AI for the Detection of Retinal Disease and Glaucoma in Patients With Diabetes Mellitus in Primary Care

Diabetic Retinopathy Clinical Trial

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04132401
• Other study ID #: P18/109
• Status: Completed
• Phase: N/A
• Start date: May 1, 2021
• Est. completion date: September 26, 2023

Study information

• Verified date: August 2022
• Source: Fundacio d'Investigacio en Atencio Primaria Jordi Gol i Gurina
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical 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 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:

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

Description:

Study Design This project will follow a methodology consisting of 2 concomitant studies: In the first study, we will develop an AI algorithm to detect the signs of DR in patients with diabetes.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 100
• Est. completion date: September 26, 2023
• Est. primary completion date: March 31, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

• Clinical diagnosis of type I or type II diabetes mellitus

• Fundus photograph taken as part of the screening for diabetic retinopathy

Exclusion Criteria:

• 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.

Related Conditions & MeSH terms

• Diabetes Mellitus
• Diabetic Retinopathy
• Glaucoma
• Retinal Diseases

Intervention

Diagnostic Test:
• 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

Locations

Country	Name	City	State
Spain	CAP Bages	Manresa	Barcelona

Sponsors (4)

Lead Sponsor	Collaborator
Fundacio d'Investigacio en Atencio Primaria Jordi Gol i Gurina	Department of Health, Generalitat de Catalunya, Institut Català de la Salut, OPTretina

Country where clinical trial is conducted

Spain, 

References & Publications (10)

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. — View Citation

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. do — View Citation

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. 2 — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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 — View Citation

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: 10.1186/1471-2105-15-1 — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Sensitivity of the algorithm	True positive rate of the algorithm	1 year
Primary	Specificity of the algorithm	True negative rate of the algorithm	1 year
Primary	Accuracy of the algorithm	Ratio of number of correct predictions to the total number of input samples	1 year
Primary	Area under the receiver operating characteristic curve of the algorithm	Diagnostic ability of the algorithm	1 year