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

Diabetic Retinopathy Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05231174
• Other study ID #: 2022KYPJ258
• Status: Completed
• Phase: N/A
• Start date: May 1, 2023
• Est. completion date: July 30, 2023

Study information

• Verified date: January 2024
• Source: Sun Yat-sen University
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 535
• Est. completion date: July 30, 2023
• Est. primary completion date: July 30, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Diabetic Retinopathy
• Diagnosis
• Retinal Diseases

Intervention

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.

Locations

Country	Name	City	State
China	Zhognshan Ophthalmic Center, Sun Yat-sen University	Guangzhou	Guangdong

Sponsors (1)

Lead Sponsor	Collaborator
Sun Yat-sen University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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