Development and Validation of a Deep Learning System for Multiple Ocular Fundus Diseases Using Retinal Images

NCT04213430 | Unknown

• 1 other identifier: CCPMOH2019- China8
• Study Type: observational
• Target: 300,000
• Locations: 1 country
• Sites: 1

Brief Summary

Retinal images can reflect both fundus and systemic conditions (diabetes and cardiovascular disease) and firstly to be used for medical artificial intelligence (AI) algorithm training due to its advantages of clinical significance and easy to obtain. Here, the investigators developed a single network model that can mine the characteristics among multiple fundus diseases, which was trained by plenty of fundus images with one or several disease labels (if they have) in each of them. The model performance was compared with those of both native and international ophthalmologists. The model was further tested by datasets with different camera types and validated by three external datasets prospectively collected from the clinical sites where the model would be applied.

Conditions

Ophthalmological Disorder

Outcome Measures

Primary Outcomes (1)

• Area under the receiver operating characteristic curve of the deep learning system

  The investigators will calculate the area under the receiver operating characteristic curve of deep learning system and compare this index between deep learning system and human doctors.

  baseline

Secondary Outcomes (2)

• Sensitivity of the deep learning system

  baseline

• Specificity of the deep learning system

  baseline

Study Arms (3)

Training dataset

Retinal images collected from hospitals and multiple screening sites all over China

Validation dataset

Retinal images separated from training dataset

Other: diagnostic

Testing dataset

Retinal images prospectively collected from the hospitals and ocular disease screening sites totally different from training dataset

Other: diagnostic

Interventions

diagnostic OTHER

Training dataset was used to train the deep learning model, which was validated and tested by other two datasets.

Testing dataset; Validation dataset

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Retinal images were collected from different health care institutes all over China and other countries around the world.

You may qualify if:

• The quality of fundus images should clinical acceptable. More than 80% of the fundus image area including four main regions (optic disk, macular, upper and lower retinal vessel archs) are easy to read and discriminate.

You may not qualify if:

• Images with light leakage (\>30% of area), spots from lens flares or stains, and overexposure were excluded from further analysis.

Sponsors & Collaborators

• Sun Yat-sen University: lead

Study Sites (1)

Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity

Guangzhou, Guangdong, 510060, China

RECRUITING

Related Publications (1)

• Lin D, Xiong J, Liu C, Zhao L, Li Z, Yu S, Wu X, Ge Z, Hu X, Wang B, Fu M, Zhao X, Wang X, Zhu Y, Chen C, Li T, Li Y, Wei W, Zhao M, Li J, Xu F, Ding L, Tan G, Xiang Y, Hu Y, Zhang P, Han Y, Li JO, Wei L, Zhu P, Liu Y, Chen W, Ting DSW, Wong TY, Chen Y, Lin H. Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study. Lancet Digit Health. 2021 Aug;3(8):e486-e495. doi: 10.1016/S2589-7500(21)00086-8.

  PMID: 34325853 DERIVED

MeSH Terms

Conditions

Eye Diseases

Central Study Contacts

Haotian Lin, PhD

CONTACT

13802793086; gddlht@aliyun.com

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Prof.

Study Record Dates

• First Submitted: December 23, 2019
• First Posted: December 30, 2019
• Study Start: January 1, 2014
• Primary Completion: February 1, 2020
• Study Completion: May 1, 2020
• Last Updated: December 30, 2019

Record last verified: 2019-12

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)