criteria
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Inclusion Criteria
* Fundus photograph taken as part of the screening for diabetic retinopathy
Exclusion Criteria
* 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.
ALL
No
Sponsors
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OPTretina
UNKNOWN
Institut Català de la Salut
OTHER
Department of Health, Generalitat de Catalunya
OTHER_GOV
Fundacio d'Investigacio en Atencio Primaria Jordi Gol i Gurina
OTHER
Responsible Party
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Principal Investigators
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Josep Vidal-Alaball, MD, PhD, MPH
Role: STUDY_CHAIR
Institut Català de la Salut / IDIAP Jordi Gol
Alba Arocas Bonache, RN
Role: PRINCIPAL_INVESTIGATOR
Institut Català de la Salut
Locations
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CAP Bages
Manresa, Barcelona, Spain
Countries
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References
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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.
Sanchez 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.
Gomez-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.
Dankwa-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.
Quellec 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.
Usher 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.
Somfai 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.
Abramoff 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.
Abramoff 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.
Li 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.
Other Identifiers
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P18/109
Identifier Type: -
Identifier Source: org_study_id
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