A linked mixture model of coronary atherosclerosis

Background & Objectives: Characterizing the progression of coronary atherosclerosis is a critical public health goal. The most common quantitative summary, the CAC score, is modelled by a variety of statistical methods, both as a predictor of coronary events and as an outcome of behavioral and population-specific risk factors. Little attempt has been made, however, to ground these statistical models in the underlying physiology of arterial aging, which would allow us to describe the onset and growth of CAC over a patient's life. Methods: Using a generative growth model for arterial plaque accumulation, we identify severe under-estimation in the age of initial onset and rate of progression (doubling time) of CAC growth with standard ln(CAC + 1) or ln(CAC | CAC > 0) models, and use this growth model to motivate new statistical approaches to CAC using logistic and log-linear mixture regressions. We compare statistical models directly by computing their average parameter biases using 540 growth trajectory simulations (113,760 patients, 268,200 observations). Results: While all models used can successfully estimate the influence of risk factors with minimal bias, we demonstrate substantial improvements in predictive accuracy in the timing of CAC onset and progression with logistic regression and linked hurdle-lognormal mixture regression, compared with standard ln(CAC + 1) or ln(CAC | CAC > 0) models. Conclusions: Using models that can account for patient-specific onset and progression rates, accurate descriptions of CAC trajectories can be made even in cross-sectional (single scan per patient) designs, with substantial clinical and epidemiological utility.

Learning Grasp Configuration Through Object-Specific Hand Primitives for Posture Planning of Anthropomorphic Hands

The proposal of postural synergy theory has provided a new approach to solve the problem of controlling anthropomorphic hands with multiple degrees of freedom. However, generating the grasp configuration for new tasks in this context remains challenging. This study proposes a method to learn grasp configuration according to the shape of the object by using postural synergy theory. By referring to past research, an experimental paradigm is first designed that enables the grasping of 50 typical objects in grasping and operational tasks. The angles of the finger joints of 10 subjects were then recorded when performing these tasks. Following this, four hand primitives were extracted by using principal component analysis, and a low-dimensional synergy subspace was established. The problem of planning the trajectories of the joints was thus transformed into that of determining the synergy input for trajectory planning in low-dimensional space. The average synergy inputs for the trajectories of each task were obtained through the Gaussian mixture regression, and several Gaussian processes were trained to infer the inputs trajectories of a given shape descriptor for similar tasks. Finally, the feasibility of the proposed method was verified by simulations involving the generation of grasp configurations for a prosthetic hand control. The error in the reconstructed posture was compared with those obtained by using postural synergies in past work. The results show that the proposed method can realize movements similar to those of the human hand during grasping actions, and its range of use can be extended from simple grasping tasks to complex operational tasks.