Artificial Intelligence-Based Phenotyping of Health From Photographs - Healthy Volunteer Study
Brigham and Women's Hospital
Summary
We aim to examine how FaceAge estimates (a measure of biological age based on facial features) change across different time points and identify factors that may influence these changes. This will help us understand the consistency and reliability of the FaceAge algorithm and allow us to make improvements.
Description
Historical background The concept of biological age, as distinct from chronological age, has gained increasing attention in recent years. Biological age aims to quantify the physiological state of an individual, which can be influenced by genetic factors, lifestyle choices, and environmental exposures1-4. With advancements in artificial intelligence, new tools have emerged to estimate biological age and an individual's health from various imaging biomarkers, including facial features. One such tool is FaceAge, a deep learning system developed to estimate biological age from a single frontal…
Eligibility
- Age range
- 18+ years
- Sex
- All
- Healthy volunteers
- Yes
Inclusion Criteria: * The protocol enrolls healthy volunteers from the adult (age 18 and above) general population. All races and genders will be included. Exclusion Criteria: * Any skin condition or recent facial injury that could affect facial appearance. * Inability to follow study instructions or provide informed consent. * Taking new or strong medication on the day of image capture could potentially affect appearance or alertness.
Location
- Brigham and Women's HospitalBoston, Massachusetts