The Monte Carlo N-Particle (MCNP) is often used to calculate the radiation dose during computed tomography (CT) scans. However, the physical calculation process of the model is complicated, the input file structure of the program is complex, and the three-dimensional (3D) display of the geometric model is not supported, so that the researchers cannot establish an accurate CT radiation system model, which affects the accuracy of the dose calculation results. Aiming at these two problems, this study designed a software that visualized CT modeling and automatically generated input files. In terms of model calculation, the theoretical basis was based on the integration of CT modeling improvement schemes of major researchers. For 3D model visualization, LabVIEW was used as the new development platform, constructive solid geometry (CSG) was used as the algorithm principle, and the introduction of editing of MCNP input files was used to visualize CT geometry modeling. Compared with a CT model established by a recent study, the root mean square error between the results simulated by this visual CT modeling software and the actual measurement was smaller. In conclusion, the proposed CT visualization modeling software can not only help researchers to obtain an accurate CT radiation system model, but also provide a new research idea for the geometric modeling visualization method of MCNP.
A good cushion can not only provide the sitter with a high comfort, but also control the distribution of the hip pressure to reduce the incidence of diseases. The purpose of this study is to introduce a computer-aided design (CAD) modeling method of the buttocks-cushion using numerical finite element (FE) simulation to predict the pressure distribution on the buttocks-cushion interface. The buttock and the cushion model geometrics were acquired from a laser scanner, and the CAD software was used to create the solid model. The FE model of a true seated individual was developed using ANSYS software (ANSYS Inc, Canonsburg, PA). The model is divided into two parts, i.e. the cushion model made of foam and the buttock model represented by the pelvis covered with a soft tissue layer. Loading simulations consisted of imposing a vertical force of 520N on the pelvis, corresponding to the weight of the user upper extremity, and then solving iteratively the system.
Virtual clinical trials are clinical trials conducted through computer simulation technology, which breaks through the limitations of traditional clinical trials and has the advantages of saving time, reducing costs, and reducing the risk of human trials. With the application of new computer technologies such as population pharmacokinetics, physiologically-based pharmacokinetics, quantitative systems pharmacology, and artificial intelligence, the field of virtual clinical trials in healthcare has become an important development direction. This article will give a preliminary review of the connotation, methods and future development trends of virtual clinical trials, aiming to provide reference for the application of new technologies and methods in clinical trials.
Valvular heart disease is a structural or functional disease of the heart due to rheumatic fever, congenital malformation, infection, or trauma, resulting in abnormal cardiac hemodynamics and ultimately heart failure. Implantation of artificial heart valves has become the main way to treat heart valvular disease. Because the structure of the artificial heart valve plays a key role in the stress distribution and hemodynamic performance of the valve and stent, the geometric configuration of the artificial heart valve is constantly updated and improved during its development from mechanical valve to biological valve, which closely mimics the geometric characteristics of the normal natural heart valve. This article sums up the design process of geometric configuration of artificial heart valves and the influence of geometric configuration on the central disc stress and durability of artificial heart valves, analyzes the important parameters of geometric modeling for artificial heart valves, and discusses the development of the corresponding modeling method, to provide reference and new ideas for the biomimetic optimization design of artificial valves.
The geometric bone model of patients is an important basis for individualized biomechanical modeling and analysis, formulation of surgical planning, design of surgical guide plate, and customization of artificial joint. In this study, a rapid three-dimensional (3D) reconstruction method based on statistical shape model was proposed for femur. Combined with the patient plain X-ray film data, rapid 3D modeling of individualized patient femur geometry was realized. The average error of 3D reconstruction was 1.597–1.842 mm, and the root mean square error was 1.453–2.341 mm. The average errors of femoral head diameter, cervical shaft angle, offset distance and anteversion angle of the reconstructed model were 0.597 mm, 1.163°, 1.389 mm and 1.354°, respectively. Compared with traditional modeling methods, the new method could achieve rapid 3D reconstruction of femur more accurately in a shorter time. This paper provides a new technology for rapid 3D modeling of bone geometry, which is helpful to promote rapid biomechanical analysis for patients, and provides a new idea for the selection of orthopedic implants and the rapid research and development of customized implants.
Objective To evaluated the application effect of reverse digital modeling combined with three-dimensional (3D)-printed disease models in the standardized training of orthopedic residents focusing on pelvic tumors. Methods From August 2022 to August 2023, 60 orthopedic residents from West China Hospital, Sichuan University were randomly assigned to a trial group (n=30) and a control group (n=30). The trial group received instruction using reverse digital modeling and 3D-printed pelvic tumor models, while the control group underwent traditional teaching methods. Teaching outcomes were evaluated and compared between groups through knowledge tests, practical skill assessments, and satisfaction surveys. Results Before training, there was no statistically significant difference in knowledge tests or practical skill assessments between the two groups (P>0.05). After training, the trial group showed significantly better performance than the control group in knowledge tests (90.5±5.2 vs. 78.4±6.8, P<0.05), skill assessments (92.7±4.9 vs. 81.3±6.2, P<0.05), and satisfaction surveys (9.40±1.10 vs. 7.60±1.20, P<0.05). One month after training, the trial group still showed significantly better performance than the control group in knowledge tests (88.1±6.4 vs. 72.3±7.1, P<0.05) and skill assessments (90.3±5.8 vs. 75.6±6.9, P<0.05). Conclusions Reverse digital modeling combined with 3D printing offers an intuitive and effective teaching approach that improves comprehension of pelvic tumor anatomy and strengthens clinical and technical competencies. This method significantly enhances learning outcomes in standardized residency training and holds promise for broader integration into medical education.
During transfer tasks, the dual-arm nursing-care robot require a human-robot mechanics model to determine the balance region to support the patient safely and stably. Previous studies utilized human-robot two-dimensional static equilibrium models, ignoring the human body volume and muscle torques, which decreased model accuracy and confined the robot ability to adjust the patient’s posture in three-dimensional spatial. Therefore, this study proposes a three-dimensional spatial mechanics modeling method based on individualized human musculoskeletal multibody dynamics. Firstly, based on the mechanical features of dual-arm support, this study constructed a foundational three-dimensional human-robot mechanics model including body posture, contact position and body force. With the computed tomography data from subjects, a three-dimensional femur-pelvis-sacrum model was reconstructed, and the individualized musculoskeletal dynamics was analyzed using the ergonomics software, which derived the human joint forces and completed the mechanic model. Then, this study established a dual-arm robot transfer platform to conduct subject transfer experiments, showing that the constructed mechanics model possessed higher accuracy than previous methods. In summary, this study provides a three-dimensional human-robot mechanics model adapting to individual transfers, which has potential application in various scenarios such as nursing-care and rehabilitating robots.
Integration of heterogeneous systems is the key to hospital information construction due to complexity of the healthcare environment. Currently, during the process of healthcare information system integration, people participating in integration project usually communicate by free-format document, which impairs the efficiency and adaptability of integration. A method utilizing business process model and notation (BPMN) to model integration requirement and automatically transforming it to executable integration configuration was proposed in this paper. Based on the method, a tool was developed to model integration requirement and transform it to integration configuration. In addition, an integration case in radiology scenario was used to verify the method.
In order to quantitatively analyze the morphology and period of pulse signals, a time-space analytical modeling and quantitative analysis method for pulse signals were proposed. Firstly, according to the production mechanism of the pulse signal, the pulse space-time analytical model was built after integrating the period and baseline of pulse signal into the analytical model, and the model mathematical expression and its 12 parameters were obtained for pulse wave quantification. Then, the model parameters estimation process based on the actual pulse signal was presented, and the optimization method, constraints and boundary conditions in parameter estimation were given. The spatial-temporal analytical modeling method was applied to the pulse waves of healthy subjects from the international standard physiological signal sub-database Fantasia of the PhysioNet in open-source, and we derived some changes in heartbeat rhythm and hemodynamic generated by aging and gender difference from the analytical models. The model parameters were employed as the input of some machine learning methods, e.g. random forest and probabilistic neural network, to classify the pulse waves by age and gender, and the results showed that random forest has the best classification performance with Kappa coefficients over 98%. Therefore, the space-time analytical modeling method proposed in this study can effectively quantify and analyze the pulse signal, which provides a theoretical basis and technical framework for some related applications based on pulse signals.