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

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.

Completed

Looking for future studies?

Notify Me

Key information

Conditions

Diagnosis Cardiovascular Diseases Diabetes Complications Diabetes Mellitus Diabetic Angiopathies Diabetic Retinopathy Disease Endocrine System Diseases Eye Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms Retinal Diseases Vascular Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Zhognshan Ophthalmic Center, Sun Yat-sen University

Guangzhou, Guangdong, 510000, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

A self-evlaution tool based on Large Language Model

Other

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.

Primary outcomes

AUROC of the self-evaluation tool

Time frame: Immediately after using the chatbot

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

Sponsors and collaborators

Lead sponsor

Sun Yat-sen University

Other

Registry information

Important dates

Study start: 2023
Primary completion: 2023
Study completion: 2023
First posted: Feb 9, 2022
Registry last updated: Jan 19, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed