LensAge to Reveal Biological Age

A Deep Learning-based Indicator to Reveal Biological Age Using Lens Photographs

Status

UNKNOWN

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05588921

Enrollment

6000

Registered

2022-10-20

Start date

2020-01-01

Completion date

2022-12-30

Last updated

2022-10-21

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Biological Age, Lens Opacities, Ophthalmology

Brief 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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

20 Years to 100 Years

Healthy volunteers

Yes

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

Design outcomes

Primary

Measure	Time frame	Description
The difference between LensAge and chronological age	Baseline	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.

Secondary

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

Countries

China

Contacts

Primary ContactHaotian Lin, M.D., Ph.D.

gddlht@aliyun.com+86-020-87330274

Outcome results