Application of Artificial Intelligence in Early Detection of Eye Complications in Diabetics (AI)

December 27, 2022 updated by: The New Model of Care, Hail Health Cluster

Application of Artificial Intelligence in Early Detection of Eye Complications in Diabetics: A Randomized Clustered Trial in Hail, Saudi Arabia

The goal of this pragmatic trial is to test the benefit of using artificial intelligence-based eye screening i.e, a fundus camera device in the early detection of eye complications in diabetics. The main questions it aims to answer are:

To what extent does the application of artificial intelligence-based eye care at primary care clinics work well in achieving early detection of eye complications such as macular oedema? To what extent does the application of artificial intelligence-based eye care at primary care clinics work well in achieving early detection of eye complications such as retinopathy? Participants will be asked to participate in the screening for eye complications at primary care centres, and a fundus camera will be used for screening.

Researchers will compare the proportion of detected cases with early signs of eye complication among those using artificial intelligence-based eye screening i.e., fundus camera, to the proportion of detected cases among those using routine eye care clinics at the primary care centre.

Early detection of eye complications in diabetics prevents the risk of blindness.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

In the era of artificial inelegance(AI), a shift from tertiary to secondary and primary care when caring for a patient with diabetic retinopathy is highly recommended.

Due to low operation, AI could be used in the early detection and screening of diabetic retinopathy by application of the service across a mass population and resource-limited areas with a scarcity of eye care services.

AI-based eye care in terms of screening for diabetic retinopathy will make the screening process more effective and cheap and could be delegated to technicians, practitioners, and/or even home-based self-screening.

Recognizing the high prevalence of type 2 diabetes mellitus (T2DM) among adults, the use of a nonmydriatic fundus camera with AI is effective in eye exams as it improves adult adherence to eye screening.

The primary aim of the trial will be to assess the effectiveness of the application of AI devices in terms of fundus cameras in the early detection of diabetic retinopathy and macular oedema among diabetic patients attending primary care centres.

Research Questions:

To what extent does the application of artificial intelligence-based eye care at primary care centre is effective in achieving a high detection rate of macular oedema? To what extent does the application of artificial intelligence-based eye care at primary care clinic is effective in achieving a high detection rate of retinopathy?

General objective:

To estimate the effectiveness of applying AI-based eye care at primary care centres in achieving a high detection rate of macular oedema and retinopathy among diabetics.

Specific Objectives:

Aim 1: To compare the proportion of detected cases of macular oedema in the intervention versus the control group (routine eye care) attending the primary care centre.

Aim 2: To compare the proportion of detected cases of retinopathy in the intervention versus the control group (routine eye care) attending the primary care centre

Literature Review:

Although recent models had been suggested for implementing digital health solutions like stream fishing, inflow funnel, pyramid, and shuffling cards that represent options for clinical services with progressively increasing capacity and willingness to operationalize digital health.

However, various challenges are facing the deployment of AI, telehealth, and the internet of things (IoT) worldwide. Barriers to adopting these digital health solutions are many and could be inferred to infrastructure, the quality of the device, common willingness, and legal aspects.

Evidence revealed that using Macustat retina function scan AI in remote monitoring of a patient with age-related macular oedema or diabetic retinopathy has a great impact on patient health.

Research Design and Methods:

This is a six months clustered randomized trial that will recruit patients with type II diabetes who are attending primary eye care clinics at primary care centres in Hail city.

Participants (P):

The participants will be type II diabetic patients of both genders attending the selected primary care centres irrespective of their duration of disease and the types of medication currently received. The participants are expected to be adults aged 18 years and above. Children and young adults with juvenile diabetes mellitus will be excluded. In addition, severely ill patients, and patients with mental disorders will be excluded. The participants will be assessed at the start to collect the baseline data about diabetic retinopathy and macular oedema using AI devices to report detected cases. At the end of the trial, a similar report of detected cases will be obtained three and six months after the beginning of the trial.

Study Type

Interventional

Enrollment (Anticipated)

440

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 90 years (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Description

Inclusion Criteria:

  • Diabetic patients aged 18-90

Exclusion Criteria:

  • Severely ill patient or patient with cancer

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Screening
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: AI-based screening for early detection of diabetic retinopathy and macular Oedema
The application of AI devices i.e Fundus Camera to detect diabetic retinopathy and macular Oedema in diabetics at the primary care centre
Other: AI based eye screening
The application of AI devices i.e Fundus Camera to detect diabetic retinopathy and macular Oedema in diabetics at the primary care centre
No Intervention: Routine screening for diabetic retinopathy and macular oedema
The Routine screening for diabetic retinopathy and macular oedema in diabetics during a routine visit to an eye care clinic at the primary care centre.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
The detection rate of diabetic retinopathy in the intervention group vs. control group
Time Frame: 6 month from the start of the study
The proportion of the detected cases of diabetic retinopathy in the intervention group vs. control group
6 month from the start of the study
The detection rate of macular oedema in the intervention group vs. control group.
Time Frame: 6 month from the start of the study
The proportion of the individuals who screened positive for macular oedema in the intervention group vs. control group.
6 month from the start of the study

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
The screening rate for retinopathy
Time Frame: 6 months after the start of the study
The proportion of individuals who receive eye care screening for diabetic retinopathy in the intervention group vs. control group.
6 months after the start of the study
The screening rate for macular odema
Time Frame: 6 months after the start of the study
The proportion of individuals who receive eye care screening for macular oedema in the intervention group vs. control group
6 months after the start of the study

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

The New Model of Care, Hail Health Cluster

Collaborators

Health Holding Company, Hail Health Cluster

Investigators

  • Study Chair: Khalil Alshammari, VIP Chief MO, Hail Health Cluster
  • Principal Investigator: Fakhralddin Elfakki, Researcher at MOC, New Model of Care, Hail Health Cluser
  • Study Director: Meshari Aljamani, MOC Lead, New Model of Care, Hail Health Cluster

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Anticipated)

January 1, 2023

Primary Completion (Anticipated)

June 1, 2023

Study Completion (Anticipated)

July 1, 2023

Study Registration Dates

First Submitted

December 7, 2022

First Submitted That Met QC Criteria

December 14, 2022

First Posted (Actual)

December 19, 2022

Study Record Updates

Last Update Posted (Estimate)

December 29, 2022

Last Update Submitted That Met QC Criteria

December 27, 2022

Last Verified

December 1, 2022

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • Model of Care Hail Cluster

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

No

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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