Multi-modal Imaging and Artificial Intelligence Diagnostic System for Multi-level Clinical Application

This study is to build an multi-modal artificial intelligence ophthalmological imaging diagnostic system covering multi-level medical institutions. We are going to evaluate this system in an evidence-based medicine view, taking diabetic retinopathy as an example. And clinical diagnostic criteria will be made based on this multi-modal artificial intelligence imaging diagnostic system. The study is designed as a cross-sectional study involving 1,000 normal individuals, 1,000 diabetes patients without ocular complications, and 1,000 with diabetic ocular complications. Statistical analysis of the diagnostic sensitivity and specificity of the artificial intelligence system will be made, and ROC curve wil be draw.

Study Overview

• Status: Unknown
• Conditions: Diabetic Retinopathy; Diabete Mellitus

• Study Type: Observational
• Enrollment (Anticipated): 3000

Contacts and Locations

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510000
      • Recruiting
      • Zhongshan Opthalmic Center

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Stratified sampling

Description

Inclusion Criteria:

Group A(normal individuals): Meet all the items 1 ~ 5 below

1. No familial or hereditary retinopathy;
2. No history of ocular trauma and / or history of ocular disease (except for mild to moderate refractive errors and / or age-related cataracts);
3. No drug use history that may cause side effects of retina (such as chloroquine, hydroxychloroquine, chlorpromazine, rifampin, etc.); 4 binocular diopter ≤ ± 6.00D; No systemic diseases that may affect the retina.

Group B(diabetes patients without ocular complications): meet any of 1 to 3, and both 4 to 5 items

1. Diagnosed with type 1 diabetes for more than 5 years;
2. Diagnosed with type 2 diabetes patients;
3. Diagnosis of gestational diabetes patients;
4. Without systemic disease that may affect the retina except for diabetes;
5. Meet the standards of 1 to 4 items.

Group C(patients with diabetic ocular complications): meet with any of 1 to 3, and all 4 to 6 items

1. Diagnosed with type 1 diabetes for more than 5 years;
2. Diagnosed with type 2 diabetes patients;
3. Diagnosis of gestational diabetes patients;
4. Meet the guidelines for diagnosis and treatment of diabetic retinopathy in any stage of diabetic retinopathy and / or diabetic macular edema.
5. Without systemic disease that may affect the retina except for diabetes;
6. A group to meet the criteria for 1 to 4 items.

Exclusion Criteria:

1. Refractive pathway is turbid, so that fundus image can not be clearly photographed;
2. With other fundus diseases and / or other diseases that seriously affect the visual function;
3. Any eye infection, such as conjunctivitis, keratitis, scleritis, endophthalmitis, acute and chronic dacryocystitis, or eye injury;
4. Serious systemic diseases such as diabetes, hypertension, heart failur,renal failure;
5. Can not cooperate with the examiner due to mental or other reasons.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The diagnostic sensitivity, specificity and ROC curve of the artificial intelligence（AI） system compared with reference standard (ophthalmologist).	Sensitivity: the percentage of diabetic retinopathy（DR） patients who are correctly identified as having the condition by AI. Sensitivity=True positive/(True positive+False negative). Specificity: the percentage of healthy people who are correctly identified as not having the condition by AI. Specificity=True negative /(True negative +False positive). The ROC curve was plotted with true positive rate (sensitivity) as the ordinate and false positive rate (1-specificity) as the abscissa.	It will take 15~20min for each subject to take the exams.

Collaborators and Investigators

• Sponsor: Zhongshan Ophthalmic Center, Sun Yat-sen University
• Investigators: Principal Investigator: Yuan Jin, Professor, Zhongshan Opthalmic Center

Study record dates

Study Major Dates

• Study Start (Actual): April 23, 2018
• Primary Completion (Anticipated): December 31, 2019
• Study Completion (Anticipated): December 31, 2019

Study Registration Dates

• First Submitted: March 29, 2019
• First Submitted That Met QC Criteria: March 31, 2019
• First Posted (Actual): April 2, 2019

Study Record Updates

• Last Update Posted (Actual): April 2, 2019
• Last Update Submitted That Met QC Criteria: March 31, 2019
• Last Verified: March 1, 2019

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Diabetic Retinopathy; Diabete Mellitus
• Additional Relevant MeSH Terms: Cardiovascular Diseases; Vascular Diseases; Glucose Metabolism Disorders; Metabolic Diseases; Eye Diseases; Endocrine System Diseases; Diabetic Angiopathies; Diabetes Complications; Diabetes Mellitus; Retinal Diseases; Diabetic Retinopathy
• Other Study ID Numbers: 2017KYPJ104

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No