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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT05588921
Other study ID # LA-2022
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date January 1, 2020
Est. completion date December 30, 2022

Study information

Verified date October 2022
Source Sun Yat-sen University
Contact Haotian Lin, M.D., Ph.D.
Phone +86-020-87330274
Email gddlht@aliyun.com
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

Assessment of aging is central to health management. Compared to chronological age, biological age can better reflect the aging process and health status; however, an effective indicator of biological age in clinical practice is lacking. Human lens accumulates biological changes during aging and is amenable to a rapid and objective assessment. Therefore, the investigators will develop LensAge as an innovative indicator to reveal biological age based on deep learning using lens photographs.


Recruitment information / eligibility

Status Recruiting
Enrollment 6000
Est. completion date December 30, 2022
Est. primary completion date December 30, 2022
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group 20 Years to 100 Years
Eligibility 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 Design


Related Conditions & MeSH terms


Locations

Country Name City State
China Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity Guangzhou Guangdong

Sponsors (1)

Lead Sponsor Collaborator
Sun Yat-sen University

Country where clinical trial is conducted

China, 

Outcome

Type Measure Description Time frame Safety issue
Primary The difference between LensAge and chronological age 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. Baseline
Secondary Correlation between the LensAge difference and age-related health parameters Age-corrected LensAge differences will be used to investigate the odds ratios (ORs) with age-related health parameters. Baseline
Secondary Mean absolute error (MAE) of the DL age estimation model. 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. Baseline
Secondary Mean error (ME) of the DL age estimation model. 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. Baseline
Secondary R-squared (R2) of the DL age estimation model. 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. Baseline
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