Machine Learning-based Biological Ageing Estimation Technologies: A Survey

TL;DR

This survey reviews machine learning methods for biological age estimation, highlighting blood biomarkers as the most accurate, while discussing challenges and future directions in leveraging big data for health monitoring.

Contribution

It provides a comprehensive overview of ML-based biological age estimation techniques, comparing methods using blood biomarkers, facial images, and neuroimaging, and discusses future research directions.

Findings

Blood biomarker models are the most accurate and straightforward.

Facial image methods are less accurate due to external factors.

The field is moving towards leveraging big data for improved health monitoring.

Abstract

In recent years, there are various methods of estimating Biological Age (BA) have been developed. Especially with the development of machine learning (ML), there are more and more types of BA predictions, and the accuracy has been greatly improved. The models for the estimation of BA play an important role in monitoring healthy aging, and could provide new tools to detect health status in the general population and give warnings to sub-healthy people. We will mainly review three age prediction methods by using ML. They are based on blood biomarkers, facial images, and structural neuroimaging features. For now, the model using blood biomarkers is the simplest, most direct, and most accurate method. The face image method is affected by various aspects such as race, environment, etc., the prediction accuracy is not very good, which cannot make a great contribution to the medical field. In summary, we are here to track the way forward in the era of big data for us and other potential general populations and show ways to leverage the vast amounts of data available today.