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
About this trial
This is an interventional other trial for Diagnosis
Eligibility Criteria
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
NCT ID
NCT05231174
First Posted
January 29, 2022
Last Updated
October 23, 2023
Sponsor
Sun Yat-sen University
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
Learn more about this trial
Using Large Language Model to Assist in DR Detection
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