![]() There is no clear consensus on which method is best for BA estimation, nor on how best to quantify the BA itself. Other methods that have been used to assess biological age or speed of aging includes handgrip strength, locomotor activity, and deep learning on biomarker data. Bobrov et al proposed a deep learning based model (called PhotoAgeClock) to estimate chronological age using images of eye corners. However, combining brain age with grey matter and cerebrospinal fluid volumes did not improve mortality risk prediction. They combined DNA-methylation with brain age and showed that the combination improved mortality risk prediction. The predicted age was identified as ”brain-predicted age” or ”brain age” for short. Cole et al studied the use of structural neuro-imaging such as MRI under a Gaussian process regression framework to estimate biological age. They analyzed within-individual longitudinal change in 18 biomarkers from the Dunedin Study across chronological ages 26 y, 32 y, 38 y to quantify each study member’s personal rate of physiological deterioration. The study also tested the hypothesis that young adults with older biological age at age 38 years were actually aging faster than those with a younger biological age. They estimated the BA for subjects at age 38 using the Klemera-Doubal equation with parameters estimated from the NHANES-III dataset. Belsky et al described biological age as a reflection of ongoing longitudinal change within a person. They employed the KD algorithm in predicting mortality. Mitnitski et al compared the performance of the frailty index (FI) with biomarker-based measures of BA. WAI is a measure that reflects present health condition rather than how it changes with age and their analysis showed that the KD method on PCA features produced the most reliable results. Cho et al studied various BA estimation methods to examine the relation with work ability index (WAI). Overall, the performance of biological age (BA) in mortality prediction was significantly better than using chronological age (CA). Klemera and Doubal’s (KD) method was found to be the most reliable predictor for mortality. Levine compared the performance of five BA estimation algorithms in terms of their ability to predict mortality. Other approaches include multiple linear regression (MLR), and combination of MLR with principal component analysis (PCA) features. The biological age (BA) estimates are derived based on minimizing the distance between biomarker points and regression lines. ![]() Klemera and Doubal’s approach is the most popular, and perhaps, the most effective biological age estimation method. To estimate biological age, some age-dependent variables are used, ,, and chronological age may or may not be a required attribute/variable depending on the application. Quantification of biological age is a difficult challenge, since there is no well defined criteria. The common idea is that, biological age provides a better estimator of the true life expectancy of the individual than his or her chronological age. Biological age (sometimes called functional age ) lacks a precise definition, but it is often viewed as the true age of an individual in the gerontology and aging research community. However, biological age is based on the interesting, yet confounded, idea that a person’s true age can be different from his/her chronological age. Chronological age estimation from face image is most popular. Chronological age is typically what we know and is based on the date of birth. This brings up the debate on “chronological” versus “biological” age. But two different people of the same age may have very different health conditions and mortality hazards. In general, a younger person is expected to have a better health condition and his/her mortality hazard should be low in comparison with a relatively older person. Aging is a gradual process that results in increased health risk, and mortality over time. The major challenge is that most of the measures used to characterize age, for instance, visual appearance, and biological/physiological markers vary significantly from person to person, even for people of the same chronological age.Īge has a deep connection with health and mortality. Doing this automatically by a machine is an even more onerous task. Although age estimation has been practiced for centuries, accurate age estimation is known to be a difficult problem. Human age estimation is an important problem that has witnessed an increased attention, given its role in various daily activities, from health assessment, to social interaction, to security and identity profiling.
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