LensAge to Reveal Biological Age
A Deep Learning-based Indicator to Reveal Biological Age Using Lens Photographs
1 other identifier
observational
6,000
1 country
1
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jan 2020
Typical duration for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
January 1, 2020
CompletedFirst Submitted
Initial submission to the registry
October 17, 2022
CompletedFirst Posted
Study publicly available on registry
October 20, 2022
CompletedPrimary Completion
Last participant's last visit for primary outcome
December 30, 2022
CompletedStudy Completion
Last participant's last visit for all outcomes
December 30, 2022
CompletedOctober 21, 2022
October 1, 2022
3 years
October 17, 2022
October 19, 2022
Conditions
Outcome Measures
Primary Outcomes (1)
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 Outcomes (4)
Correlation between the LensAge difference and age-related health parameters
Baseline
Mean absolute error (MAE) of the DL age estimation model.
Baseline
Mean error (ME) of the DL age estimation model.
Baseline
R-squared (R2) of the DL age estimation model.
Baseline
Study Arms (1)
Aging group
Participants with baseline information, medical history of diseases, and lens photographs
Eligibility Criteria
Participants aged 20 to 90 have anterior segment photographs, baseline information, and medical records.
You may qualify if:
- ages from 20 to 100 years
- have anterior segment photographs
- have ophthalmic and physical examination records
You may not qualify if:
- have a history of previous eye surgery, eye trauma, or ocular diseases that can cause complicated cataracts
- baseline information missing
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
Guangzhou, Guangdong, 510060, China
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Haotian Lin, M.D., Ph.D.
Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Professor
Study Record Dates
First Submitted
October 17, 2022
First Posted
October 20, 2022
Study Start
January 1, 2020
Primary Completion
December 30, 2022
Study Completion
December 30, 2022
Last Updated
October 21, 2022
Record last verified: 2022-10