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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
The New Model of Care, Hail Health Cluster
About
Eligibility
Locations
Arms
Outcomes
Full info

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

18 Years - 90 Years (Adult, Older Adult)All SexesAccepts Healthy Volunteers

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

    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
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    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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