Current Role at Stanford
Joy Ku is focused on biocomputation and the advancement of their use through teaching, science communications, community building, and the promotion of research resource sharing efforts, particularly as related to reproducibility and open-source science.
She is currently Deputy Director of the Wu Tsai Human Performance Alliance at Stanford (https://humanperformance.stanford.edu) and also leads the education and outreach efforts for the overall Wu Tsai Human Performance Alliance, which consists of institutions across the country, including Boston Children's Hospital, Salk, UC San Diego, the University of Kansas, and the University of Oregon. The Alliance's mission is to discover biological principles to optimize human performance and catalyze innovations in human health.
Dr. Ku is also the Director of Promotions and Didactic Interactions for the NIH-funded Restore Center (https://restore.stanford.edu), as well as the Director of Education and Communications for the Mobilize Center (https://mobilize.stanford.edu), an NIH Biomedical Technology Resource Center. Both Centers provide tools, infrastructure, and training to support the research community. The Mobilize Center's emphasis is on biomechanical modeling and machine learning algorithms to provide new insights into human movement from data sources, such as wearables, video, and medical images. The Restore Center's mission is to advance rehabilitation research using mobile sensor and video technology for real-world assessments of movement and factors affecting movement.
She also manages SimTK (https://simtk.org), a software, model, and data-sharing platform for the biocomputation research community.
Leveraging Mobile Technology for Public Health Promotion: A Multidisciplinary Perspective.
Annual review of public health
Health behaviors are inextricably linked to health and well-being, yet issues such as physical inactivity and insufficient sleep remain significant global public health problems. Mobile technology-and the unprecedented scope and quantity of data it generates-has a promising but largely untapped potential to promote health behaviors at the individual and population levels. This perspective article provides multidisciplinary recommendations on the design and use of mobile technology, and the concomitant wealth of data, to promote behaviors that support overall health. Using physical activity as an exemplar health behavior, we review emerging strategies for health behavior change interventions. We describe progress on personalizing interventions to an individual and their social, cultural, and built environments, as well as on evaluating relationships between mobile technology data and health to establish evidence-based guidelines. In reviewing these strategies and highlighting directions for future research, we advance the use of theory-based, personalized, and human-centered approaches in promoting health behaviors. Expected final online publication date for the Annual Review of Public Health, Volume 44 is April 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
View details for DOI 10.1146/annurev-publhealth-060220-041643
View details for PubMedID 36542772
- Mobile Health: making the leap to research and clinics NPJ DIGITAL MEDICINE 2021; 4 (1): 83
Credible practice of modeling and simulation in healthcare: ten rules from a multidisciplinary perspective
JOURNAL OF TRANSLATIONAL MEDICINE
2020; 18 (1): 369
The complexities of modern biomedicine are rapidly increasing. Thus, modeling and simulation have become increasingly important as a strategy to understand and predict the trajectory of pathophysiology, disease genesis, and disease spread in support of clinical and policy decisions. In such cases, inappropriate or ill-placed trust in the model and simulation outcomes may result in negative outcomes, and hence illustrate the need to formalize the execution and communication of modeling and simulation practices. Although verification and validation have been generally accepted as significant components of a model's credibility, they cannot be assumed to equate to a holistic credible practice, which includes activities that can impact comprehension and in-depth examination inherent in the development and reuse of the models. For the past several years, the Committee on Credible Practice of Modeling and Simulation in Healthcare, an interdisciplinary group seeded from a U.S. interagency initiative, has worked to codify best practices. Here, we provide Ten Rules for credible practice of modeling and simulation in healthcare developed from a comparative analysis by the Committee's multidisciplinary membership, followed by a large stakeholder community survey. These rules establish a unified conceptual framework for modeling and simulation design, implementation, evaluation, dissemination and usage across the modeling and simulation life-cycle. While biomedical science and clinical care domains have somewhat different requirements and expectations for credible practice, our study converged on rules that would be useful across a broad swath of model types. In brief, the rules are: (1) Define context clearly. (2) Use contextually appropriate data. (3) Evaluate within context. (4) List limitations explicitly. (5) Use version control. (6) Document appropriately. (7) Disseminate broadly. (8) Get independent reviews. (9) Test competing implementations. (10) Conform to standards. Although some of these are common sense guidelines, we have found that many are often missed or misconstrued, even by seasoned practitioners. Computational models are already widely used in basic science to generate new biomedical knowledge. As they penetrate clinical care and healthcare policy, contributing to personalized and precision medicine, clinical safety will require established guidelines for the credible practice of modeling and simulation in healthcare.
View details for DOI 10.1186/s12967-020-02540-4
View details for Web of Science ID 000576928000001
View details for PubMedID 32993675
View details for PubMedCentralID PMC7526418
Reference data on in vitro anatomy and indentation response of tissue layers of musculoskeletal extremities
2020; 7 (1): 20
The skin, fat, and muscle of the musculoskeletal system provide essential support and protection to the human body. The interaction between individual layers and their composite structure dictate the body's response during mechanical loading of extremity surfaces. Quantifying such interactions may improve surgical outcomes by enhancing surgical simulations with lifelike tissue characteristics. Recently, a comprehensive tissue thickness and anthropometric database of in vivo extremities was acquired using a load sensing instrumented ultrasound to enhance the fidelity of advancing surgical simulations. However detailed anatomy of tissue layers of musculoskeletal extremities was not captured. This study aims to supplement that database with an enhanced dataset of in vitro specimens that includes ultrasound imaging supported by motion tracking of the ultrasound probe and two additional full field imaging modalities (magnetic resonance and computed tomography). The additional imaging datasets can be used in conjunction with the ultrasound/force data for more comprehensive modeling of soft tissue mechanics. Researchers can also use the image modalities in isolation if anatomy of legs and arms is needed.
View details for DOI 10.1038/s41597-020-0358-1
View details for Web of Science ID 000511441300002
View details for PubMedID 31941894
View details for PubMedCentralID PMC6962198
Reference data on thickness and mechanics of tissue layers and anthropometry of musculoskeletal extremities
2018; 5: 180193
Musculoskeletal extremities exhibit a multi-layer tissue structure that is composed of skin, fat, and muscle. Body composition and anthropometric measurements have been used to assess health status and build anatomically accurate biomechanical models of the limbs. However, comprehensive datasets inclusive of regional tissue anatomy and response under mechanical manipulation are missing. The goal of this study was to acquire and disseminate anatomical and mechanical data collected on extremities of the general population. An ultrasound system, instrumented with a load transducer, was used for in vivo characterization of skin, fat, and muscle thicknesses in the extremities of 100 subjects at unloaded (minimal force) and loaded (through indentation) states. For each subject, the unloaded and loaded state provided anatomic tissue layer measures and tissue indentation response for 48 and 8 regions, respectively. A publicly available web-based system has been used for data management and dissemination. This comprehensive database will provide the foundation for comparative studies in regional musculoskeletal composition and improve visual and haptic realism for computational models of the limbs.
View details for DOI 10.1038/sdata.2018.193
View details for Web of Science ID 000445578300001
View details for PubMedID 30251995
View details for PubMedCentralID PMC6154283
OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement.
PLoS computational biology
2018; 14 (7): e1006223
Movement is fundamental to human and animal life, emerging through interaction of complex neural, muscular, and skeletal systems. Study of movement draws from and contributes to diverse fields, including biology, neuroscience, mechanics, and robotics. OpenSim unites methods from these fields to create fast and accurate simulations of movement, enabling two fundamental tasks. First, the software can calculate variables that are difficult to measure experimentally, such as the forces generated by muscles and the stretch and recoil of tendons during movement. Second, OpenSim can predict novel movements from models of motor control, such as kinematic adaptations of human gait during loaded or inclined walking. Changes in musculoskeletal dynamics following surgery or due to human-device interaction can also be simulated; these simulations have played a vital role in several applications, including the design of implantable mechanical devices to improve human grasping in individuals with paralysis. OpenSim is an extensible and user-friendly software package built on decades of knowledge about computational modeling and simulation of biomechanical systems. OpenSim's design enables computational scientists to create new state-of-the-art software tools and empowers others to use these tools in research and clinical applications. OpenSim supports a large and growing community of biomechanics and rehabilitation researchers, facilitating exchange of models and simulations for reproducing and extending discoveries. Examples, tutorials, documentation, and an active user forum support this community. The OpenSim software is covered by the Apache License 2.0, which permits its use for any purpose including both nonprofit and commercial applications. The source code is freely and anonymously accessible on GitHub, where the community is welcomed to make contributions. Platform-specific installers of OpenSim include a GUI and are available on simtk.org.
View details for PubMedID 30048444
Credibility, Replicability, and Reproducibility in Simulation for Biomedicine and Clinical Applications in Neuroscience
FRONTIERS IN NEUROINFORMATICS
2018; 12: 18
Modeling and simulation in computational neuroscience is currently a research enterprise to better understand neural systems. It is not yet directly applicable to the problems of patients with brain disease. To be used for clinical applications, there must not only be considerable progress in the field but also a concerted effort to use best practices in order to demonstrate model credibility to regulatory bodies, to clinics and hospitals, to doctors, and to patients. In doing this for neuroscience, we can learn lessons from long-standing practices in other areas of simulation (aircraft, computer chips), from software engineering, and from other biomedical disciplines. In this manuscript, we introduce some basic concepts that will be important in the development of credible clinical neuroscience models: reproducibility and replicability; verification and validation; model configuration; and procedures and processes for credible mechanistic multiscale modeling. We also discuss how garnering strong community involvement can promote model credibility. Finally, in addition to direct usage with patients, we note the potential for simulation usage in the area of Simulation-Based Medical Education, an area which to date has been primarily reliant on physical models (mannequins) and scenario-based simulations rather than on numerical simulations.
View details for DOI 10.3389/fninf.2018.00018
View details for Web of Science ID 000430129400001
View details for PubMedID 29713272
View details for PubMedCentralID PMC5911506
Perspectives on Sharing Models and Related Resources in Computational Biomechanics Research
JOURNAL OF BIOMECHANICAL ENGINEERING-TRANSACTIONS OF THE ASME
2018; 140 (2)
The role of computational modeling for biomechanics research and related clinical care will be increasingly prominent. The biomechanics community has been developing computational models routinely for exploration of the mechanics and mechanobiology of diverse biological structures. As a result, a large array of models, data, and discipline-specific simulation software has emerged to support endeavors in computational biomechanics. Sharing computational models and related data and simulation software has first become a utilitarian interest, and now, it is a necessity. Exchange of models, in support of knowledge exchange provided by scholarly publishing, has important implications. Specifically, model sharing can facilitate assessment of reproducibility in computational biomechanics and can provide an opportunity for repurposing and reuse, and a venue for medical training. The community's desire to investigate biological and biomechanical phenomena crossing multiple systems, scales, and physical domains, also motivates sharing of modeling resources as blending of models developed by domain experts will be a required step for comprehensive simulation studies as well as the enhancement of their rigor and reproducibility. The goal of this paper is to understand current perspectives in the biomechanics community for the sharing of computational models and related resources. Opinions on opportunities, challenges, and pathways to model sharing, particularly as part of the scholarly publishing workflow, were sought. A group of journal editors and a handful of investigators active in computational biomechanics were approached to collect short opinion pieces as a part of a larger effort of the IEEE EMBS Computational Biology and the Physiome Technical Committee to address model reproducibility through publications. A synthesis of these opinion pieces indicates that the community recognizes the necessity and usefulness of model sharing. There is a strong will to facilitate model sharing, and there are corresponding initiatives by the scientific journals. Outside the publishing enterprise, infrastructure to facilitate model sharing in biomechanics exists, and simulation software developers are interested in accommodating the community's needs for sharing of modeling resources. Encouragement for the use of standardized markups, concerns related to quality assurance, acknowledgement of increased burden, and importance of stewardship of resources are noted. In the short-term, it is advisable that the community builds upon recent strategies and experiments with new pathways for continued demonstration of model sharing, its promotion, and its utility. Nonetheless, the need for a long-term strategy to unify approaches in sharing computational models and related resources is acknowledged. Development of a sustainable platform supported by a culture of open model sharing will likely evolve through continued and inclusive discussions bringing all stakeholders at the table, e.g., by possibly establishing a consortium.
View details for PubMedID 29247253
View details for PubMedCentralID PMC5821103
The mobilize center: an NIH big data to knowledge center to advance human movement research and improve mobility.
Journal of the American Medical Informatics Association
2015; 22 (6): 1120-1125
Regular physical activity helps prevent heart disease, stroke, diabetes, and other chronic diseases, yet a broad range of conditions impair mobility at great personal and societal cost. Vast amounts of data characterizing human movement are available from research labs, clinics, and millions of smartphones and wearable sensors, but integration and analysis of this large quantity of mobility data are extremely challenging. The authors have established the Mobilize Center (http://mobilize.stanford.edu) to harness these data to improve human mobility and help lay the foundation for using data science methods in biomedicine. The Center is organized around 4 data science research cores: biomechanical modeling, statistical learning, behavioral and social modeling, and integrative modeling. Important biomedical applications, such as osteoarthritis and weight management, will focus the development of new data science methods. By developing these new approaches, sharing data and validated software tools, and training thousands of researchers, the Mobilize Center will transform human movement research.
View details for DOI 10.1093/jamia/ocv071
View details for PubMedID 26272077
View details for PubMedCentralID PMC4639715
OpenMM 4: A Reusable, Extensible, Hardware Independent Library for High Performance Molecular Simulation
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
2013; 9 (1): 461-469
OpenMM is a software toolkit for performing molecular simulations on a range of high performance computing architectures. It is based on a layered architecture: the lower layers function as a reusable library that can be invoked by any application, while the upper layers form a complete environment for running molecular simulations. The library API hides all hardware-specific dependencies and optimizations from the users and developers of simulation programs: they can be run without modification on any hardware on which the API has been implemented. The current implementations of OpenMM include support for graphics processing units using the OpenCL and CUDA frameworks. In addition, OpenMM was designed to be extensible, so new hardware architectures can be accommodated and new functionality (e.g., energy terms and integrators) can be easily added.
View details for DOI 10.1021/ct300857j
View details for Web of Science ID 000313378700049
View details for PubMedCentralID PMC3539733
Simbios: an NIH national center for physics-based simulation of biological structures
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
2012; 19 (2): 186-189
Physics-based simulation provides a powerful framework for understanding biological form and function. Simulations can be used by biologists to study macromolecular assemblies and by clinicians to design treatments for diseases. Simulations help biomedical researchers understand the physical constraints on biological systems as they engineer novel drugs, synthetic tissues, medical devices, and surgical interventions. Although individual biomedical investigators make outstanding contributions to physics-based simulation, the field has been fragmented. Applications are typically limited to a single physical scale, and individual investigators usually must create their own software. These conditions created a major barrier to advancing simulation capabilities. In 2004, we established a National Center for Physics-Based Simulation of Biological Structures (Simbios) to help integrate the field and accelerate biomedical research. In 6 years, Simbios has become a vibrant national center, with collaborators in 16 states and eight countries. Simbios focuses on problems at both the molecular scale and the organismal level, with a long-term goal of uniting these in accurate multiscale simulations.
View details for DOI 10.1136/amiajnl-2011-000488
View details for Web of Science ID 000300768100009
View details for PubMedID 22081222
View details for PubMedCentralID PMC3277621
Comparison of CFD and MRI flow and velocities in an in vitro large artery bypass graft model
ANNALS OF BIOMEDICAL ENGINEERING
2005; 33 (3): 257-269
Bypass graft failures have been attributed to various hemodynamic factors, including flow stasis and low shear stress. Ideally, surgeries would minimize the occurrence of these detrimental flow conditions, but surgeons cannot currently assess this. Numerical simulation techniques have been proposed as one method for predicting changes in flow distributions and patterns from surgical bypass procedures, but comparisons against experimental results are needed to assess their usefulness. Previous in vitro studies compared simulated results against experimentally obtained measurements, but they focused on peripheral arteries, which have lower Reynolds numbers than those found in the larger arteries. In this study, we compared simulation results against measurements obtained using magnetic resonance imaging (MRI) techniques for a phantom model of a stenotic vessel with a bypass graft under conditions suitable for surgical planning purposes and with inlet Reynolds numbers closer to those found inthe larger arteries. Comparisons of flow rate and velocity profiles were performed at maximum and minimum flows at four locations and used simulation results that were temporally and spatially averaged, key postprocessing when comparing against phase contrast MRI measurements. The maximum error in the computed volumetric flow rates was 6% of the measured values, and excellent qualitative agreement was obtained for the through-plane velocity profiles in both magnitude and shape. The in-plane velocities also agreed reasonably well at most locations.
View details for DOI 10.1007/s10439-005-1729-7
View details for Web of Science ID 000228208300001
View details for PubMedID 15868717
In vivo validation of a one-dimensional finite-element method for predicting blood flow in cardiovascular bypass grafts
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
2003; 50 (6): 649-656
Current practice in vascular surgery utilizes only diagnostic and empirical data to plan treatments and does not enable quantitative a priori prediction of the outcomes of interventions. We have previously described a new approach to vascular surgery planning based on solving the governing equations of blood flow in patient-specific models. A one-dimensional finite-element method was used to simulate blood flow in eight porcine thoraco-thoraco aortic bypass models. The predicted flow rate was compared to in vivo data obtained using cine phase-contrast magnet resonance imaging. The mean absolute difference between computed and measured flow distribution in the stenosed aorta was found to be 4.2% with the maximum difference of 10.6% anda minimum difference of 0.4%. Furthermore, the sensitivity of the flow rate and distribution with respect to stenosis and branch losses were quantified.
View details for DOI 10.1109/TBME.2003.812201
View details for Web of Science ID 000183413100001
View details for PubMedID 12814231
Internet-based system for simulation-based medical planning for cardiovascular disease
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE
2003; 7 (2): 123-129
Current practice in vascular surgery utilizes only diagnostic and empirical data to plan treatments, which does not enable quantitative a priori prediction of the outcomes of interventions. We have previously described simulation-based medical planning methods to model blood flow in arteries and plan medical treatments based on physiologic models. An important consideration for the design of these patient-specific modeling systems is the accessibility to physicians with modest computational resources. We describe a simulation-based medical planning environment developed for the World Wide Web (WWW) using the Virtual Reality Modeling Language (VRML) and the Java programming language.
View details for DOI 10.1109/TITB.2003.811880
View details for Web of Science ID 000183723600007
View details for PubMedID 12834168
In vivo validation of numerical prediction of blood flow in arterial bypass grafts
ANNALS OF BIOMEDICAL ENGINEERING
2002; 30 (6): 743-752
In planning operations for patients with cardiovascular disease, vascular surgeons rely on their training, past experiences with patients with similar conditions, and diagnostic imaging data. However, variability in patient anatomy and physiology makes it difficult to quantitatively predict the surgical outcome for a specific patient a priori. We have developed a simulation-based medical planning system that utilizes three-dimensional finite-element analysis methods and patient-specific anatomic and physiologic information to predict changes in blood flow resulting from surgical bypass procedures. In order to apply these computational methods, they must be validated against direct experimental measurements. In this study, we compared in vivo flow measurements obtained using magnetic resonance imaging techniques to calculated flow values predicted using our analysis methods in thoraco-thoraco aortic bypass procedures in eight pigs. Predicted average flow rates and flow rate waveforms were compared for two locations. The predicted and measured waveforms had similar shapes and amplitudes, while flow distribution predictions were within 10.6% of the experimental data. The average absolute difference in the bypass-to-inlet blood flow ratio was 5.4 +/- 2.8%. For the aorta-to-inlet blood flow ratio, the average absolute difference was 6.0 +/- 3.3%.
View details for DOI 10.1114/1.1496086
View details for Web of Science ID 000177640900001
View details for PubMedID 12220075
In vivo validation of a one-dimensional finite element method for simulation-based medical planning for cardiovascular bypass surgery
23rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society
IEEE. 2001: 120–123
View details for Web of Science ID 000178871900036
Predictive medicine: computational techniques in therapeutic decision-making.
Computer aided surgery
1999; 4 (5): 231-247
The current paradigm for surgery planning for the treatment of cardiovascular disease relies exclusively on diagnostic imaging data to define the present state of the patient, empirical data to evaluate the efficacy of prior treatments for similar patients, and the judgement of the surgeon to decide on a preferred treatment. The individual variability and inherent complexity of human biological systems is such that diagnostic imaging and empirical data alone are insufficient to predict the outcome of a given treatment for an individual patient. We propose a new paradigm of predictive medicine in which the physician utilizes computational tools to construct and evaluate a combined anatomic/physiologic model to predict the outcome of alternative treatment plans for an individual patient. The predictive medicine paradigm is implemented in a software system developed for Simulation-Based Medical Planning. This system provides an integrated set of tools to test hypotheses regarding the effect of alternate treatment plans on blood flow in the cardiovascular system of an individual patient. It combines an Internet-based user interface developed using Java and VRML, image segmentation, geometric solid modeling, automatic finite element mesh generation, computational fluid dynamics, and scientific visualization techniques. This system is applied to the evaluation of alternate, patient-specific treatments for a case of lower extremity occlusive cardiovascular disease.
View details for PubMedID 10581521