Machine Learning-based Imaging Biomarkers for Metabolic and Age-related Diseases

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Machine Learning-based Imaging Biomarkers for Metabolic and Age-related Diseases Specific Aims Age-related and metabolic diseases such as type-2 diabetes mellitus (T2DM), cardiovascular disease (CVD), obesity, osteoporosis and sarcopenia have become a worldwide epidemic that affects the quality of life of millions. To give a global perspective, roughly 343.8 million people in the world have type-2 diabetes today, and 175 million do not know they have diabetes at all. Metabolic diseases, such as diabetes and obesity, are strongly linked to longitudinal changes in body composition, morphology and function. Changes in skeletal muscle composition are strongly linked to loss in muscle strength and mass, frequently termed as sarcopenia, leading to decreased mobility and function. The accumulation of adipose tissue in the human body and changes of its regional distribution are associated with type-2 diabetes, cardiovascular disease and the metabolic syndrome. Contemporary imaging studies that are performed in vivo on a large number of participants have enabled cross sectional and longitudinal studies of age-related and metabolic diseases, and effects of pharmacological interventions. The emergence of advanced imaging technologies has also created the need for automated image analysis techniques for identification and quantification of morphological patterns of anatomies and tissues and their changes with increasing age. This project will contribute novel and non-invasive medical image analysis techniques for studying the human body composition to achieve timely diagnosis of these pathologies. Our research interests will concentrate on identification of morphological patterns in the lower extremity that will eventually lead to development of imaging biomarkers. We will use imaging and clinical data collected by the Baltimore Longitudinal Study of Aging (BLSA) that is the longest ongoing epidemiology study in the US, as well as publicly available datasets. We will build on recent advances in medical image analysis to contribute novel and non-invasive techniques for studying the human body composition and its changes (aim 1). Then we will develop machine learning methods for timely diagnosis and prognosis of metabolic and age-related diseases (aim 2). We will implement these techniques as open-source software for further use and development by the research community (aim 3).