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

NCT ID: NCT04213430

Last Updated: 2019-12-30

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

300000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2014-01-31

Study Completion Date

2020-05-31

Brief Summary

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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.

Detailed Description

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Conditions

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Ophthalmological Disorder

Study Design

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Observational Model Type

OTHER

Study Time Perspective

OTHER

Study Groups

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Training dataset

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

No interventions assigned to this group

Validation dataset

Retinal images separated from training dataset

diagnostic

Intervention Type OTHER

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

Testing dataset

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

diagnostic

Intervention Type OTHER

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

Interventions

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diagnostic

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

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* 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.

Exclusion Criteria

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

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Sun Yat-sen University

OTHER

Sponsor Role lead

Responsible Party

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Haotian Lin

Prof.

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity

Guangzhou, Guangdong, China

Site Status RECRUITING

Countries

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China

Central Contacts

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Haotian Lin, PhD

Role: CONTACT

Phone: 13802793086

Email: [email protected]

Facility Contacts

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Haotian Lin, Ph.D

Role: primary

References

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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.

Reference Type DERIVED
PMID: 34325853 (View on PubMed)

Other Identifiers

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CCPMOH2019- China8

Identifier Type: -

Identifier Source: org_study_id