Clinical trial NCT05166122: Implementation of an Integrated System of Artificial Intelligence and Referral Tracking for Real-time Diabetic Retinopathy Screening
Official title: Implementation of an Integrated System of Artificial Intelligence and Referral Tracking for Real-time Diabetic Retinopathy Screening
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Diabetic Retinopathy
Artificial Intelligence
Screening
Lead sponsor: Department of Medical Services Ministry of Public Health of Thailand
Sponsor class: OTHER_GOV
Collaborators: name: Health Systems Research Institute,Thailand
name: Google LLC.
First posted: 2021-12-21
Enrollment: 1600
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: SCREENING

Brief 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 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.

Eligibility criteria: Inclusion Criteria:

* 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.

Exclusion Criteria:

* 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)

Primary outcomes:
measure: Referral adherence
timeFrame: 6 months
Secondary outcomes:
measure: User trust and acceptability
timeFrame: 6 months
measure: Screening throughput
timeFrame: Compare time unit of 1 day for each arm
measure: Assess AI performance
timeFrame: 6 months
Other outcomes:
Locations:
city: Bangkok
country: Thailand
facility: Rajavithi hospital
Study officials:
References: