LensAge to Reveal Biological Age

Sun Yat-sen University6,000 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

6,000

Conditions

Ophthalmology Lens Opacities Biological Age

Eligibility

Sex: ALLMin age: 20 YearsMax age: 100 YearsHealthy volunteers:

Medical Language ↔ Plain English

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

Locations (1)

Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity

Guangzhou, Guangdong, China

RECRUITING

Outcomes

Primary Outcomes

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.

Time frame: Baseline

Secondary Outcomes

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.

Time frame: Baseline

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.

Time frame: Baseline

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.

Time frame: Baseline

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.

Time frame: Baseline

Central Contacts

Haotian Lin, M.D., Ph.D.

CONTACT

+86-020-87330274 gddlht@aliyun.com

Data from ClinicalTrials.gov

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