Proceedings Article | 2 April 2024
KEYWORDS: Biological research, Biological imaging, Head, Computed tomography, Image processing, Education and training, X-ray computed tomography, Statistical analysis, Performance modeling, Neuroimaging
While chronological age is based on time elapsed from the time of the birth of a person, cellular or biological age (BA) is a measure of gradual decline in cell division and proper functioning. BA may be affected by intrinsic factors like genetic changes and cellular metabolism as well as extrinsic factors like exposure to environmental toxins, lifestyle, and diet [1][2][3]. While chronological age is often used as a crude estimate of BA to assess risk of mortality and morbidity, BA can provide a more accurate risk estimate. Even with the lack of a universally agreed upon definition and quantification of BA, laboratory test based BA estimation measures have been well-established [4][5][6]. Biological age related markers may be incidentally collected in medical data such as physical activity from wearable devices[7][8] and medical imaging data [9][10][11][12][13][14][15][16]. Age-related changes in bone density[17][18], body composition[19][20][21], cardiovascular structures[22][23], lungs[24][25], and ocular structures[26][27] have been demonstrated on imaging. Deep learning based image processing models have recently been designed to quantify such biomarkers for biological age estimation using magnetic resonance imaging of the brain[28][29], retinal photograph[30][31], and chest X-rays[32]. “Brain age” has been established as indicative of cognitive decline and mortality[28][29]. Degree of atrophy in brain tissue captured in radiological images such as brain MR and head CT, can indicate biological age of a person which may be different from chronological age. Biological age estimation studies have often connected risk of mortality and morbidity with larger gaps between chronological and biological age[30][31]. While this gap may be caused to some extent by intrinsic factors, social determinants of health (SDoH) can influence biological age, accelerating cellular aging and overall health outcomes[33]. Electronic health records (EHR) collected for patients in hospitals may provide clues to social determinants of health but cannot guarantee quantification of its influence. Objective of the current study is to understand the relation between biological age calculated using imaging features derived from head CT studies and social determinants of health. Our hypothesis is that higher biological age than chronological age primarily represents poor quality of life including unhealthy diet, stress, exposure to environmental toxins, sedentary lifestyle, etc. Social deprivation index (SDI) is a composite measure of seven demographic characteristics collected in the American Community Survey (ACS) to quantify social determinants based on geolocation: percent living in poverty, percent with less than 12 years of education, percent single-parent households, the percentage living in rented housing units, the percentage living in the overcrowded housing unit, percent of households without a car, and percentage unemployed adults under 65 years of age. SDI can address challenges in quantifying social determinants of health to best guide clinical and community health interventions. The SDI measure was calculated at the four geographic areas: county, census tract, aggregated Zip Code Tabulation Area (ZCTA), and Primary Care Service Area (PCSA, v 3.1). While raw values are directly computed by the formula using the seven measures described above, SDI score is a value normalized between 0- 100 with higher value indicating higher extent of social disadvantage. We use SDI score as a surrogate for social determinant of health and study the relation between gaps in biological age estimated by our deep learning-based imaging model and chronological age of the patient at the time of the imaging exam. In order to prove our hypothesis, we adopted two parallel experiments - (i) (post-processing) establish the correlation between model computed biological age and SDI; (ii) (in-processing) include SDI during the learning of the model which may boost the model performance for biological age by providing surrogate for social determinant of the patient.
