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

NCT05231174 | Completed

• 1 other identifier: 2022KYPJ258
• Study Type: interventional
• Target: 535
• Locations: 1 country
• Sites: 1

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.

Conditions

Diagnosis; Diabetic Retinopathy

Outcome Measures

Primary Outcomes (1)

• 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.

  Immediately after using the chatbot

Study Arms (1)

A self-evlaution tool based on Large Language Model

EXPERIMENTAL

The self-evlaution tool, powered by a large language model, processes user queries through a comprehensive generation, decision, action, and safety framework to deliver optimal responses. The system's key features include retrieval-augmented in-context learning, which enhances the responses generated by sourcing information from reliable websites. It also incorporates a Guardrail module to mitigate potential harmful content in the responses by validating the content before delivery. Additionally, the system features a Self-checking memory module that maintains essential clinical characteristics across multi-turn dialogues, ensuring consistent and continuous interactions with users.

Other: A self-evlaution tool based on Large Language Model

Interventions

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.

A self-evlaution tool based on Large Language Model

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

Sponsors & Collaborators

• Sun Yat-sen University: lead

Study Sites (1)

Zhognshan Ophthalmic Center, Sun Yat-sen University

Guangzhou, Guangdong, 510000, China

Location

MeSH Terms

Conditions

Disease; Diabetic Retinopathy

Condition Hierarchy (Ancestors)

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

Study Officials

• Yingfeng Zheng

  Zhongshan Ophthalmic Center, Sun Yat-sen University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: OTHER
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: January 29, 2022
• First Posted: February 9, 2022
• Study Start: May 1, 2023
• Primary Completion: July 30, 2023
• Study Completion: July 30, 2023
• Last Updated: January 19, 2024

Record last verified: 2024-01

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)