NCT04132401

Brief 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 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 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:
  • Development of an algorithm with artificial intelligence to detect signs of DR, other pathologies of the central retina and glaucoma in patients with diabetes.
  • 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.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
900

participants targeted

Target at P75+ for not_applicable

Timeline
Completed

Started May 2021

Typical duration for not_applicable

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

October 10, 2019

Completed
8 days until next milestone

First Posted

Study publicly available on registry

October 18, 2019

Completed
1.5 years until next milestone

Study Start

First participant enrolled

May 1, 2021

Completed
11 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

March 31, 2022

Completed
1.5 years until next milestone

Study Completion

Last participant's last visit for all outcomes

September 26, 2023

Completed
Last Updated

November 25, 2025

Status Verified

August 1, 2022

Enrollment Period

11 months

First QC Date

October 10, 2019

Last Update Submit

November 20, 2025

Conditions

Diabetic Retinopathy

Keywords

fundus oculiartificial intelligencecomputer assisted diagnosisneural network computerDiabetic Retinopathy

Outcome Measures

Primary Outcomes (4)

  • Sensitivity of the algorithm

    True positive rate of the algorithm

    1 year

  • Specificity of the algorithm

    True negative rate of the algorithm

    1 year

  • Accuracy of the algorithm

    Ratio of number of correct predictions to the total number of input samples

    1 year

  • Area under the receiver operating characteristic curve of the algorithm

    Diagnostic ability of the algorithm

    1 year

Study Arms (2)

family medicine physicians

EXPERIMENTAL

Retina reading

Diagnostic Test: algorithm

retina specialists

EXPERIMENTAL

Retina reading (gold standard)

Diagnostic Test: algorithm

Interventions

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

family medicine physiciansretina specialists

Eligibility Criteria

Sexall
Healthy VolunteersNo
Age GroupsChild (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

  • Clinical diagnosis of type I or type II diabetes mellitus
  • Fundus photograph taken as part of the screening for diabetic retinopathy

You may not qualify if:

  • 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.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

CAP Bages

Manresa, Barcelona, 08242, Spain

Location

Related Publications (10)

  • 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.

    PMID: 25104599RESULT

  • 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.

    PMID: 28126193RESULT

  • 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.

    PMID: 12145239RESULT

  • 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.

    PMID: 30256722RESULT

  • 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.

    PMID: 28511066RESULT

  • 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.

    PMID: 14706060RESULT

  • 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.

    PMID: 24725911RESULT

  • 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.

    PMID: 27701631RESULT

  • 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.

    PMID: 31304320RESULT

  • 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.

    PMID: 30275284RESULT

MeSH Terms

Conditions

Diabetic Retinopathy

Interventions

Algorithms

Condition Hierarchy (Ancestors)

Retinal DiseasesEye DiseasesDiabetic AngiopathiesVascular DiseasesCardiovascular DiseasesDiabetes ComplicationsDiabetes MellitusEndocrine System Diseases

Intervention Hierarchy (Ancestors)

Mathematical Concepts

Study Officials

  • Josep Vidal-Alaball, MD, PhD, MPH

    Institut Català de la Salut / IDIAP Jordi Gol

    STUDY CHAIR

  • Alba Arocas Bonache, RN

    Institut Català de la Salut

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
interventional
Phase
not applicable
Allocation
NON RANDOMIZED
Masking
NONE
Purpose
OTHER
Intervention Model
PARALLEL
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

October 10, 2019

First Posted

October 18, 2019

Study Start

May 1, 2021

Primary Completion

March 31, 2022

Study Completion

September 26, 2023

Last Updated

November 25, 2025

Record last verified: 2022-08

Data Sharing

IPD Sharing
Will share

The protocol has been published.

Shared Documents
STUDY PROTOCOL, CSR
Time Frame
End of the study
Access Criteria
Information will be published in international scientific journals

Locations

ES(1)