Type 2 diabetes (T2D) is generally thought to result from the combination of two metabolic defects, insulin resistance, which increases the level of insulin required to maintain glucose within the normal range, and failure of insulin-secreting pancreatic beta cells to compensate for the increased demand. We have built up a comprehensive mathematical model of progression to T2D. The dynamics of failure and compensation can be described on two-dimensional slow manifold to investigate the mechanisms of progression to diabetes. In addition, we have extended our mathematical model by adding daily meals and hepatic glucose production, which is helpful for studying the clinical implications. These enhancements allow us to look at the mechanistic defects that underlie observed pathologies such as impaired fasting glucose (IFG) and impaired glucose tolerance (IGT). The model supports associations found in experiments between IFG and excess HGP and between IGT and peripheral insulin resistance. The model suggests how to personalize therapeutic approaches for subjects based on their underlying metabolic abnormalities.

In this talk I will discuss research with the goal of building models of brain anatomy. The neuronanatomical structures of interest can be broadly subdivided into two categories - cortical and non-cortical. Cortical structures (particularly the cerbral cortex) are typically highly folded, thin sheets of gray matter. Functionally, the cerebral cortex has been shown to have a "columnar" architecture. For this reason, we construct surface-based models for analysis of cortical properties. The construction of such models is a difficult task due to the high degree of folding of the cortical manifold in conjunction with the limited (~ 1 mm) resolution of current neuroimaging technologies. Once constructed, the cortical models can be deformed for morphometry, visualization and registration purposes. I will show some results of this type of analysis, including the morphometric changes that the cortex undergoes in disorders such as schizophrenia, Alzheimer's disease, and Huntington's disease, as well as healthy aging.
A different set of techniques have been developed for the construction of models of subcortical structures. Here, we model the segmentation as an anisotropic nonstationary Markov Random Field. The anisotropy lets us model the local spatial relationships that exist between neuroanatomical structures (e.g. hippocampus is anterior and inferior to amygdala), while the nonstationarity facilitates the encoding of inhomogeneous properties of the tissue within a structure. This approach is based on extracting the relevant model parameters from a manually labeled training set, and has been shown to be comparable in accuracy to the manual labeling.

현재 의료와 헬스케어 산업은 변혁의 시기를 지나고 있다. 의료와 헬스케어 분야를 근본적으로 변화시키고 있는 이 변혁의 시발점은 기하급수적인 디지털 기술의 발전이다. '디지털 헬스케어’ 라고 불리는 이 새로운 분야는 기존의 헬스케어 및 의료 기술이 디지털 기술과 융합되면서 태동되었다. 스마트폰, 웨어러블 디바이스, 사물인터넷, 인공지능, 3D 프린터, 클라우드 컴퓨팅, 증강 현실 등의 디지털 기술 혁신들은 헬스케어에 접목되어 기존의 건강 관리와 질병 진단 및 치료의 판도를 뒤집어 놓고 있다. 디지털 헬스케어 분야에서 어떠한 변화가 일어나고 있으며, 이 변화를 우리는 어떠한 시각으로 바라봐야 할지, 또한 우리가 맞이할 새로운 숙제와 이슈들은 무엇인지를 포괄적으로 살펴본다.