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

Sun Yat-sen University535 enrolled

Overview

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.

Study Type

INTERVENTIONAL

Allocation

Purpose

OTHER

Masking

NONE

Enrollment

535

Conditions

Diagnosis Diabetic Retinopathy

Interventions

A self-evlaution tool based on Large Language ModelOTHER

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.

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Inclusion Criteria The study will include adults aged 18 years and above who have been diagnosed with Type 2 diabetes but have not previously been screened for DR. Participants must demonstrate good compliance with clinical examinations, and provide informed consent. Exclusion criteria The study will exclude patients who have previously been diagnosed with DR, those who have recently undergone eye surgery, and those with other significant eye diseases that could potentially confound the results of DR screening. Individuals with ocular, auditory, or cognitive impairments that prevent the use of mobile phones or reading will also be excluded.

Locations (1)

Zhognshan Ophthalmic Center, Sun Yat-sen University

Guangzhou, Guangdong, China

Outcomes

Primary Outcomes

AUROC of the self-evaluation tool

The performance of the self-evaluation tool is evaluated with accuracy with reference to the diagnostic labels by senior ophthalmologists based on fundus photos.

Time frame: Immediately after using the chatbot

Data from ClinicalTrials.gov

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