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

NCT ID: NCT05231174

Last Updated: 2024-01-19

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 535 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2023-05-01
• Study Completion Date: 2023-07-30

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

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: OTHER
• Blinding Strategy: NONE

Study Groups

A self-evlaution tool based on Large Language Model

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.

Group Type: EXPERIMENTAL

A self-evlaution tool based on Large Language Model

Intervention Type: 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.

Interventions

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.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

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.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Sun Yat-sen University

OTHER

Sponsor Role: lead

Responsible Party

Yingfeng Zheng

Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Yingfeng Zheng

Role: PRINCIPAL_INVESTIGATOR

Zhongshan Ophthalmic Center, Sun Yat-sen University

Locations

Zhognshan Ophthalmic Center, Sun Yat-sen University

Guangzhou, Guangdong, China

Countries

China

Other Identifiers

2022KYPJ258

Identifier Type: -

Identifier Source: org_study_id