Using Machine Learning to Adapt Visual Aids for Patients With Low Vision
NCT ID: NCT04892316
Last Updated: 2021-05-20
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
400 participants
OBSERVATIONAL
2020-07-27
2021-12-27
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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OTHER
PROSPECTIVE
Study Groups
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Junior doctor group
Patients receive assisting devices fitting services from junior doctors
Diagnostic test
The training dataset was used to train the model, which was validated and tested by the other two datasets.
Senior doctor group
Patients receive assisting devices fitting services from senior doctors
Diagnostic test
The training dataset was used to train the model, which was validated and tested by the other two datasets.
Algorithm assisted group
Patients receive assisting devices fitting services from junior doctors assisted by the machine learning model
Diagnostic test
The training dataset was used to train the model, which was validated and tested by the other two datasets.
Interventions
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Diagnostic test
The training dataset was used to train the model, which was validated and tested by the other two datasets.
Eligibility Criteria
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Inclusion Criteria
* Aged 3 to 105
Exclusion Criteria
* Failure to sign informed consent or unwilling to participate
3 Years
105 Years
ALL
No
Sponsors
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Sun Yat-sen University
OTHER
Responsible Party
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Haotian Lin
Principal Investigator
Locations
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2nd Affilliated Hospital of Fujian Medical University
Quanzhou, Fujian, China
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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SFLV-2020
Identifier Type: -
Identifier Source: org_study_id
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