NCT05231174

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

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 535 (actual)
• Sponsor: Sun Yat-sen University · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

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

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2023-05-01
• Primary completion: 2023-07-30
• Completion: 2023-07-30
• First posted: 2022-02-09
• Last updated: 2024-01-19

Locations

1 site across 1 country: China