Validation of the Utility of Ophthalmology Intelligent Diagnostic System

NCT ID: NCT03499145

Last Updated: 2019-10-21

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

COMPLETED

Total Enrollment

615 participants

Study Classification

OBSERVATIONAL

Study Start Date

2018-04-01

Study Completion Date

2019-08-31

Brief Summary

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The prevention and treatment of diseases via artificial intelligence represents an ultimate goal in computational medicine. Application scenarios of the current medical algorithms are too simple to be generally applied to real-world complex clinical settings. Here, the investigators use "deep learning" and "visionome technique", an novel annotation method for artificial intelligence in medical, to create an automatic detection and classification system for four key clinical scenarios: 1) mass screening, 2) comprehensive clinical triage, 3) hyperfine diagnostic assessment, and 4) multi-path treatment planning. The investigator also establish a telemedicine system and conduct clinical trial and website-based study to validate its versatility.

Detailed Description

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Conditions

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Ophthalmopathy Artificial Intelligence

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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Eligible patients for AI test.

Device: ophthalmology diagnostic system. An artificial intelligence to make comprehensive evaluation and treatment decision of ocular diseases.

Ophthalmology diagnostic system.

Intervention Type DEVICE

An artificial intelligence to make comprehensive evaluation and treatment decision of ocular diseases.

Interventions

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Ophthalmology diagnostic system.

An artificial intelligence to make comprehensive evaluation and treatment decision of ocular diseases.

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

* Patients and residents who underwent ophthalmic examination of the eye and recorded their ocular information in the outpatient clinic and community.
Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Ministry of Health, China

OTHER_GOV

Sponsor Role collaborator

Xidian University

OTHER

Sponsor Role collaborator

Sun Yat-sen University

OTHER

Sponsor Role lead

Responsible Party

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Haotian Lin

Clinical Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Zhongshan Ophthalmic Center, Sun Yat-sen University

Guangzhou, Guangdong, China

Site Status

Countries

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China

References

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Li W, Yang Y, Zhang K, Long E, He L, Zhang L, Zhu Y, Chen C, Liu Z, Wu X, Yun D, Lv J, Liu Y, Liu X, Lin H. Dense anatomical annotation of slit-lamp images improves the performance of deep learning for the diagnosis of ophthalmic disorders. Nat Biomed Eng. 2020 Aug;4(8):767-777. doi: 10.1038/s41551-020-0577-y. Epub 2020 Jun 22.

Reference Type DERIVED
PMID: 32572198 (View on PubMed)

Other Identifiers

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CCPMOH2018-China-2

Identifier Type: -

Identifier Source: org_study_id

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