Application of Artificial Intelligence in Early Detection of Eye Complications in Diabetics (AI)
Primary Purpose
Artificial Intelegence, Diabetic Retinopathy Associated With Type 2 Diabetes Mellitus, Macular Edema Due to Type 2 Diabetes Mellitus
Status
Not yet recruiting
Phase
Not Applicable
Locations
Study Type
Interventional
Intervention
AI based eye screening
Sponsored by
About this trial
This is an interventional screening trial for Artificial Intelegence focused on measuring AI Eye Screening in diabetics, Diabetic retinopathy, macular oedema
Eligibility Criteria
Inclusion Criteria: Diabetic patients aged 18-90 Exclusion Criteria: Severely ill patient or patient with cancer
Sites / Locations
Arms of the Study
Arm 1
Arm 2
Arm Type
Experimental
No Intervention
Arm Label
AI-based screening for early detection of diabetic retinopathy and macular Oedema
Routine screening for diabetic retinopathy and macular oedema
Arm Description
The application of AI devices i.e Fundus Camera to detect diabetic retinopathy and macular Oedema in diabetics at the primary care centre
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.
Outcomes
Primary Outcome Measures
The detection rate of diabetic retinopathy in the intervention group vs. control group
The proportion of the detected cases of diabetic retinopathy in the intervention group vs. control group
The detection rate of macular oedema in the intervention group vs. control group.
The proportion of the individuals who screened positive for macular oedema in the intervention group vs. control group.
Secondary Outcome Measures
The screening rate for retinopathy
The proportion of individuals who receive eye care screening for diabetic retinopathy in the intervention group vs. control group.
The screening rate for macular odema
The proportion of individuals who receive eye care screening for macular oedema in the intervention group vs. control group
Full Information
NCT ID
NCT05655117
First Posted
December 7, 2022
Last Updated
December 27, 2022
Sponsor
The New Model of Care, Hail Health Cluster
Collaborators
Health Holding Company, Hail Health Cluster
1. Study Identification
Unique Protocol Identification Number
NCT05655117
Brief Title
Application of Artificial Intelligence in Early Detection of Eye Complications in Diabetics
Acronym
AI
Official Title
Application of Artificial Intelligence in Early Detection of Eye Complications in Diabetics: A Randomized Clustered Trial in Hail, Saudi Arabia
Study Type
Interventional
2. Study Status
Record Verification Date
December 2022
Overall Recruitment Status
Not yet recruiting
Study Start Date
January 2023 (Anticipated)
Primary Completion Date
June 2023 (Anticipated)
Study Completion Date
July 2023 (Anticipated)
3. Sponsor/Collaborators
Responsible Party, by Official Title
Sponsor
Name of the Sponsor
The New Model of Care, Hail Health Cluster
Collaborators
Health Holding Company, Hail Health Cluster
4. Oversight
Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
No
5. Study Description
Brief Summary
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.
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.
6. Conditions and Keywords
Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Artificial Intelegence, Diabetic Retinopathy Associated With Type 2 Diabetes Mellitus, Macular Edema Due to Type 2 Diabetes Mellitus
Keywords
AI Eye Screening in diabetics, Diabetic retinopathy, macular oedema
7. Study Design
Primary Purpose
Screening
Study Phase
Not Applicable
Interventional Study Model
Parallel Assignment
Masking
None (Open Label)
Allocation
Randomized
Enrollment
440 (Anticipated)
8. Arms, Groups, and Interventions
Arm Title
AI-based screening for early detection of diabetic retinopathy and macular Oedema
Arm Type
Experimental
Arm Description
The application of AI devices i.e Fundus Camera to detect diabetic retinopathy and macular Oedema in diabetics at the primary care centre
Arm Title
Routine screening for diabetic retinopathy and macular oedema
Arm Type
No Intervention
Arm Description
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.
Intervention Type
Other
Intervention Name(s)
AI based eye screening
Intervention Description
The application of AI devices i.e Fundus Camera to detect diabetic retinopathy and macular Oedema in diabetics at the primary care centre
Primary Outcome Measure Information:
Title
The detection rate of diabetic retinopathy in the intervention group vs. control group
Description
The proportion of the detected cases of diabetic retinopathy in the intervention group vs. control group
Time Frame
6 month from the start of the study
Title
The detection rate of macular oedema in the intervention group vs. control group.
Description
The proportion of the individuals who screened positive for macular oedema in the intervention group vs. control group.
Time Frame
6 month from the start of the study
Secondary Outcome Measure Information:
Title
The screening rate for retinopathy
Description
The proportion of individuals who receive eye care screening for diabetic retinopathy in the intervention group vs. control group.
Time Frame
6 months after the start of the study
Title
The screening rate for macular odema
Description
The proportion of individuals who receive eye care screening for macular oedema in the intervention group vs. control group
Time Frame
6 months after the start of the study
10. Eligibility
Sex
All
Minimum Age & Unit of Time
18 Years
Maximum Age & Unit of Time
90 Years
Accepts Healthy Volunteers
Accepts Healthy Volunteers
Eligibility Criteria
Inclusion Criteria:
Diabetic patients aged 18-90
Exclusion Criteria:
Severely ill patient or patient with cancer
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Fakhralddin Elfakki, Researcher at MOC
Phone
+966530855161
Email
abbasfakhraddin@gmail.com
First Name & Middle Initial & Last Name or Official Title & Degree
Marwa Mahmoud Mahdy, CSoC Lead
Phone
+966508258235
Email
maroo_79@hotmail.com
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Khalil Alshammari, VIP Chief MO
Organizational Affiliation
Hail Health Cluster
Official's Role
Study Chair
First Name & Middle Initial & Last Name & Degree
Fakhralddin Elfakki, Researcher at MOC
Organizational Affiliation
New Model of Care, Hail Health Cluser
Official's Role
Principal Investigator
First Name & Middle Initial & Last Name & Degree
Meshari Aljamani, MOC Lead
Organizational Affiliation
New Model of Care, Hail Health Cluster
Official's Role
Study Director
12. IPD Sharing Statement
Plan to Share IPD
No
Learn more about this trial
Application of Artificial Intelligence in Early Detection of Eye Complications in Diabetics
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