The Efficacy of an Artificial Intelligence Platform to Adapt Visual Aids for Patients With Low Vision: a Randomised Controlled Trial
NCT ID: NCT04919837
Last Updated: 2021-06-09
Study Results
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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UNKNOWN
NA
200 participants
INTERVENTIONAL
2020-07-27
2021-07-30
Brief Summary
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Traditional statistical methods were not enough to solve the problem of intelligent fitting of assisting devices. At present, there are almost no intelligent fitting models of assisting devices in the world. Therefore, in order to allow more low-vision patients to receive accurate and rapid rehabilitation services, we conducted a cross-sectional study on the assisting devices fitting for low-vision patients in Fujian Province, China in the past five years, and at the same time constructed a machine learning model to intelligently predict the adaptation result of the basic assisting devices for low vision patients.
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Detailed Description
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Conditions
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Study Design
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RANDOMIZED
PARALLEL
SUPPORTIVE_CARE
DOUBLE
Study Groups
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Algorithm assisted group
Patients receive assisting devices fitting services from human doctors assisted by the machine learning model
Low vision aids
the assisting devices fitting for low-vision patients
Human doctor group
Patients receive assisting devices fitting services from humanr doctors
Low vision aids
the assisting devices fitting for low-vision patients
Interventions
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Low vision aids
the assisting devices fitting for low-vision patients
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
3 Years
105 Years
ALL
No
Sponsors
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2nd Affilliated Hospital of Fujian Medical University
UNKNOWN
Sun Yat-sen University
OTHER
Responsible Party
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Haotian Lin
Professor
Locations
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2nd Affilliated Hospital of Jujian Medical University
Quanzhou, Fujian, China
Countries
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Facility Contacts
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Other Identifiers
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SFLVRCT-2020
Identifier Type: -
Identifier Source: org_study_id
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