Diabetic RetinopathyNCT05166122
Implementation of an Integrated System of Artificial Intelligence and Referral Tracking for Real-time Diabetic Retinopathy Screening
A study testing Artificial Intelligence in people with Diabetic Retinopathy and Artificial Intelligence, run by Department of Medical Services Ministry of Public Health of Thailand at 1 site in Thailand.
Trial summary
This research study aims to bring an artificial intelligence system to screen for diabetic retinopathy (DR) along with referral tracking systems to the screening unit in Uthai Hospital in Phra Nakhon Sri Ayutthaya to assess the effectiveness of screening and follow-up of patients referred to Phra Nakhon Sri Ayutthaya… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Diabetic Retinopathy, Artificial Intelligence, Screening
- What is being tested?
- Artificial Intelligence
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,600 participants
- Where
- 1 siteThailand
- Study dates
- 1 Jan 2022to 30 Sep 2022
- 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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Patients aged 18 years and over.
- Patients who have been screened for diabetic retinopathy at Uthai Hospital Phra Nakhon Sri Ayutthaya Province that can refer patients to Phra Nakhon Sri Ayutthaya Hospital to see an ophthalmologist
- People with diabetes who are listed on the civil registry
- Able to take pictures of the retina at least 1 eye.
Who cannot join
- Being a patient in a community hospital with an in-house ophthalmologist
- Patients who were previously diagnosed for the following conditions / diseases: retinal edema, diabetic retinopathy (NPDR, PDR). The retina is affected by radiation (Radiation retinopathy) or retinal vein blockage (RVO).
- Past history of laser retinal treatment or retinal surgery
- Having other eye diseases (non-diabetic retinopathy) that requires referral to an ophthalmologist.
- Inability to take pictures of the retina (for any reason)
Trial locations and contacts
1 site in 1 country.
Thailand1 site, 1 recruiting
- Recruiting
Rajavithi hospital
Bangkok, 10400
Rajavithi hospital, 0661155598, paisan.trs@gmail.com
About this study
This research study aims to bring an artificial intelligence system to screen for diabetic retinopathy (DR) along with referral tracking systems to the screening unit in Uthai Hospital in Phra Nakhon Sri Ayutthaya to assess the effectiveness of screening and follow-up of patients referred to Phra Nakhon Sri Ayutthaya Hospital. It will be compared with the existing screening system and follow up with regular referral by personnel
Read the detailed description
Diabetic retinopathy is the most common ocular complication in people with diabetes. It is a leading cause of vision loss and blindness in people aged 20-64 years around the world because in the early stages of the disease there is no warning, causing the patients to be unaware. If the blood sugar content is allowed to increase, severe diabetic retinopathy can occur leading to blindness.
The incidence of diabetic retinopathy in diabetic patients tends to increase with the duration of diabetes. And according to the age of the patient, it was found that within 20 years, patients with diabetes type 1 with diabetic retinopathy is about 99% and diabetes type 2 with diabetic retinopathy is about 60%.
Screening for diabetic retinopathy is accepted and performed in health systems around the world. Evidence shows that screening can reduce blindness(1-3). Thailand uses the percentage of diabetic patients who have been eye tested. It is one of the indicators of service quality of the Eye Health District of the Ministry of Public Health. Screening for diabetic retinopathy using the retinal imaging method is cost-effective. It provides diabetic patients in distant places access to screening, such as bringing a mobile retina camera to take pictures in the community in conjunction with the use of teleophthalmology technology in screening(4-6). But according to a report by the Ministry of Public Health in the HDC system in 2015-2017, it was found that only 40% of the patients who were screened for diabetic retinopathy had not reached the 60% target.
In 2016, Rajavithi Hospital, in collaboration with researchers in Google Health, assessed the use of artificial intelligence to read retina images of diabetic patients in all 13 health districts of Thailand. It found that the artificial intelligence system was able to identify patients for referral to ophthalmologists (moderate non-proliferative diabetic retinopathy [NPDR]) with 95% sensitivity and 96% specificity, which is 73% higher than screening personnel specificity 98%.
From thereon, a prospective study with the introduction of artificial intelligence system was conducted to screen real patients in the project titled "Thailand-Google Prospective, Real-World Deployment of Artificial Intelligence for Diabetic Retinopathy Screening" (THAIGER) (NCT TCTR 20190902002) in 2018 to 2020 to assess the feasibility, including obstacles to implementing an intelligence-based screening process. The project integrated AI into the nation-wide screening system of the country. By conducting research in the primary care facilities, Rajavithi Hospital and 9 community hospitals in Pathum Thani Province and Chiang Mai, the diabetic patients in the THAIGER project received the results of reading images by artificial intelligence in real time. However, it was found that of the patients who were referred, very few actually went to see a doctor. There are also images that were unreadable (ungradable) by the artificial intelligence. And the artificial intelligence used in THAIGER has not yet been fully integrated into the screening system, including with a patient tracking system.
This research study aims to bring an artificial intelligence system to screen for diabetic retinopathy (DR) along with referral tracking systems to the screening unit in Uthai Hospital in Phra Nakhon Sri Ayutthaya to assess the effectiveness of screening and follow-up of patients referred to Phra Nakhon Sri Ayutthaya Hospital. It will be compared with the existing screening system and follow up with regular referral by personnel.
- Conditions
- Diabetic Retinopathy, Artificial Intelligence, Screening
- Being tested
- Artificial Intelligence
- Medical subject headings
- Diabetic Retinopathy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial IntelligenceDiagnostic test
Introduction of digitized system with an AI tool to detect and intrepret the severity of diabetic retinopathy and presence of diabetic macular edema in screening for diabetes patients
| Study group | Role | What they receive |
|---|---|---|
| AI workflow | Active comparator | In AI work flow, patients will be screened by taking normal retinal images and all images will be assessed for the severity of diabetic retinopathy by a computerized artificial intelligence system immediately after the photograph is taken via the Internet and retinal images will be sent to the retinal ophthalmologist for overreading. Receives: Diagnostic Test: Artificial Intelligence |
| Manual workflow | No intervention | Volunteers who have been screened by manual workflow will be screened by imaging the retina and image that are not normal will be sent to assess the severity of diabetic retinopathy by specialist staff. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Referral adherence
Total number of patients who completed referral visit in each arm (ie, presented to tertiary eye care center)
Measured: 6 months
Secondary outcomes (3)
User trust and acceptability
Assessment of staff satisfaction with workflows and patient experience in each arm
Measured: 6 months
Screening throughput
Assess the number of patients who successfully completed screening in a given day in the AI versus manual arm
Measured: Compare time unit of 1 day for each arm
Assess AI performance
Confirm sensitivity and specificity of AI reading as demonstrated in previous prospective study (THAIGER, TCTR20190902002)
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Dec 2021
- Study start1 Jan 2022
- Primary completion19 Aug 2022
- Study completion30 Sep 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Diabetic RetinopathyStudies 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
- 1,600 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Department of Medical Services Ministry of Public Health of Thailand
- Sponsor type
- Other government
- Collaborators
- Health Systems Research Institute,Thailand, Google LLC.
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05166122
- Sponsor's protocol ID
- 64077
- Status last verified
- Aug 2022
- First submitted
- 8 Dec 2021
- Last updated
- 2 Sep 2022
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Chotcomwongse P, Ruamviboonsuk P, Karavapitayakul C, Thongthong K, Amornpetchsathaporn A, Chainakul M, Triprachanath M, Lerdpanyawattananukul E, Arjkongharn N, Ruamviboonsuk V, Vongsa N, Pakaymaskul P, Waiwaree T, Ruampunpong H, Tiwari R, Tangcharoensathien V. Transforming Non-Digital, Clinical Workflows to Detect and Track Vision-Threatening Diabetic Retinopathy via a Digital Platform Integrating Artificial Intelligence: Implementation Research. Ophthalmol Ther. 2025 Feb;14(2):447-460. doi: 10.1007/s40123-024-01086-8. Epub 2025 Jan 10.
PubMed 39792334
Study at a glance
From the registry record, last updated by the sponsor on 2 Sep 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Implementation of an Integrated System of Artificial Intelligence and Referral Tracking for Real-time Diabetic Retinopathy Screening
- ClinicalTrials.gov ID
- NCT05166122
- Acronym / sponsor ID
- 64077
- Conditions
- Diabetic Retinopathy, Artificial Intelligence, Screening
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 1,600 participants (estimated)
- Lead sponsor
- Department of Medical Services Ministry of Public Health of ThailandOther government
- Collaborators
- Health Systems Research Institute,Thailand, Google LLC.
- Study start
- 1 Jan 2022 (actual)
- Primary completion
- 19 Aug 2022 (actual)
- Study completion
- 30 Sep 2022 (estimated)
- First posted
- 21 Dec 2021 (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.
This research study aims to bring an artificial intelligence system to screen for diabetic retinopathy (DR) along with referral tracking systems to the screening unit in Uthai Hospital in Phra Nakhon Sri Ayutthaya to assess the effectiveness of screening and follow-up of patients referred to Phra Nakhon Sri Ayutthaya Hospital. It will be compared with the existing screening system and follow up with regular referral by personnel
Detailed description
Diabetic retinopathy is the most common ocular complication in people with diabetes. It is a leading cause of vision loss and blindness in people aged 20-64 years around the world because in the early stages of the disease there is no warning, causing the patients to be unaware. If the blood sugar content is allowed to increase, severe diabetic retinopathy can occur leading to blindness.
The incidence of diabetic retinopathy in diabetic patients tends to increase with the duration of diabetes. And according to the age of the patient, it was found that within 20 years, patients with diabetes type 1 with diabetic retinopathy is about 99% and diabetes type 2 with diabetic retinopathy is about 60%.
Screening for diabetic retinopathy is accepted and performed in health systems around the world. Evidence shows that screening can reduce blindness(1-3). Thailand uses the percentage of diabetic patients who have been eye tested. It is one of the indicators of service quality of the Eye Health District of the Ministry of Public Health. Screening for diabetic retinopathy using the retinal imaging method is cost-effective. It provides diabetic patients in distant places access to screening, such as bringing a mobile retina camera to take pictures in the community in conjunction with the use of teleophthalmology technology in screening(4-6). But according to a report by the Ministry of Public Health in the HDC system in 2015-2017, it was found that only 40% of the patients who were screened for diabetic retinopathy had not reached the 60% target.
In 2016, Rajavithi Hospital, in collaboration with researchers in Google Health, assessed the use of artificial intelligence to read retina images of diabetic patients in all 13 health districts of Thailand. It found that the artificial intelligence system was able to identify patients for referral to ophthalmologists (moderate non-proliferative diabetic retinopathy [NPDR]) with 95% sensitivity and 96% specificity, which is 73% higher than screening personnel specificity 98%.
From thereon, a prospective study with the introduction of artificial intelligence system was conducted to screen real patients in the project titled "Thailand-Google Prospective, Real-World Deployment of Artificial Intelligence for Diabetic Retinopathy Screening" (THAIGER) (NCT TCTR 20190902002) in 2018 to 2020 to assess the feasibility, including obstacles to implementing an intelligence-based screening process. The project integrated AI into the nation-wide screening system of the country. By conducting research in the primary care facilities, Rajavithi Hospital and 9 community hospitals in Pathum Thani Province and Chiang Mai, the diabetic patients in the THAIGER project received the results of reading images by artificial intelligence in real time. However, it was found that of the patients who were referred, very few actually went to see a doctor. There are also images that were unreadable (ungradable) by the artificial intelligence. And the artificial intelligence used in THAIGER has not yet been fully integrated into the screening system, including with a patient tracking system.
This research study aims to bring an artificial intelligence system to screen for diabetic retinopathy (DR) along with referral tracking systems to the screening unit in Uthai Hospital in Phra Nakhon Sri Ayutthaya to assess the effectiveness of screening and follow-up of patients referred to Phra Nakhon Sri Ayutthaya Hospital. It will be compared with the existing screening system and follow up with regular referral by personnel.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI workflow | Active comparator | Diagnostic Test: Artificial IntelligenceIn AI work flow, patients will be screened by taking normal retinal images and all images will be assessed for the severity of diabetic retinopathy by a computerized artificial intelligence system immediately after the photograph is taken via the Internet and retinal images will be sent to the retinal ophthalmologist for overreading. |
| Manual workflow | No intervention | –Volunteers who have been screened by manual workflow will be screened by imaging the retina and image that are not normal will be sent to assess the severity of diabetic retinopathy by specialist staff. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Patients aged 18 years and over.
- Patients who have been screened for diabetic retinopathy at Uthai Hospital Phra Nakhon Sri Ayutthaya Province that can refer patients to Phra Nakhon Sri Ayutthaya Hospital to see an ophthalmologist
- People with diabetes who are listed on the civil registry
- Able to take pictures of the retina at least 1 eye.
Key exclusion criteria (5 listed)
- Being a patient in a community hospital with an in-house ophthalmologist
- Patients who were previously diagnosed for the following conditions / diseases: retinal edema, diabetic retinopathy (NPDR, PDR). The retina is affected by radiation (Radiation retinopathy) or retinal vein blockage (RVO).
- Past history of laser retinal treatment or retinal surgery
- Having other eye diseases (non-diabetic retinopathy) that requires referral to an ophthalmologist.
- Inability to take pictures of the retina (for any reason)
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 |
|---|---|---|
| Artificial IntelligenceIntroduction of digitized system with an AI tool to detect and intrepret the severity of diabetic retinopathy and presence of diabetic macular edema in screening for diabetes patients | Diagnostic test | AI workflow |
Primary outcome
Referral adherence
Time frame: 6 months
Total number of patients who completed referral visit in each arm (ie, presented to tertiary eye care center)
3 secondary outcomes
User trust and acceptability
Time frame: 6 months
Screening throughput
Time frame: Compare time unit of 1 day for each arm
Assess AI performance
Time frame: 6 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Sep 2022.
- Derived from this study: PubMed 39792334Chotcomwongse P, Ruamviboonsuk P, Karavapitayakul C, Thongthong K, Amornpetchsathaporn A, Chainakul M, Triprachanath M, Lerdpanyawattananukul E, Arjkongharn N, Ruamviboonsuk V, Vongsa N, Pakaymaskul P, Waiwaree T, Ruampunpong H, Tiwari R, Tangcharoensathien V. Transforming Non-Digital, Clinical Workflows to Detect and Track Vision-Threatening Diabetic Retinopathy via a Digital Platform Integrating Artificial Intelligence: Implementation Research. Ophthalmol Ther. 2025 Feb;14(2):447-460. doi: 10.1007/s40123-024-01086-8. Epub 2025 Jan 10.
- Individual participant dataSharing planned: No
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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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial Intelligence in people with Diabetic Retinopathy and Artificial Intelligence, run by Department of Medical Services Ministry of Public Health of Thailand at 1 site in Thailand.
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 4 inclusion and 5 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 2 Sep 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Thailand. Whether a site is taking part right now should be confirmed with that site. See every site.
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 Paisan Ruamviboonsuk, MD (081-489-4455, paisan.trs@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Referral adherence (time frame: 6 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05166122, which also lists 1 linked publication.
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 2 Sep 2022.