In population-oriented ergonomics product design and musculoskeletal kinetics analysis, digital spine models of different shape, pose and material property are in great demand. The purpose of this study was to construct a parameterized finite element spine model with adjustable spine shape and material property. We used statistical shape model approach to learn inter-subject shape variations from 65 CT images of training subjects. Second order polynomial regression was used to model the age-dependent changes in vertebral material property derived from spatially aligned CT images. Finally, a parametric spine generator was developed to create finite element instances of different shapes and material properties. For quantitative analysis, the generalization ability to emulate spine shapes of different people was evaluated by fitting into 17 test CT images. The median fitting accuracy was 0.8 for Dice coefficient and 0.43 mm for average surface distance. The age-dependent bone density regression curve was also proved to well agree with large population statistics data. Finite element simulation was performed to compare how shape parameters influenced the biomechanics distribution of spine. The proposed parametric finite element whole spine model will assist the design process of new devices and biomechanical simulation towards a wide range of population.
SVS is a complication arising from excessive cerebrospinal fluid shunting after shunt surgery. It has complex pathogenesis and typical clinical symptoms. But abnormalities may not be detected on the images, resulting in misdiagnosis and missing the best time for treatment. Therefore, we cannot rely too much on imaging resources when treating such patients. We also need to make the most correct diagnosis and effective treatment based on the patient's condition and our clinical experience.
Dialysis is a process for removing the waste and excess water from the blood used for people with renal failure.The existing dialysis machine draws blood directly to the dialyzer, and the flow velocity of blood is difficult to be controlled. This paper proposes a rule-based expert system fusing the neural network method to maintain the stability and the safety of the blood drainage during hemodialysis. The expert system with a rule base is presented for the blood pumps to change the rotating speed in order to keep a proper volume of blood for dialysis. Then, the neural network is combined with the rule-based expert system to analyze the influences of the air bubbles and the pressures in catheters to the speeds of blood pumps. The simulations and the experiments on an actual dialysis sessions are carried out, which verify the hemodynamic stability and the effect of the proposed method.
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