How Old do you look?

Yova Health uses facial recognition technology to estimate biological age as part of a broader health assessment, combining facial analysis with lifestyle and health data (like diet, exercise, and biomarkers) to provide personalized health recommendations.

Predicting biological age from facial features is a rapidly advancing field that leverages machine learning, computer vision, and medical science to estimate an individual's "biological age" based on visual cues. Unlike chronological age (how old you are in years), biological age refers to how old your body appears to be based on various health indicators, including facial features.

1. Chronological Age: The number of years a person has lived.

2. Biological Age: An estimate of how "old" someone is on a cellular level, which may differ from their chronological age due to factors like lifestyle, genetics, and  environmental influences. Biological age can provide insights into a person’s overall health and longevity.

Several studies and technological developments have shown that facial analysis can be used to predict biological age. These systems analyze facial features such as wrinkles, skin texture, pigmentation, and facial structure to estimate an individual's biological age.

1. AI and Machine Learning Models:

a. **Deep Learning Algorithms**: Researchers have developed deep learning models that analyze high-resolution images of faces to estimate biological age.These models are trained on large datasets of facial images with known biological age markers (e.g., telomere length, epigenetic markers) to  learn correlations between facial features and biological aging.

2. **Key Studies**:

a. **Hannum et al. (2013)** Genome-wide methylation profiles reveal quantitative views of human aging rates : This study demonstrated the correlation between epigenetic changes and biological age, and subsequent research has extended this to visual markers. Facial features reflect some of these epigenetic changes and can serve as proxies for underlying biological aging processes.

b. **Belsky et al. (2015)** Quantification of biological aging in young adults : This study demonstrated that adults who are aging faster feel and look older than their peers. “Based on the facial images alone, student raters scored study members with advanced Biological Age as looking older than their biologically younger peers (r = 0.21, P < 0.001).”

1. **Accuracy**:  While facial analysis provides useful information, it’s not a definitive measure of biological age. Factors like lighting, facial expressions, and image quality can affect the accuracy of predictions.

2. **Ethnic and Genetic Differences**: Aging manifests differently across different ethnicities, so models must be trained on diverse datasets to ensure accurate predictions across populations.

3. **Lifestyle Influences**: Factors such as sun exposure, smoking, and stress      significantly impact facial aging, and these need to be considered when predicting biological age.

Using facial features to predict biological age is an emerging field that combines cutting-edge AI technology with medical research. While not perfect, facial analysis can provide insights into the aging process and help identify early signs of accelerated aging. This has applications in cosmetics, wellness, and even healthcare, where it can be used to monitor and potentially slow down age-related decline.

For the most accurate assessment, facial-based biological age estimation is often used in combination with other biomarkers like blood tests, epigenetic analysis, and lifestyle data. Check out the Phenotypic Age Estimator on the next page to get your age prediction based on the blood biomarkers.