OphthalmologyNCT05588921
LensAge to Reveal Biological Age
An observational study of Ophthalmology and Lens Opacities, run by Sun Yat-sen University at 1 site in China.
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. Read more about this study
- Who is it for?
- Ages 20 to 100 yearsHealthy volunteers can take part
- Conditions the study is for
- Ophthalmology, Lens Opacities, Biological Age
- How many people
- 6,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2020to 30 Dec 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 to 100 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Participants aged 20 to 90 have anterior segment photographs, baseline information, and medical records.
Who may be able to join
- ages from 20 to 100 years
- have anterior segment photographs
- have ophthalmic and physical examination records
Who cannot join
- have a history of previous eye surgery, eye trauma, or ocular diseases that can cause complicated cataracts
- baseline information missing
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
Guangzhou, Guangdong, 510060
Haotain Lin, M.D., Ph.D., gddlht@aliyun.com
About this study
Official title: 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.
- Conditions
- Ophthalmology, Lens Opacities, Biological Age
- Medical subject headings
- Cataract
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Aging group | Participants with baseline information, medical history of diseases, and lens photographs |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Baseline
Secondary outcomes (4)
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Oct 2022
- Study start1 Jan 2020
- Primary completion30 Dec 2022 (estimated)
- Study completion30 Dec 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for OphthalmologyStudies of the same condition, open and under way.
- More trials run by Sun Yat-sen UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 6,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Haotian Lin, Sun Yat-sen University
- Investigators
- Haotian Lin, M.D., Ph.D., Principal investigator, Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05588921
- Sponsor's protocol ID
- LA-2022
- Status last verified
- Oct 2022
- First submitted
- 17 Oct 2022
- Last updated
- 21 Oct 2022
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 21 Oct 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Deep Learning-based Indicator to Reveal Biological Age Using Lens Photographs
- ClinicalTrials.gov ID
- NCT05588921
- Acronym / sponsor ID
- LA-2022
- Conditions
- Ophthalmology, Lens Opacities, Biological Age
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 6,000 participants (estimated)
- Lead sponsor
- Sun Yat-sen UniversityUniversity, hospital or other
- Study start
- 1 Jan 2020 (actual)
- Primary completion
- 30 Dec 2022 (estimated)
- Study completion
- 30 Dec 2022 (estimated)
- First posted
- 20 Oct 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Aging group | – | –Participants with baseline information, medical history of diseases, and lens photographs |
Who may qualify
- Ages
- 20 to 100 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Participants aged 20 to 90 have anterior segment photographs, baseline information, and medical records.
Key inclusion criteria (3 listed)
- ages from 20 to 100 years
- have anterior segment photographs
- have ophthalmic and physical examination records
Key exclusion criteria (2 listed)
- have a history of previous eye surgery, eye trauma, or ocular diseases that can cause complicated cataracts
- baseline information missing
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
The difference between LensAge and chronological age
Time frame: 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.
4 secondary outcomes
Correlation between the LensAge difference and age-related health parameters
Time frame: Baseline
Mean absolute error (MAE) of the DL age estimation model.
Time frame: Baseline
Mean error (ME) of the DL age estimation model.
Time frame: Baseline
R-squared (R2) of the DL age estimation model.
Time frame: Baseline
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Oct 2022.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Haotian Lin, M.D., Ph.D. principal investigator
Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Ophthalmology and Lens Opacities, run by Sun Yat-sen University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 21 Oct 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Haotian Lin, M.D., Ph.D. (+86-020-87330274, gddlht@aliyun.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The difference between LensAge and chronological age (time frame: Baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 21 Oct 2022.