LensAge to Reveal Biological Age

Lens Opacities Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05588921
• Other study ID #: LA-2022
• Status: Recruiting
• 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
• 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

Related Conditions & MeSH terms

• Cataract
• Lens Opacities
• Ophthalmology

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