NCT05166122

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

Trial Health

43
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
1,600

participants targeted

Target at P75+ for not_applicable

Timeline
Completed

Started Jan 2022

Shorter than P25 for not_applicable

Geographic Reach
1 country

1 active site

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

December 8, 2021

Completed
13 days until next milestone

First Posted

Study publicly available on registry

December 21, 2021

Completed
11 days until next milestone

Study Start

First participant enrolled

January 1, 2022

Completed
8 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

August 19, 2022

Completed
1 month until next milestone

Study Completion

Last participant's last visit for all outcomes

September 30, 2022

Completed
Last Updated

September 2, 2022

Status Verified

August 1, 2022

Enrollment Period

8 months

First QC Date

December 8, 2021

Last Update Submit

August 31, 2022

Conditions

Diabetic RetinopathyArtificial IntelligenceScreening

Outcome Measures

Primary Outcomes (1)

  • Referral adherence

    Total number of patients who completed referral visit in each arm (ie, presented to tertiary eye care center)

    6 months

Secondary Outcomes (3)

  • User trust and acceptability

    6 months

  • Screening throughput

    Compare time unit of 1 day for each arm

  • Assess AI performance

    6 months

Study Arms (2)

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.

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.

Interventions

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

AI workflow

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)

You may qualify if:

  • 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.

You may not qualify if:

  • 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)

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Rajavithi hospital

Bangkok, 10400, Thailand

RECRUITING

Related Publications (1)

  • 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.

    PMID: 39792334DERIVED

MeSH Terms

Conditions

Diabetic Retinopathy

Interventions

Artificial Intelligence

Condition Hierarchy (Ancestors)

Retinal DiseasesEye DiseasesDiabetic AngiopathiesVascular DiseasesCardiovascular DiseasesDiabetes ComplicationsDiabetes MellitusEndocrine System Diseases

Intervention Hierarchy (Ancestors)

AlgorithmsMathematical Concepts

Central Study Contacts

Paisan Ruamviboonsuk, MD

CONTACT

081-489-4455paisan.trs@gmail.com

Anyarak Amornpetchsathaporn, MD

CONTACT

083-167-7170yinyin.anyarak@gmail.com

Study Design

Study Type
interventional
Phase
not applicable
Allocation
RANDOMIZED
Masking
NONE
Purpose
SCREENING
Intervention Model
PARALLEL
Sponsor Type
OTHER GOV
Responsible Party
SPONSOR

Study Record Dates

First Submitted

December 8, 2021

First Posted

December 21, 2021

Study Start

January 1, 2022

Primary Completion

August 19, 2022

Study Completion

September 30, 2022

Last Updated

September 2, 2022

Record last verified: 2022-08

Data Sharing

IPD Sharing
Will not share

Locations

TH(1)