There is a growing need for precise diagnosis and personalized treatment of disease in recent years. Providing treatment tailored to each patient and maximizing efficacy and efficiency are broad goals of the healthcare system. As an engineering concept that connects the physical entity and digital space, the digital twin (DT) entered our lives at the beginning of Industry 4.0. It is evaluated as a revolution in many industrial fields and has shown the potential to be widely used in the field of medicine. This technology can offer innovative solutions for precise diagnosis and personalized treatment processes. Although there are difficulties in data collection, data fusion, and accurate simulation at this stage, we speculated that the DT may have an increasing use in the future and will become a new platform for personal health management and healthcare services. We introduced the DT technology and discussed the advantages and limitations of its applications in the medical field. This article aims to provide a perspective that combining Big Data, the Internet of Things (IoT), and artificial intelligence (AI) technology; the DT will help establish high-resolution models of patients to achieve precise diagnosis and personalized treatment.
Background: With significant advancement and demand for digital transformation, the digital twin has been gaining increasing attention as it is capable of establishing real-time mapping between physical space and virtual space. In this work, a shape-performance integrated digital twin solution is presented to predict the real-time biomechanics of the lumbar spine during human movement. Methods: A finite element model (FEM) of the lumbar spine was firstly developed using computed tomography (CT) and constrained by the body movement which was calculated by the inverse kinematics algorithm. The Gaussian process regression was utilized to train the predicted results and create the digital twin of the lumbar spine in real-time. Finally, a three-dimensional virtual reality system was developed using Unity3D to display and record the real-time biomechanics performance of the lumbar spine during body movement. Results: The evaluation results presented an agreement (R-squared > 0.8) between the real-time prediction from digital twin and offline FEM prediction. Conclusions: This approach provides an effective method of real-time planning and warning in spine rehabilitation.
As simulation is playing an increasingly important role in medicine, providing the individual patient with a customised diagnosis and treatment is envisaged as part of future precision medicine. Such customisation will become possible through the emergence of digital twin (DT) technology. The objective of this article is to review the progress of prominent research on DT technology in medicine and discuss the potential applications and future opportunities as well as several challenges remaining in digital healthcare. A review of the literature was conducted using PubMed, Web of Science, Google Scholar, Scopus and related bibliographic resources, in which the following terms and their derivatives were considered during the search: DT, medicine and digital health virtual healthcare. Finally, analyses of the literature yielded 465 pertinent articles, of which we selected 22 for detailed review. We summarised the application examples of DT in medicine and analysed the applications in many fields of medicine. It revealed encouraging results that DT is being increasing applied in medicine. Results from this literature review indicated that DT healthcare, as a key fusion approach of future medicine, will bring the advantages of precision diagnose and personalised treatment into reality.
Digital twin has the potential for increasing production, achieving real-time monitor, and realizing predictive maintenance by establishing a real-time high-fidelity mapping between the physical entity and its digital model. However, the high accuracy and instantaneousness requirements of digital twins have hindered their applications in practical engineering. This paper presents a universal framework to fulfill the requirements and to build an accurate and trustworthy digital twin by integrating numerical simulations, sensor data, multi-fidelity surrogate (MFS) models, and visualization techniques. In practical engineering, the number of sensors available to measure quantities of interest is often limited, complementary simulations are necessary to compute these quantities. The simulation results are generally more comprehensive but not as accurate as the sensor data. Therefore, the proposed framework combines the benefits of both simulation results and sensor data by using an MFS model based on moving least squares, named MFS-MLS. The MFS-MLS was developed as an essential part to calibrate the continuous field of the simulation by limited sensor data to obtain accurate results for the digital twin. Then single-fidelity surrogate models are built on the whole domain using the calibrated results of the MFS-MLS as training samples and sensor data as inputs to predict and visualize the quantities of interest in real-time. In addition, the framework was validated by a truss test case, and the results demonstrate that the proposed framework has the potential to be an effective tool to build accurate and trustworthy digital twins.
Undetected fatigue and overload damages at the key locations of the crane boom are among the biggest threats in construction, leading to structural failure. Thus, the structural health of the crane boom should be monitored in real-time to ensure that it works under the designed load capacity. In this work, we developed a lightweight digital twin by the multi-fidelity surrogate (MFS) model to improve the real-time monitoring and prediction accuracy of the structural safety of a crane boom. Digital twin technology, which can establish real-time mapping between the physical space and the digital space, has a promising potential for online monitoring and analysis of structures, equipment, and even human bodies. By combining the MFS model and sensor data, the lightweight digital twin can dynamically mirror the crane boom postures and predict its structural performance in real-time. In this study, the structural analysis of the crane boom is limited to the linear elastic stage of materials. Numerical experiments showed that the accuracy of the lightweight digital twin was enhanced compared with that established by the single-fidelity surrogate model, and the computational cost of the lightweight digital twin was decreased with respect to the digital twin built by the numerical method. Meanwhile, the uncertainty from the physical space was analyzed to enhance the reliability of the lightweight digital twin. Thus, the lightweight digital twin developed in our work can ensure accurate safety prediction and design optimization for crane booms.
Surrogate model provides a promising way to reasonably approximate complex underlying relationships between system parameters. However, the expensive modeling cost, especially in large problem sizes, hinders its applications in practical problems. To overcome this issue, with the advantages of the multi-fidelity surrogate (MFS) model, this paper proposes a single-fidelity surrogate (SFS) model with a hierarchical structure, named nonlinearity integrated correlation mapping surrogate (NI-CMS) model. The NI-CMS model first establishes the low-fidelity model to capture the underlying landscape of the true function, and then, based on the idea of MFS model, the established low-fidelity model is corrected by minimizing the mean square error to ensure prediction accuracy. Especially, a novel MFS model (named NI-MFS), is constructed to enhance the stability of the proposed NI-CMS model. More specifically, a nonlinear scaling term, which assumes the linear combination of the projected low-fidelity predictions in a high-dimensional space can reach the high-fidelity level, is introduced to assist the traditional scaling term. The performances of the proposed model are evaluated through a series of numerical test functions. In addition, a surrogate-based digital twin of an XY compliant parallel manipulator is used to validate the practical performance of the proposed model. The results show that compared with the existing models, the NI-CMS model provides a higher performance under the condition of a small sample set, illustrating the promising potential of this surrogate modeling technique.
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