A framework for different levels of integration of computational models into web-based virtual patients

BACKGROUND

Virtual patients are increasingly common tools used in health care education to foster learning of clinical reasoning skills. One potential way to expand their functionality is to augment virtual patients' interactivity by enriching them with computational models of physiological and pathological processes.

OBJECTIVE

The primary goal of this paper was to propose a conceptual framework for the integration of computational models within virtual patients, with particular focus on (1) characteristics to be addressed while preparing the integration, (2) the extent of the integration, (3) strategies to achieve integration, and (4) methods for evaluating the feasibility of integration. An additional goal was to pilot the first investigation of changing framework variables on altering perceptions of integration.

METHODS

The framework was constructed using an iterative process informed by Soft System Methodology. The Virtual Physiological Human (VPH) initiative has been used as a source of new computational models. The technical challenges associated with development of virtual patients enhanced by computational models are discussed from the perspectives of a number of different stakeholders. Concrete design and evaluation steps are discussed in the context of an exemplar virtual patient employing the results of the VPH ARCH project, as well as improvements for future iterations.

RESULTS

The proposed framework consists of four main elements. The first element is a list of feasibility features characterizing the integration process from three perspectives: the computational modelling researcher, the health care educationalist, and the virtual patient system developer. The second element included three integration levels: basic, where a single set of simulation outcomes is generated for specific nodes in the activity graph; intermediate, involving pre-generation of simulation datasets over a range of input parameters; advanced, including dynamic solution of the model. The third element is the description of four integration strategies, and the last element consisted of evaluation profiles specifying the relevant feasibility features and acceptance thresholds for specific purposes. The group of experts who evaluated the virtual patient exemplar found higher integration more interesting, but at the same time they were more concerned with the validity of the result. The observed differences were not statistically significant.

CONCLUSIONS

This paper outlines a framework for the integration of computational models into virtual patients. The opportunities and challenges of model exploitation are discussed from a number of user perspectives, considering different levels of model integration. The long-term aim for future research is to isolate the most crucial factors in the framework and to determine their influence on the integration outcome.

The onset of type 2 diabetes: proposal for a multi-scale model

Background

Type 2 diabetes mellitus (T2D) is a common age-related disease, and is a major health concern, particularly in developed countries where the population is aging, including Europe. The multi-scale immune system simulator for the onset of type 2 diabetes (MISSION-T2D) is a European Union-funded project that aims to develop and validate an integrated, multilevel, and patient-specific model, incorporating genetic, metabolic, and nutritional data for the simulation and prediction of metabolic and inflammatory processes in the onset and progression of T2D. The project will ultimately provide a tool for diagnosis and clinical decision making that can estimate the risk of developing T2D and predict its progression in response to possible therapies. Recent data showed that T2D and its complications, specifically in the heart, kidney, retina, and feet, should be considered a systemic disease that is sustained by a pervasive, metabolically-driven state of inflammation. Accordingly, there is an urgent need (1) to understand the complex mechanisms underpinning the onset of this disease, and (2) to identify early patient-specific diagnostic parameters and related inflammatory indicators.

Objective

We aim to accomplish this mission by setting up a multi-scale model to study the systemic interactions of the biological mechanisms involved in response to a variety of nutritional and metabolic stimuli and stressors.

Methods

Specifically, we will be studying the biological mechanisms of immunological/inflammatory processes, energy intake/expenditure ratio, and cell cycle rate. The overall architecture of the model will exploit an already established immune system simulator as well as several discrete and continuous mathematical methods for modeling of the processes critically involved in the onset and progression of T2D. We aim to validate the predictions of our models using actual biological and clinical data.

Results

This study was initiated in March 2013 and is expected to be completed by February 2016.

Conclusions

MISSION-T2D aims to pave the way for translating validated multilevel immune-metabolic models into the clinical setting of T2D. This approach will eventually generate predictive biomarkers for this disease from the integration of clinical data with metabolic, nutritional, immune/inflammatory, genetic, and gut microbiota profiles. Eventually, it should prove possible to translate these into cost-effective and mobile-based diagnostic tools.

Integrative approaches to computational biomedicine

The new discipline of computational biomedicine is concerned with the application of computer-based techniques and particularly modelling and simulation to human health. Since 2007, this discipline has been synonymous, in Europe, with the name given to the European Union's ambitious investment in integrating these techniques with the eventual aim of modelling the human body as a whole: the virtual physiological human. This programme and its successors are expected, over the next decades, to transform the study and practice of healthcare, moving it towards the priorities known as ‘4P's’: predictive, preventative, personalized and participatory medicine.

Development and implementation of a web-enabled 3D consultation tool for breast augmentation surgery based on 3D-image reconstruction of 2D pictures

Background

Producing a rich, personalized Web-based consultation tool for plastic surgeons and patients is challenging.

Objective

(1) To develop a computer tool that allows individual reconstruction and simulation of 3-dimensional (3D) soft tissue from ordinary digital photos of breasts, (2) to implement a Web-based, worldwide-accessible preoperative surgical planning platform for plastic surgeons, and (3) to validate this tool through a quality control analysis by comparing 3D laser scans of the patients with the 3D reconstructions with this tool from original 2-dimensional (2D) pictures of the same patients.

Methods

The proposed system uses well-established 2D digital photos for reconstruction into a 3D torso, which is then available to the user for interactive planning. The simulation is performed on dedicated servers, accessible via Internet. It allows the surgeon, together with the patient, to previsualize the impact of the proposed breast augmentation directly during the consultation before a surgery is decided upon. We retrospectively conduced a quality control assessment of available anonymized pre- and postoperative 2D digital photographs of patients undergoing breast augmentation procedures. The method presented above was used to reconstruct 3D pictures from 2D digital pictures. We used a laser scanner capable of generating a highly accurate surface model of the patient’s anatomy to acquire ground truth data. The quality of the computed 3D reconstructions was compared with the ground truth data used to perform both qualitative and quantitative evaluations.

Results

We evaluated the system on 11 clinical cases for surface reconstructions and 4 clinical cases of postoperative simulations, using laser surface scan technologies showing a mean reconstruction error between 2 and 4 mm and a maximum outlier error of 16 mm. Qualitative and quantitative analyses from plastic surgeons demonstrate the potential of these new emerging technologies.

Conclusions

We tested our tool for 3D, Web-based, patient-specific consultation in the clinical scenario of breast augmentation. This example shows that the current state of development allows for creation of responsive and effective Web-based, 3D medical tools, even with highly complex and time-consuming computation, by off-loading them to a dedicated high-performance data center. The efficient combination of advanced technologies, based on analysis and understanding of human anatomy and physiology, will allow the development of further Web-based reconstruction and predictive interfaces at different scales of the human body. The consultation tool presented herein exemplifies the potential of combining advancements in the core areas of computer science and biomedical engineering with the evolving areas of Web technologies. We are confident that future developments based on a multidisciplinary approach will further pave the way toward personalized Web-enabled medicine.