LensAge to Reveal Biological Age

Study Overview
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Brief Summary
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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.

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

Conditions
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Ophthalmology, Lens Opacities, Biological Age

Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 20 Years
Maximum Age: 100 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Aging group<br>Participants with baseline information, medical history of diseases, and lens photographs | |

What is the study measuring?
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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 |