LensAge to Reveal Biological Age

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

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 Overview

• Status: Recruiting
• Conditions: Lens Opacities; Ophthalmology; Biological Age

• Study Type: Observational
• Enrollment (Anticipated): 6000

Contacts and Locations

• Study Contact: Name: Haotian Lin, M.D., Ph.D.; Phone Number: +86-020-87330274; Email: gddlht@aliyun.com

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510060
      • Recruiting
      • Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
      • Contact: Haotain Lin, M.D., Ph.D.; Email: gddlht@aliyun.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 98 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Participants aged 20 to 90 have anterior segment photographs, baseline information, and medical records.

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

1

Cohorts and Interventions

Group / Cohort
Aging group; Participants with baseline information, medical history of diseases, and lens photographs

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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
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
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
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

Collaborators and Investigators

• Sponsor: Sun Yat-sen University
• 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): January 1, 2020
• Primary Completion (Anticipated): December 30, 2022
• Study Completion (Anticipated): December 30, 2022

Study Registration Dates

• First Submitted: October 17, 2022
• First Submitted That Met QC Criteria: October 17, 2022
• First Posted (Actual): October 20, 2022

Study Record Updates

• Last Update Posted (Actual): October 21, 2022
• Last Update Submitted That Met QC Criteria: October 19, 2022
• Last Verified: October 1, 2022

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Eye Diseases; Lens Diseases; Cataract
• Other Study ID Numbers: LA-2022

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No