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Validation of the Utility of Ophthalmology Intelligent Diagnostic System

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT03499145
Recruitment Status : Completed
First Posted : April 17, 2018
Last Update Posted : October 21, 2019
Sponsor:
Collaborators:
Ministry of Health, China
Xidian University
Information provided by (Responsible Party):
Haotian Lin, Sun Yat-sen University

Brief Summary:
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.

Condition or disease Intervention/treatment
Ophthalmopathy Artificial Intelligence Device: Ophthalmology diagnostic system.

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Study Type : Observational
Actual Enrollment : 615 participants
Observational Model: Cohort
Time Perspective: Prospective
Official Title: Validation of the Utility of Ophthalmology Intelligent Diagnostic System: A Clinical Trial
Actual Study Start Date : April 1, 2018
Actual Primary Completion Date : August 31, 2019
Actual Study Completion Date : August 31, 2019

Group/Cohort Intervention/treatment
Eligible patients for AI test.
Device: ophthalmology diagnostic system. An artificial intelligence to make comprehensive evaluation and treatment decision of ocular diseases.
Device: Ophthalmology diagnostic system.
An artificial intelligence to make comprehensive evaluation and treatment decision of ocular diseases.




Primary Outcome Measures :
  1. The proportion of accurate, mistaken and miss detection of the ophthalmology diagnostic system. [ Time Frame: Up to 5 years ]


Information from the National Library of Medicine

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Ages Eligible for Study:   Child, Adult, Older Adult
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Sampling Method:   Probability Sample
Study Population
A prospective study of patients and residents who use the web platform for diagnosis.
Criteria

Inclusion Criteria:

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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03499145


Locations
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China, Guangdong
Zhongshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong, China, 510000
Sponsors and Collaborators
Sun Yat-sen University
Ministry of Health, China
Xidian University

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Responsible Party: Haotian Lin, Clinical Professor, Sun Yat-sen University
ClinicalTrials.gov Identifier: NCT03499145     History of Changes
Other Study ID Numbers: CCPMOH2018-China-2
First Posted: April 17, 2018    Key Record Dates
Last Update Posted: October 21, 2019
Last Verified: October 2019

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Haotian Lin, Sun Yat-sen University:
conjunctivitis
keratitis
pterygium
cataracts
Additional relevant MeSH terms:
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Eye Diseases