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Efficacy of Using Large Language Model to Assist in Diabetic Retinopathy Detection

NCT05231174 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 535

Last updated 2024-01-19

No results posted yet for this study

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.

Conditions

Interventions

OTHER

A self-evlaution tool based on Large Language Model

Following the baseline assessment, participants will be guided to use a self-evaluation tool independently to assess their risk of diabetic retinopathy (DR). This tool is a fusion of a conversational AI system based on LLM and an existing logistic diagnostic model. The AI system is designed to collect clinical variables, including age, duration of diabetes, Body Mass Index (BMI), and insulin usage. Additionally, clinical test data such as mean arterial pressure, HbA1c, serum creatinine, and microalbuminuria will be extracted from a local dataset using the patient's name and ID. Once collected, these data will be transmitted to a server-based diagnostic model for further analysis to determine the presence of DR.

Sponsors & Collaborators

  • Sun Yat-sen University

    lead OTHER

Principal Investigators

  • Yingfeng Zheng · Zhongshan Ophthalmic Center, Sun Yat-sen University

Study Design

Allocation
NA
Purpose
OTHER
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-05-01
Primary Completion
2023-07-30
Completion
2023-07-30

Countries

  • China

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05231174 on ClinicalTrials.gov