Real-world Diagnostic Effectiveness of Artificial Intelligence Algorithm in Diabetic Retinopathy Screening

NCT03911323 | Unknown

• 1 other identifier: 20190401
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

Brief Summary

Recently, artificial intelligence algorithm has made great progress in the prediction of diabetic retinopathy based on fundus images，showing very high sensitivity and specificity. However，the real-world diagnosis effectiveness of deep learning model is still unclear. This study is designed to evaluate the clinical efficacy of such an algorithm in detecting referable diabetic retinopathy.

Conditions

Diabetic Retinopathy

Outcome Measures

Primary Outcomes (1)

• Sensitivity and specificity

  To evaluate the sensitivity and specificity of the model in detecting referable DR (more than mild NPDR)

  No more than 1 day for each subject

Interventions

diabetic retinopathy OTHER

Patients with diabetes enrolled will undergo nonmydriatic fundus imaging and seven-field stereoscopic photography. The images will be run on an artificial intelligence (AI) algorithm. The diagnosis of the AI algorithm will be compared to the diagnosis of seven-field stereoscopic photography by ophthalmologist. Sensitivity and specificity will be calculated to evaluate the performance of AI algorithm.

Eligibility Criteria

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

Study Population

Diabetic patients who meet the eligibility criteria.

You may qualify if:

• Subject must understand the study, participate voluntarily, and has signed informed consent
• Age 18 or older, no limitations on gender identity
• Patients with type 1 or type 2 diabetes.

You may not qualify if:

• Subjects diagnosed with eye diseases other than diabetic retinopathy
• Subjects diagnosed with macular edema, severe non-proliferative retinopathy, proliferative retinopathy, radioactive retinopathy or retinal vein obstruction.
• Pregnant woman, subjects with mydriatic allergy, unclear refractive medium, family history of glaucoma, or diagnosed as narrow angle
• Subjects with a history of laser therapy, retinal surgery or anti-vascular endothelial growth factor injection
• Subjects currently participating in another ophthalmic research, receiving ophthalmic research products.
• Subject who is photo-sensitivity or taking medication that causes photosensitivity
• Subjects received photodynamic therapy recently

Sponsors & Collaborators

• Shenzhen Second People's Hospital: lead

Study Sites (1)

Shenzhen second peoples's hospital

Shenzhen, Guangdong, 518000, China

RECRUITING

MeSH Terms

Conditions

Diabetic Retinopathy

Condition Hierarchy (Ancestors)

Retinal Diseases; Eye Diseases; Diabetic Angiopathies; Vascular Diseases; Cardiovascular Diseases; Diabetes Complications; Diabetes Mellitus; Endocrine System Diseases

Study Officials

• Lisha Mou, PhD

  Shenzhen Second People's Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Jiying Hu, PhD

CONTACT

+86-1808-6481-821; 18086481821@163.com

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 9, 2019
• First Posted: April 11, 2019
• Study Start: October 1, 2018
• Primary Completion: October 1, 2020
• Study Completion: October 1, 2020
• Last Updated: April 11, 2019

Record last verified: 2019-04

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)