LensAge to Reveal Biological Age
A Deep Learning-based Indicator to Reveal Biological Age Using Lens Photographs
Study Overview
Status
Status
Conditions
Conditions
Study Type
Study Type
Enrollment (Anticipated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Haotian Lin, M.D., Ph.D.
- Phone Number: +86-020-87330274
- Email: gddlht@aliyun.com
Study Locations
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Guangdong
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Guangzhou, Guangdong, China, 510060
- Recruiting
- Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
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Contact:
- Haotain Lin, M.D., Ph.D.
- Email: gddlht@aliyun.com
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- ages from 20 to 100 years
- have anterior segment photographs
- have ophthalmic and physical examination records
Exclusion Criteria:
- have a history of previous eye surgery, eye trauma, or ocular diseases that can cause complicated cataracts
- baseline information missing
Study Plan
How is the study designed?
Design Details
- Observational Models: Cohort
- Time Perspectives: Retrospective
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
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Aging group
Participants with baseline information, medical history of diseases, and lens photographs
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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The difference between LensAge and chronological age
Time Frame: Baseline
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The age estimation models based on a convolutional neural network (CNN) using lens photographs will be used to generate LensAge.
LensAge at the individual level will be calculated by averaging the results of all images corresponding to one individual.
The difference between LensAge at the individual level and chronological age will be used to unveil an individual's aging process.
A difference above 0 indicates an individual with a faster pace of aging than their peers of the same chronological age, while a difference below 0 indicates a slower pace of aging.
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Baseline
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Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Correlation between the LensAge difference and age-related health parameters
Time Frame: Baseline
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Age-corrected LensAge differences will be used to investigate the odds ratios (ORs) with age-related health parameters.
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Baseline
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Mean absolute error (MAE) of the DL age estimation model.
Time Frame: Baseline
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Mean absolute error (MAE) in terms of both image level and individual level will be used to evaluate the performance of the DL age estimation model.
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Baseline
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Mean error (ME) of the DL age estimation model.
Time Frame: Baseline
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Mean error (ME) in terms of both image level and individual level will be used to evaluate the performance of the DL age estimation model.
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Baseline
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R-squared (R2) of the DL age estimation model.
Time Frame: Baseline
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R-squared (R2) in terms of both image level and individual level will be used to evaluate the performance of the DL age estimation model.
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Baseline
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Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Haotian Lin, M.D., Ph.D., Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Anticipated)
Primary Completion
Study Completion (Anticipated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- LA-2022
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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