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Using Large Language Model to Assist in DR Detection

Primary Purpose

Diagnosis, Diabetic Retinopathy

Status
Recruiting
Phase
Not Applicable
Locations
China
Study Type
Interventional
Intervention
A diagnostic chatbot based on large langauge model
Sponsored by
Sun Yat-sen University
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional other trial for Diagnosis

Eligibility Criteria

18 Years - undefined (Adult, Older Adult)All SexesDoes not accept healthy volunteers

Inclusion Criteria:

  • People over 18 years old.
  • Having a smartphone/tablet and able to view and operate the eTriaging APP.
  • Willing to participate in the study and provide informed content.

Exclusion Criteria:

  • NA

Sites / Locations

  • Zhognshan Ophthalmic Center, Sun Yat-sen UniversityRecruiting

Arms of the Study

Arm 1

Arm Type

Experimental

Arm Label

Interactive Q&A chatbot based on large langauge model

Arm Description

Outcomes

Primary Outcome Measures

AUROC of the diagnostic chatbot
AUROC of the diagnostic chatbot is based on ground truth from senior ophthalmologists.

Secondary Outcome Measures

Full Information

First Posted
January 29, 2022
Last Updated
October 23, 2023
Sponsor
Sun Yat-sen University
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1. Study Identification

Unique Protocol Identification Number
NCT05231174
Brief Title
Using Large Language Model to Assist in DR Detection
Official Title
Efficacy of Using Large Language Model to Assist in Diabetic Retinopathy Detection
Study Type
Interventional

2. Study Status

Record Verification Date
October 2023
Overall Recruitment Status
Recruiting
Study Start Date
February 8, 2022 (Actual)
Primary Completion Date
December 1, 2023 (Anticipated)
Study Completion Date
December 31, 2023 (Anticipated)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Principal Investigator
Name of the Sponsor
Sun Yat-sen University

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
No

5. Study Description

Brief Summary
With the increase in population and the rising prevalence of various diseases, the workload of disease diagnosis has sharply increased. The accessibility of healthcare services and long waiting times have become common issues in the public health medical system, with many primary patients having to wait for extended periods to receive medical services. There is an urgent need for rapid, accurate, and low-cost diagnostic services.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Diagnosis, Diabetic Retinopathy

7. Study Design

Primary Purpose
Other
Study Phase
Not Applicable
Interventional Study Model
Single Group Assignment
Masking
None (Open Label)
Allocation
N/A
Enrollment
500 (Anticipated)

8. Arms, Groups, and Interventions

Arm Title
Interactive Q&A chatbot based on large langauge model
Arm Type
Experimental
Intervention Type
Other
Intervention Name(s)
A diagnostic chatbot based on large langauge model
Intervention Description
We designed a chatbot combining large language model and a diagnostic model for diabetic retinopathy.
Primary Outcome Measure Information:
Title
AUROC of the diagnostic chatbot
Description
AUROC of the diagnostic chatbot is based on ground truth from senior ophthalmologists.
Time Frame
Immediately after using the chatbot

10. Eligibility

Sex
All
Minimum Age & Unit of Time
18 Years
Accepts Healthy Volunteers
No
Eligibility Criteria
Inclusion Criteria Individuals aged 18 years and above residing in the designated research area. Individuals who have a confirmed diagnosis of Type 2 diabetes. Individuals who haven't had a DR screening. Possession of a smartphone/tablet and the ability to smoothly type in chat content. Willingness to participate in the study and provide informed consent. Exclusion criteria Patients with prior diagnosis of DR. Individuals who had recent eye surgery. Individuals with other major eye diseases that might confound the DR screening results.
Facility Information:
Facility Name
Zhognshan Ophthalmic Center, Sun Yat-sen University
City
Guangzhou
State/Province
Guangdong
ZIP/Postal Code
510000
Country
China
Individual Site Status
Recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Yingfeng Zheng, M.D, Ph.D
Phone
+8613922286455
Email
zhyfeng@mail.sysu.edu.cn

12. IPD Sharing Statement

Plan to Share IPD
No

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Using Large Language Model to Assist in DR Detection

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