Ignite Creation Date:
2024-05-06 @ 5:01 PM
Last Modification Date:
2024-10-26 @ 2:20 PM
Study NCT ID:
NCT05166122
Status:
UNKNOWN
Last Update Posted:
2022-09-02
First Post:
2021-12-08
Brief Title:
Implementation of an Integrated System of Artificial Intelligence and Referral Tracking for Real-time Diabetic Retinopathy Screening
Sponsor:
Rajavithi Hospital
Organization:
Rajavithi Hospital
Study Overview
Official Title:
Implementation of an Integrated System of Artificial Intelligence and Referral Tracking for Real-time Diabetic Retinopathy Screening
Status:
UNKNOWN
Status Verified Date:
2022-08
Last Known Status:
RECRUITING
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 blindness1-3 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 screening4-6 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
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 blindness1-3 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 screening4-6 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None