Artificial IntelegenceAINCT05655117
Application of Artificial Intelligence in Early Detection of Eye Complications in Diabetics
A study testing AI based eye screening in people with Artificial Intelegence and Diabetic Retinopathy Associated With Type 2 Diabetes Mellitus, run by The New Model of Care, Hail Health Cluster.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 90 yearsHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelegence, Diabetic Retinopathy Associated With Type 2 Diabetes Mellitus, Macular Edema Due to Type 2 Diabetes Mellitus
- What is being tested?
- AI based eye screening
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 440 participants
- Where
- No sites listed yet
- Study dates
- Jan 2023to Jul 2023
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 90 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Diabetic patients aged 18-90
Who cannot join
- Severely ill patient or patient with cancer
Trial locations and contacts
No sites are listed yet.
About this study
Official title: 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.
Read more
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.
Read the 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.
- Conditions
- Artificial Intelegence, Diabetic Retinopathy Associated With Type 2 Diabetes Mellitus, Macular Edema Due to Type 2 Diabetes Mellitus
- Being tested
- AI based eye screening
- Keywords
- AI Eye Screening in diabetics, Diabetic retinopathy, macular oedema
- Medical subject headings
- Diabetic Retinopathy, Macular Edema
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI based eye screeningOther
The application of AI devices i.e Fundus Camera to detect diabetic retinopathy and macular Oedema in diabetics at the primary care centre
| Study group | Role | What they receive |
|---|---|---|
| AI-based screening for early detection of diabetic retinopathy and macular Oedema | Experimental | The application of AI devices i.e Fundus Camera to detect diabetic retinopathy and macular Oedema in diabetics at the primary care centre Receives: Other: AI based eye screening |
| Routine screening for diabetic retinopathy and macular oedema | No intervention | 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 being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 6 month from the start of the study
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.
Measured: 6 month from the start of the study
Secondary outcomes (2)
The screening rate for retinopathy
The proportion of individuals who receive eye care screening for diabetic retinopathy in the intervention group vs. control group.
Measured: 6 months after the start of the study
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
Measured: 6 months after the start of the study
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Dec 2022
- Study startJan 2023 (estimated)
- Primary completionJun 2023 (estimated)
- Study completionJul 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artificial IntelegenceStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 440 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The New Model of Care, Hail Health Cluster
- Sponsor type
- Other government
- Collaborators
- Health Holding Company, Hail Health Cluster
- Responsible party
- Sponsor
- Investigators
- Khalil Alshammari, VIP Chief MO, Study chair, The New Model of Care, Hail Health ClusterFakhralddin Elfakki, Researcher at MOC, Principal investigator, New Model of Care, Hail Health CluserMeshari Aljamani, MOC Lead, Study director, New Model of Care, Hail Health Cluster
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05655117
- Sponsor's protocol ID
- Model of Care Hail Cluster
- Acronym
- AI
- Status last verified
- Dec 2022
- First submitted
- 7 Dec 2022
- Last updated
- 29 Dec 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 29 Dec 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Application of Artificial Intelligence in Early Detection of Eye Complications in Diabetics: A Randomized Clustered Trial in Hail, Saudi Arabia
- ClinicalTrials.gov ID
- NCT05655117
- Acronym / sponsor ID
- AI / Model of Care Hail Cluster
- Conditions
- 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
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 440 participants (estimated)
- Lead sponsor
- The New Model of Care, Hail Health ClusterOther government
- Collaborators
- Health Holding Company, Hail Health Cluster
- Study start
- Jan 2023 (estimated)
- Primary completion
- Jun 2023 (estimated)
- Study completion
- Jul 2023 (estimated)
- First posted
- 19 Dec 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI-based screening for early detection of diabetic retinopathy and macular Oedema | Experimental | Other: AI based eye screeningThe application of AI devices i.e Fundus Camera to detect diabetic retinopathy and macular Oedema in diabetics at the primary care centre |
| Routine screening for diabetic retinopathy and macular oedema | No intervention | –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. |
Who may qualify
- Ages
- 18 to 90 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Diabetic patients aged 18-90
Key exclusion criteria (1 listed)
- Severely ill patient or patient with cancer
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI based eye screeningThe 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 screening for early detection of diabetic retinopathy and macular Oedema |
Primary outcome
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
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.
2 secondary outcomes
The screening rate for retinopathy
Time frame: 6 months after the start of the study
The screening rate for macular odema
Time frame: 6 months after the start of the study
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Dec 2022.
- Individual participant dataSharing planned: No
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Khalil Alshammari, VIP Chief MO study chair
The New Model of Care, Hail Health Cluster
Fakhralddin Elfakki, Researcher at MOC principal investigator
New Model of Care, Hail Health Cluser
Meshari Aljamani, MOC Lead study director
New Model of Care, Hail Health Cluster
Questions about this trial
What is this clinical trial studying?
A study testing AI based eye screening in people with Artificial Intelegence and Diabetic Retinopathy Associated With Type 2 Diabetes Mellitus, run by The New Model of Care, Hail Health Cluster.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 29 Dec 2022. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Fakhralddin Elfakki, Researcher at MOC (+966530855161, abbasfakhraddin@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The detection rate of diabetic retinopathy in the intervention group vs. control group (time frame: 6 month from the start of the study). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Dec 2022.