Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects

Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of 'sick-care' to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the population, and disease diversity. However, to date, efforts are fragmented, based on single-institution, size-limited, and annotation-limited datasets. Available public datasets (e.g., The Cancer Imaging Archive, TCIA, USA) are limited in scope, making model generalizability really difficult. In this direction, five European Union projects are currently working on the development of big data infrastructures that will enable European, ethically and General Data Protection Regulation-compliant, quality-controlled, cancer-related, medical imaging platforms, in which both large-scale data and AI algorithms will coexist. The vision is to create sustainable AI cloud-based platforms for the development, implementation, verification, and validation of trustable, usable, and reliable AI models for addressing specific unmet needs regarding cancer care provision. In this paper, we present an overview of the development efforts highlighting challenges and approaches selected providing valuable feedback to future attempts in the area.Key points• Artificial intelligence models for health imaging require access to large amounts of harmonized imaging data and metadata.• Main infrastructures adopted either collect centrally anonymized data or enable access to pseudonymized distributed data.• Developing a common data model for storing all relevant information is a challenge.• Trust of data providers in data sharing initiatives is essential.• An online European Union meta-tool-repository is a necessity minimizing effort duplication for the various projects in the area.

CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools

The CHAIMELEON project aims to set up a pan-European repository of health imaging data, tools and methodologies, with the ambition to set a standard and provide resources for future AI experimentation for cancer management. The project is a 4 year long, EU-funded project tackling some of the most ambitious research in the fields of biomedical imaging, artificial intelligence and cancer treatment, addressing the four types of cancer that currently have the highest prevalence worldwide: lung, breast, prostate and colorectal. To allow this, clinical partners and external collaborators will populate the repository with multimodality (MR, CT, PET/CT) imaging and related clinical data. Subsequently, AI developers will enable a multimodal analytical data engine facilitating the interpretation, extraction and exploitation of the information stored at the repository. The development and implementation of AI-powered pipelines will enable advancement towards automating data deidentification, curation, annotation, integrity securing and image harmonization. By the end of the project, the usability and performance of the repository as a tool fostering AI experimentation will be technically validated, including a validation subphase by world-class European AI developers, participating in Open Challenges to the AI Community. Upon successful validation of the repository, a set of selected AI tools will undergo early in-silico validation in observational clinical studies coordinated by leading experts in the partner hospitals. Tool performance will be assessed, including external independent validation on hallmark clinical decisions in response to some of the currently most important clinical end points in cancer. The project brings together a consortium of 18 European partners including hospitals, universities, R&D centers and private research companies, constituting an ecosystem of infrastructures, biobanks, AI/in-silico experimentation and cloud computing technologies in oncology.

Serverless Workflows for Containerised Applications in the Cloud Continuum

This paper introduces an open-source platform to support serverless computing for scientific data-processing workflow-based applications across the Cloud continuum (i.e. simultaneously involving both on-premises and public Cloud platforms to process data captured at the edge). This is achieved via dynamic resource provisioning for FaaS platforms compatible with scale-to-zero approaches that minimise resource usage and cost for dynamic workloads with different elasticity requirements. The platform combines the usage of dynamically deployed auto-scaled Kubernetes clusters on on-premises Clouds and automated Cloud bursting into AWS Lambda to achieve higher levels of elasticity. A use case in public health for smart cities is used to assess the platform, in charge of detecting people not wearing face masks from captured videos. Faces are blurred for enhanced anonymity in the on-premises Cloud and detection via Deep Learning models is performed in AWS Lambda for this data-driven containerised workflow. The results indicate that hybrid workflows across the Cloud continuum can efficiently perform local data processing for enhanced regulations compliance and perform Cloud bursting for increased levels of elasticity.

ReMindCare, an app for daily clinical practice in patients with first episode psychosis: A pragmatic real-world study protocol

AIM

Despite the potential benefits of e-health interventions for patients with psychosis, the integration of these applications into the clinical workflow and analysis of their long-term effects still face significant challenges. To address these issues, we developed the ReMindCare app. This app aims to improve the treatment quality for patients with psychosis. We chose to study the app in real world and pragmatic manner to ensure results will be generalizable.

METHODS

This is a naturalistic empirical study of patients in a first episode of psychosis programme. The app was purpose-designed based on two previous studies, and it offers the following assessments: (a) three daily questions regarding anxiety, sadness and irritability; and (b) 18 weekly questions about medication adherence, medication side effects, medication attitudes and prodromal symptoms. The app offers preset alerts, reminders and the ability for patients to reach out to their clinicians. Data captured by the app are linked to the electronic medical record of the patient. Patients will use the app as part of their ongoing care for a maximum period of 5 years, and assessments will occur at baseline and at the end of the first, second and fifth years of app use.

RESULTS

Recruitment started in October 2018 and is still ongoing.

CONCLUSIONS

The ReMindCare app represents early real-world use of digital mental health tools that offer direct integration into clinical care. High retention and compliance rates are expected, and this will in turn lead to improved quality of assessments and communication between patients and clinicians.

ReMindCare App for Early Psychosis: Pragmatic Real World Intervention and Usability Study

Background

eHealth interventions are widely used in clinical trials and increasingly in care settings as well; however, their efficacy in real-world contexts remains unknown. ReMindCare is a smartphone app that has been systematically implemented in a first episode of psychosis program (FEPP) for patients with early psychosis since 2018.

Objective

The objective of this study was to assess the efficacy of ReMindCare after 19 months of use in the clinic and varying use by individual patients.

Methods

The integration of the ReMindCare app into the FEPP started in October 2018. Patients with early psychosis self-selected to the app (ReMindCare group) or treatment as usual (TAU group). The outcome variables considered were adherence to the intervention and number of relapses, hospital admissions, and visits to urgent care units. Data from 90 patients with early psychosis were analyzed: 59 in the ReMindCare group and 31 in the TAU group. The mean age of the sample was 32.8 (SD 9.4) years, 73% (66/90) were males, 91% (83/90) were White, and 81% (74/90) were single.

Results

Significant differences between the ReMindCare and TAU groups were found in the number of relapses, hospitalizations, and visits to urgent care units, with each showing benefits for the app. Only 20% (12/59) of patients from the ReMindCare group had a relapse, while 58% (18/31) of the TAU patients had one or more relapses (χ²=13.7, P=.001). Moreover, ReMindCare patients had fewer visits to urgent care units (χ²=7.4, P=.006) and fewer hospitalizations than TAU patients (χ²=4.6, P=.03). The mean of days using the app was 352.2 (SD 191.2; min/max: 18-594), and the mean of engagement was 84.5 (SD 16.04).

Conclusions

To our knowledge, this is the first eHealth intervention that has preliminarily proven its benefits in the real-world treatment of patients with early psychosis.

International Registered Report Identifier (IRRID)

RR2-10.1111/eip.12960

PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers

PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consortium, including the European Society for Paediatric Oncology, two imaging biobanks, and three prominent European paediatric oncology units. The project is constructed as an observational in silico study involving high-quality anonymised datasets (imaging, clinical, molecular, and genetics) for the training and validation of machine learning and multiscale algorithms. The open cloud-based platform will offer precise clinical assistance for phenotyping (diagnosis), treatment allocation (prediction), and patient endpoints (prognosis), based on the use of imaging biomarkers, tumour growth simulation, advanced visualisation of confidence scores, and machine-learning approaches. The decision support prototype will be constructed and validated on two paediatric cancers: neuroblastoma and diffuse intrinsic pontine glioma. External validation will be performed on data recruited from independent collaborative centres. Final results will be available for the scientific community at the end of the project, and ready for translation to other malignant solid tumours.

Differences in the Use and Opinions About New eHealth Technologies Among Patients With Psychosis: Structured Questionnaire

BACKGROUND

Despite a growing interest in the use of technology in order to support the treatment of psychotic disorders, limited knowledge exists about the viability and acceptability of these eHealth interventions in relation to the clinical characteristics of patients.

OBJECTIVE

The objective of this study was to assess the access and use of, as well as experiences and interest in, new technologies using a survey of patients diagnosed with early psychosis compared with a survey of patients diagnosed with chronic psychotic disorders.

METHODS

We designed a structured questionnaire. This questionnaire was divided into five parts: (1) clinical and demographic information, (2) access and use of the internet, (3) use of the internet in relation to mental health, (4) experiences with technology, and (5) patients' interest in eHealth services. In total, 105 patients were recruited from early psychosis units (n=65) and recovery units (n=40).

RESULTS

In this study, 84.8% (89/105) of the patients had access to the internet and 88.6% (93/105) owned an electronic internet device. In total, 71.3% (57/80) of patients who owned a mobile phone were interested in eHealth systems and 38.2% (37/97) reported negative experiences related to the internet usage. We observed differences between the groups in terms of device ownership (P=.02), the frequency of internet access (P<.001), the use of social media (P=.01), and seeking health information (P=.04); the differences were found to be higher in the early psychosis group. No differences were found between the groups in terms of the use of internet in relation to mental health, experiences and opinions about the internet, or interest in eHealth interventions (P=.43).

CONCLUSIONS

The availability and use of technology for the participants in our survey were equivalent to those for the general population. The differences found between the groups in relation to the access or use of technology seemed to due to age-related factors. The use of technology involving mental health and the interest in eHealth interventions were mainly positive and equivalent between the groups. Accordingly, this group of patients is a potential target for the emerging eHealth interventions, regardless of their clinical status. However, 28.7% (23/80) of the studied patients rejected the use of internet interventions and 38.2% (37/97) had unpleasant experiences related to its usage; thus, more in-depth studies are needed to better define the profile of patients with psychosis who may benefit from eHealth treatments.

The Grid as a Healthcare Provision Tool

Objectives: This paper presents a survey on HealthGrid technologies, describing the current status of Grid and eHealth and analyzing them in the medium-term future. The objective is to analyze the key points, barriers and driving forces for the take-up of HealthGrids.

Methods: The article considers the procedures from other Grid disciplines such as high energy physics or biomolecular engineering and discusses the differences with respect to healthcare. It analyzes the status of the basic technology, the needs of the eHealth environment and the successes of current projects in health and other relevant disciplines.

Results: Information and communication technology (ICT) in healthcare is a promising area for the use of the Grid. There are many driving forces that are fostering the application of the secure, pervasive, ubiquitous and transparent access to information and computing resources that Grid technologies can provide. However, there are many barriers that must be solved. Many technical problems that arise in eHealth (standardization of data, federation of databases, content-based knowledge extraction, and management of personal data …) can be solved with Grid technologies.

Conclusions: The article presents the development of successful and demonstrative applications as the key for the take-up of HealthGrids, where short-term future medical applications will surely be biocomputing-oriented, and the future of Grid technologies on medical imaging seems promising. Finally, exploitation of HealthGrid is analyzed considering the curve of the adoption of ICT solutions and the definition of business models, which are far more complex than in other e-business technologies such ASP.

Increasing the Efficiency on Producing Radiology Reports for Breast Cancer Diagnosis by Means of Structured Reports

Background: Radiology reports are commonly written on free-text using voice recognition devices. Structured reports (SR) have a high potential but they are usually considered more difficult to fill-in so their adoption in clinical practice leads to a lower efficiency. However, some studies have demonstrated that in some cases, producing SRs may require shorter time than plain-text ones. This work focuses on the definition and demonstration of a methodology to evaluate the productivity of software tools for producing radiology reports. A set of SRs for breast cancer diagnosis based on BI-RADS have been developed using this method. An analysis of their efficiency with respect to free-text reports has been performed.

Material and Methods: The methodology proposed compares the Elapsed Time (ET) on a set of radiological reports. Free-text reports are produced with the speech recognition devices used in the clinical practice. Structured reports are generated using a web application generated with TRENCADIS framework. A team of six radiologists with three different levels of experience in the breast cancer diagnosis was recruited. These radiologists performed the evaluation, each one introducing 50 reports for mammography, 50 for ultrasound scan and 50 for MRI using both approaches. Also, the Relative Efficiency (REF) was computed for each report, dividing the ET of both methods. We applied the T-Student (T-S) test to compare the ETs and the ANOVA test to compare the REFs. Both tests were computed using the SPSS software.

Results: The study produced three DICOM- SR templates for Breast Cancer Diagnosis on mammography, ultrasound and MRI, using RADLEX terms based on BIRADs 5th edition. The T-S test on radiologists with high or intermediate profile, showed that the difference between the ET was only statistically significant for mammography and ultrasound. The ANOVA test performed grouping the REF by modalities, indicated that there were no significant differences between mammograms and ultrasound scans, but both have significant statistical differences with MRI. The ANOVA test of the REF for each modality, indicated that there were only significant differences in Mammography (ANOVA p = 0.024) and Ultrasound (ANOVA p = 0.008). The ANOVA test for each radiologist profile, indicated that there were significant differences on the high profile (ANOVA p = 0.028) and medium (ANOVA p=0.045).

Conclusions: In this work, we have defined and demonstrated a methodology to evaluate the productivity of software tools for producing radiology reports in Breast Cancer. We have evaluated that adopting Structured Reporting in mammography and ultrasound studies in breast cancer diagnosis improves the performance in producing reports.

Use of mobile technologies in patients with psychosis: A systematic review

There is a growing interest in mobile Health interventions (m-Health) in patients with psychosis. The aim of this study is to conduct a systematic review in order to analysethe current state of research in this area. The search of articles was carried out following the PRISMA criteria, focusing on those studies that used mobile technologies in patients with psychosis during the period from 1990 to 2016. A total of 20 articles were selected from the 431 studies found. Three types of studies are distinguished: 1) Analysis of quality and usability, 2) Improving treatment adherence and reducing hospital admissions, and 3) Analysisof patient symptoms.

CONCLUSIONS

m-Health interventions are feasible, and are easy to use for patients with psychosis. They evaluate the evolution of psychotic symptoms more efficiently, and improve adherence to treatment, as well as symptoms and hospital admissions. However, a particular strategy does not stand out over the rest, because differences in methodology make them difficult to compare.

MuffinInfo: HTML5-Based Statistics Extractor from Next-Generation Sequencing Data

Usually, the information known a priori about a newly sequenced organism is limited. Even resequencing the same organism can generate unpredictable output. We introduce MuffinInfo, a FastQ/Fasta/SAM information extractor implemented in HTML5 capable of offering insights into next-generation sequencing (NGS) data. Our new tool can run on any software or hardware environment, in command line or graphically, and in browser or standalone. It presents information such as average length, base distribution, quality scores distribution, k-mer histogram, and homopolymers analysis. MuffinInfo improves upon the existing extractors by adding the ability to save and then reload the results obtained after a run as a navigable file (also supporting saving pictures of the charts), by supporting custom statistics implemented by the user, and by offering user-adjustable parameters involved in the processing, all in one software. At the moment, the extractor works with all base space technologies such as Illumina, Roche, Ion Torrent, Pacific Biosciences, and Oxford Nanopore. Owing to HTML5, our software demonstrates the readiness of web technologies for mild intensive tasks encountered in bioinformatics.

OMWS: A Web Service Interface for Ecological Niche Modelling

Ecological niche modelling (ENM) experiments often involve a high number of tasks to be performed. Such tasks may consume a significant amount of computing resources and take a long time to complete, especially when using personal computers. OMWS is a Web service interface that allows more powerful computing back-ends to be remotely exploited by other applications to carry out ENM tasks. Its latest version includes a new operation that can be used to specify complex workflows in a single request, adding the possibility of using workflow management systems on parallel computing back-end. In this paper we describe the OMWS protocol and compare its most recent version with the previous one by running the same ENM experiment using two functionally equivalent clients, each designed for one of the OMWS interface versions. Different back-end configurations were used to investigate how the performance scales for each protocol version when more processing power is made available. Results show that the new version outperforms (in a factor of 2) the previous one when more computing resources are used.

Fast inexact mapping using advanced tree exploration on backward search methods

BACKGROUND

Short sequence mapping methods for Next Generation Sequencing consist on a combination of seeding techniques followed by local alignment based on dynamic programming approaches. Most seeding algorithms are based on backward search alignment, using the Burrows Wheeler Transform, the Ferragina and Manzini Index or Suffix Arrays. All these backward search algorithms have excellent performance, but their computational cost highly increases when allowing errors. In this paper, we discuss an inexact mapping algorithm based on pruning strategies for search tree exploration over genomic data.

RESULTS

The proposed algorithm achieves a 13x speed-up over similar algorithms when allowing 6 base errors, including insertions, deletions and mismatches. This algorithm can deal with 400 bps reads with up to 9 errors in a high quality Illumina dataset. In this example, the algorithm works as a preprocessor that reduces by 55% the number of reads to be aligned. Depending on the aligner the overall execution time is reduced between 20-40%.

CONCLUSIONS

Although not intended as a complete sequence mapping tool, the proposed algorithm could be used as a preprocessing step to modern sequence mappers. This step significantly reduces the number reads to be aligned, accelerating overall alignment time. Furthermore, this algorithm could be used for accelerating the seeding step of already available sequence mappers. In addition, an out-of-core index has been implemented for working with large genomes on systems without expensive memory configurations.

NeuPAT: an intranet database supporting translational research in neuroblastic tumors

Translational research in oncology is directed mainly towards establishing a better risk stratification and searching for appropriate therapeutic targets. This research generates a tremendous amount of complex clinical and biological data needing speedy and effective management. The authors describe the design, implementation and early experiences of a computer-aided system for the integration and management of data for neuroblastoma patients. NeuPAT facilitates clinical and translational research, minimizes the workload in consolidating the information, reduces errors and increases correlation of data through extensive coding. This design can also be applied to other tumor types.

A service-based BLAST command tool supported by cloud infrastructures

Notwithstanding the benefits of distributed-computing infrastructures for empowering bioinformatics analysis tools with the needed computing and storage capability, the actual use of these infrastructures is still low. Learning curves and deployment difficulties have reduced the impact on the wide research community. This article presents a porting strategy of BLAST based on a multiplatform client and a service that provides the same interface as sequential BLAST, thus reducing learning curve and with minimal impact on their integration on existing workflows. The porting has been done using the execution and data access components from the EC project Venus-C and the Windows Azure infrastructure provided in this project. The results obtained demonstrate a low overhead on the global execution framework and reasonable speed-up and cost-efficiency with respect to a sequential version.

Integrating TRENCADIS components in gLite to share DICOM medical images and structured reports

The problem of sharing medical information among different centres has been tackled by many projects. Several of them target the specific problem of sharing DICOM images and structured reports (DICOM-SR), such as the TRENCADIS project. In this paper we propose sharing and organizing DICOM data and DICOM-SR metadata benefiting from the existent deployed Grid infrastructures compliant with gLite such as EGEE or the Spanish NGI. These infrastructures contribute with a large amount of storage resources for creating knowledge databases and also provide metadata storage resources (such as AMGA) to semantically organize reports in a tree-structure. First, in this paper, we present the extension of TRENCADIS architecture to use gLite components (LFC, AMGA, SE) on the shake of increasing interoperability. Using the metadata from DICOM-SR, and maintaining its tree structure, enables federating different but compatible diagnostic structures and simplifies the definition of complex queries. This article describes how to do this in AMGA and it shows an approach to efficiently code radiology reports to enable the multi-centre federation of data resources.

SHARE road map for HealthGrids: methodology

The SHARE(1) project (http://www.eu-share.org) was asked to identify the key developments needed to achieve wide adoption and deployment of HealthGrids throughout Europe. The project was asked to organise these as milestones on a road map, so that all technical advances, social actions, economic investments and ethical or legal initiatives necessary for HealthGrids would be seen together in a single coherent document. The full road map includes an extensive analysis of several case studies exploring their technical requirements, full discussion of the ethical, legal, social and economic issues which may impede early deployment, and concludes with an attempt to reconcile the tensions between technological developments and regulatory frameworks. This paper has been restricted to the technical aspects of the project. SHARE built on the work of the 'HealthGrid' initiative so we begin by, reviewing work carried out in various European HealthGrid projects and report on joint work with numerous European collaborators. Following many successful HealthGrid projects, HealthGrid published a 'White Paper' which establishes the foundations, potential scope and prospects of an approach to health informatics based on a grid infrastructure. The White Paper demonstrates the ways in which the HealthGrid approach supports many modern trends in medicine and healthcare, such as evidence-based practice, integration across levels, from molecules and cells, through tissues and organs to the whole person and community, and the promise of individualised healthcare. SHARE was funded by the European Commission to define a research roadmap for a 'HealthGrid for Europe', to be seen as the preferred infrastructure for biomedical and healthcare projects in the European Research Area.

Using grid-enabled distributed metadata database to index DICOM-SR

Integrating medical data at inter-centre level implies many challenges that are being tackled from many disciplines and technologies. Medical informatics have applied an important effort on describing and standardizing Electronic Health Records, and specially standardisation has achieved an important extent on Medical Imaging. Grid technologies have been extensively used to deal with multi-domain authorisation issues and to provide single access points for accessing DICOM Medical Images, enabling the access and processing to large repositories of data. However, this approach introduces the challenge of efficiently organising data according to their relevance and interest, in which the medical report is a key factor. The present work shows an approach to efficiently code radiology reports to enable the multi-centre federation of data resources. This approach follows the tree-like structure of DICOM-SR reports in a self-organising metadata catalogue based on AMGA. This approach enables federating different but compatible distributed repositories, automatically reconfiguring the database structure, and preserving the autonomy of each centre in defining the template. Tools developed so far and some performance results are provided to prove the effectiveness of the approach.

Metagenomics reveals our incomplete knowledge of global diversity

Contact: Javier.tamames@uv.es

A highly optimized grid deployment: the metagenomic analysis example

Computational resources and computationally expensive processes are two topics that are not growing at the same ratio. The availability of large amounts of computing resources in Grid infrastructures does not mean that efficiency is not an important issue. It is necessary to analyze the whole process to improve partitioning and submission schemas, especially in the most critical experiments. This is the case of metagenomic analysis, and this text shows the work done in order to optimize a Grid deployment, which has led to a reduction of the response time and the failure rates. Metagenomic studies aim at processing samples of multiple specimens to extract the genes and proteins that belong to the different species. In many cases, the sequencing of the DNA of many microorganisms is hindered by the impossibility of growing significant samples of isolated specimens. Many bacteria cannot survive alone, and require the interaction with other organisms. In such cases, the information of the DNA available belongs to different kinds of organisms. One important stage in Metagenomic analysis consists on the extraction of fragments followed by the comparison and analysis of their function stage. By the comparison to existing chains, whose function is well known, fragments can be classified. This process is computationally intensive and requires of several iterations of alignment and phylogeny classification steps. Source samples reach several millions of sequences, which could reach up to thousands of nucleotides each. These sequences are compared to a selected part of the "Non-redundant" database which only implies the information from eukaryotic species. From this first analysis, a refining process is performed and alignment analysis is restarted from the results. This process implies several CPU years. The article describes and analyzes the difficulties to fragment, automate and check the above operations in current Grid production environments. This environment has been tuned-up from an experimental study which has tested the most efficient and reliable resources, the optimal job size, and the data transference and database reindexation overhead. The environment should re-submit faulty jobs, detect endless tasks and ensure that the results are correctly retrieved and workflow synchronised. The paper will give an outline on the structure of the system, and the preparation steps performed to deal with this experiment.

The SHARE road map: Healthgrids for biomedical research and healthcare

The HealthGrid White Paper was published at the third annual conference in Oxford in 2005. Starting from the conclusions of the White Paper, the EU funded SHARE project (http://www.eu-share.org) has aimed at identifying the most important steps and significant milestones towards wide deployment and adoption of healthgrids in Europe. The project has defined a strategy to address the issues identified in the action plan for European e-Health (COM(2004).356) and has devised a roadmap for the major technological and ethical and legal developments and social and economic investments needed for successful take up of healthgrids in the next 10 years. A "beta" version of the road map underwent full review by a panel of 25 prominent European experts at a workshop in December 2007. The present document is an executive policy summary of the final draft road map. It has sought to reconcile likely conflicts between technological developments and regulatory frameworks by bringing together the project's technical road map and conceptual map of ethical and legal issues and socio-economic prospects. A key tool in this process was a collection of case studies of healthgrid applications.

SHARE, from vision to road map: technical steps

We present the 'HealthGrid' initiative and briefly review work carried out in various European healthgrid projects. We report on joint work with numerous European collaborators. Since the European Commission's Information Society Technologies programme funded the first gridbased health and medical projects, the HealthGrid movement has flourished in Europe. Many projects have now been completed and 'HealthGrid' consulted a number of experts to compile and publish a 'White Paper' which establishes the foundations, potential scope and prospects of an approach to health informatics based on a grid infrastructure. With a second generation of projects now funded, the EC has commissioned the SHARE Project, a study to define a research roadmap for a 'healthgrid for Europe' as the preferred infrastructure for medical and health care projects in the European Research Area. The project explores the ways in which the healthgrid approach supports modern trends both in research in biomedicine and in healthcare, such as evidence-based practice and information integration.

TRENCADIS - secure architecture to share and manage DICOM objects in a ontological framework based on OGSA

Today most European healthcare centers use the digital format for their databases of images. TRENCADIS is a software architecture comprising a set of services as a solution for interconnecting, managing and sharing selected parts of medical DICOM data for the development of training and decision support tools. The organization of the distributed information in virtual repositories is based on semantic criteria. Different groups of researchers could organize themselves to propose a Virtual Organization (VO). These VOs will be interested in specific target areas, and will share information concerning each area. Although the private part of the information to be shared will be removed, special considerations will be taken into account to avoid the access by non-authorized users. This paper describes the security model implemented as part of TRENCADIS. The paper is organized as follows. First introduces the problem and presents our motivations. Section 1 defines the objectives. Section 2 presents an overview of the existing proposals per objective. Section 3 outlines the overall architecture. Section 4 describes how TRENCADIS is architected to realize the security goals discussed in the previous sections. The different security services and components of the infrastructure are briefly explained, as well as the exposed interfaces. Finally, Section 5 concludes and gives some remarks on our future work.

Data integration in eHealth: a domain/disease specific roadmap

The paper documents a series of data integration workshops held in 2006 at the UK National e-Science Centre, summarizing a range of the problem/solution scenarios in multi-site and multi-scale data integration with six HealthGrid projects using schizophrenia as a domain-specific test case. It outlines emerging strategies, recommendations and objectives for collaboration on shared ontology-building and harmonization of data for multi-site trials in this domain.

Advances in the biomedical applications of the EELA Project

In the last years an increasing demand for Grid Infrastructures has resulted in several international collaborations. This is the case of the EELA Project, which has brought together collaborating groups of Latin America and Europe. One year ago we presented this e-infrastructure used, among others, by the biomedical groups for the studies of oncological analysis, neglected diseases, sequence alignments and computational phylogenetics. After this period, the achieved advances are summarised in this paper.

SHARE roadmap 1: towards a debate

We present the 'HealthGrid' initiative and review work carried out in various European projects. Since the European Commission's Information Society Technologies programme funded the first grid-based health and medical projects, the HealthGrid movement has flourished in Europe. Many projects have now been completed and 'Healthgrid' consulted a number of experts to compile and publish a 'White Paper' which establishes the foundations, potential scope and prospects of an approach to health informatics based on a grid infrastructure. The White Paper demonstrates the ways in which the healthgrid approach supports many modern trends in medicine and healthcare, such as evidence-based practice, integration across levels, from molecules and cells, through tissues and organs to the whole person and community, and the promise of individualized health care. A second generation of projects have now been funded, and the EC has commissioned a study to define a research roadmap for a 'healthgrid for Europe', seen as the preferred infrastructure for medical and health care projects in the European Research Area.

Roadmap for a European healthgrid

This paper proposes a 10-year roadmap to achieve the goal to offer to healthcare professionals an environment created through the sharing of resources, in which heterogeneous and dispersed health data as well as applications can be accessed by all users as a tailored information providing system according to their authorisation and without loss of information. The paper identifies milestones and presents short term objectives on the road to this healthgrid.

TRENCADIS--a WSRF grid MiddleWare for managing DICOM structured reporting objects

The adoption of the digital processing of medical data, especially on radiology, has leaded to the availability of millions of records (images and reports). However, this information is mainly used at patient level, being the extraction of information, organised according to administrative criteria, which make the extraction of knowledge difficult. Moreover, legal constraints make the direct integration of information systems complex or even impossible. On the other side, the widespread of the DICOM format has leaded to the inclusion of other information different from just radiological images. The possibility of coding radiology reports in a structured form, adding semantic information about the data contained in the DICOM objects, eases the process of structuring images according to content. DICOM Structured Reporting (DICOM-SR) is a specification of tags and sections to code and integrate radiology reports, with seamless references to findings and regions of interests of the associated images, movies, waveforms, signals, etc. The work presented in this paper aims at developing of a framework to efficiently and securely share medical images and radiology reports, as well as to provide high throughput processing services. This system is based on a previously developed architecture in the framework of the TRENCADIS project, and uses other components such as the security system and the Grid processing service developed in previous activities. The work presented here introduces a semantic structuring and an ontology framework, to organise medical images considering standard terminology and disease coding formats (SNOMED, ICD9, LOINC..).

Proposing a roadmap for HealthGrids

With the regular progress of technology and infrastructures, a growing number of grid applications are developed and deployed for life science and medical research. At the last HealthGrid conference in April 2005 in Oxford, many groups described successful usage of grids for compute intensive calculations. Very large scale deployment of a biomedical application in the area of drug discovery has been achieved on EGEE during 2005. On the other hand, beside a few pioneers, very few data grids have been deployed so far and knowledge grids are still at a conceptual level. This situation is expected to evolve quickly as many projects are focussed on developing data management services and knowledge management tools relevant to biomedical sciences. At this stage, it is important to identify the potential bottlenecks and to define a roadmap for the wide adoption of grids for healthcare. This article presents an analysis of the present adoption of grids for biomedical sciences and healthcare in Europe: it identifies bottlenecks and proposes actions that will be further assessed within the framework of the SHARE European project dedicated to the definition of a roadmap for HealthGrids.

Blast2GO goes grid: developing a grid-enabled prototype for functional genomics analysis

The vast amount in complexity of data generated in Genomic Research implies that new dedicated and powerful computational tools need to be developed to meet their analysis requirements. Blast2GO (B2G) is a bioinformatics tool for Gene Ontology-based DNA or protein sequence annotation and function-based data mining. The application has been developed with the aim of affering an easy-to-use tool for functional genomics research. Typical B2G users are middle size genomics labs carrying out sequencing, ETS and microarray projects, handling datasets up to several thousand sequences. In the current version of B2G. The power and analytical potential of both annotation and function data-mining is somehow restricted to the computational power behind each particular installation. In order to be able to offer the possibility of an enhanced computational capacity within this bioinformatics application, a Grid component is being developed. A prototype has been conceived for the particular problem of speeding up the Blast searches to obtain fast results for large datasets. Many efforts have been done in the literature concerning the speeding up of Blast searches, but few of them deal with the use of large heterogeneous production Grid Infrastructures. These are the infrastructures that could reach the largest number of resources and the best load balancing for data access. The Grid Service under development will analyse requests based on the number of sequences, splitting them accordingly to the available resources. Lower-level computation will be performed through MPIBLAST. The software architecture is based on the WSRF standard.

Biomedical applications in EELA

The current demand for Grid Infrastructures to bring collabarating groups between Latina America and Europe has created the EELA proyect. This e-infrastructure is used by Biomedical groups in Latina America and Europe for the studies of ocnological analisis, neglected diseases, sequence alignments and computation plygonetics.

Using the grid to analyze the pharmacokinetic modelling after contrast administration in dynamic MRI

The analysis of the angiogenesis in hepatic lesions is an important marker of tumour aggressiveness and response to therapy. However, the quantitative analysis of this fact requires a deep knowledge of the hepatic perfusion. The development of pharmacokinetic models constitutes a very valuable tool, but it is computationally intensive. Moreover, abdominal imaging processing increases the computational requirements since the movement of the patient makes images in a time series incomparable, requiring a previous pre-processing. This work presents a Grid environment developed to deal with the computational demand of pharmacokinetic modelling. This article proposes and implements a four-level software architecture that provides a simple interface to the user and deals transparently with the complexity of Grid environment. The four layers implemented are: Grid Layer (the closest to the Grid infrastructure), the Gate-to- Grid (which transforms the user requests to Grid operations), the Web Services layer (which provides a simple, standard and ubiquitous interface to the user) and the Application Layer. An application has been developed on top of this architecture to manage the execution of multi-parametric groups of co-registration actions on a large set of medical images. The execution has been performed on the EGEE Grid infrastructure. The application is platform-independent and can be used from any computer without special requirements.

Privacy protection in HealthGrid: distributing encryption management over the VO

Grid technologies have proven to be very successful in tackling challenging problems in which data access and processing is a bottleneck. Notwithstanding the benefits that Grid technologies could have in Health applications, privacy leakages of current DataGrid technologies due to the sharing of data in VOs and the use of remote resources, compromise its widespreading. Privacy control for Grid technology has become a key requirement for the adoption of Grids in the Healthcare sector. Encrypted storage of confidential data effectively reduces the risk of disclosure. A self-enforcing scheme for encrypted data storage can be achieved by combining Grid security systems with distributed key management and classical cryptography techniques. Virtual Organizations, as the main unit of user management in Grid, can provide a way to organize key sharing, access control lists and secure encryption management. This paper provides programming models and discusses the value, costs and behavior of such a system implemented on top of one of the latest Grid middlewares. This work is partially funded by the Spanish Ministry of Science and Technology in the frame of the project Investigación y Desarrollo de Servicios GRID: Aplicación a Modelos Cliente-Servidor, Colaborativos y de Alta Productividad, with reference TIC2003-01318.

Clinical Decision Support Systems (CDSS) in GRID Environments

This paper presents an architecture defined for searching and executing Clinical Decision Support Systems (CDSS) in a LCG2/GT2 Grid environment, using web-based protocols. A CDSS is a system that provides a classification of the patient illness according to the knowledge extracted from clinical practice and using the patient's information in a structured format. The CDSS classification engines can be installed in any site and can be used by different medical users from a Virtual Organization (VO). All users in a VO can consult and execute different classification engines that have been installed in the Grid independently of the platform, architecture or site where the engines are installed or the users are located. The present paper present a solution to requirements such as short-job execution, reducing the response delay on LCG2 environments and providing grid-enabled authenticated access through web portals. Resource discovering and job submission is performed through web services, which are also described in the article.

The Healthgrid White Paper

Over the last four years, a community of researchers working on Grid and High Performance Computing technologies started discussing the barriers and opportunities that grid technologies must face and exploit for the development of health-related applications. This interest lead to the first Healthgrid conference, held in Lyon, France, on January 16th-17th, 2003, with the focus of creating increased awareness about the possibilities and advantages linked to the deployment of grid technologies in health, ultimately targeting the creation of a European/international grid infrastructure for health. The topics of this conference converged with the position of the eHealth division of the European Commission, whose mandate from the Lisbon Meeting was "To develop an intelligent environment that enables ubiquitous management of citizens' health status, and to assist health professionals in coping with some major challenges, risk management and the integration into clinical practice of advances in health knowledge." In this context "Health" involves not only clinical procedures but covers the whole range of information from molecular level (genetic and proteomic information) over cells and tissues, to the individual and finally the population level (social healthcare). Grid technology offers the opportunity to create a common working backbone for all different members of this large "health family" and will hopefully lead to an increased awareness and interoperability among disciplines. The first HealthGrid conference led to the creation of the Healthgrid association, a non-profit research association legally incorporated in France but formed from the broad community of European researchers and institutions sharing expertise in health grids. After the second Healthgrid conference, held in Clermont-Ferrand on January 29th-30th, 2004, the need for a "white paper" on the current status and prospective of health grids was raised. Over fifty experts from different areas of grid technologies, eHealth applications and the medical world were invited to contribute to the preparation of this document.

Large Medical Datasets on the Grid

Objective: This paper shows the use of the emerging Grid technology for gathering underused resources that are distributed among a corporate network. The work of these resources is coordinated for facing tasks which are not affordable by the individual usage of each of them.

Methods: This paper shows an application for the projection, using Volume Rendering techniques, of huge medical volumes obtained from CTs and RMIs, adapted to Grid computing.

Results: As a result the article shows the feasibility of the creation of an application based up on Grid technology, which solves problems that cannot be addressed by using common techniques. As an example, the article describes the projection of a huge medical dataset, which exceeds the resources of most common PCs, carried out by taking profit of idle CPU cycles from the computers of an organization.

Conclusions: Grid technology is emerging as a new framework which allows gathering and coordinating resources distributed among a network (LAN or WAN), for addressing problems which cannot be solved through the single use of any of these resources. Medical Imaging is a clear application area for this technology.

The Grid as a healthcare provision tool

OBJECTIVES

This paper presents a survey on HealthGrid technologies, describing the current status of Grid and eHealth and analyzing them in the medium-term future. The objective is to analyze the key points, barriers and driving forces for the take-up of HealthGrids.

METHODS

The article considers the procedures from other Grid disciplines such as high energy physics or biomolecular engineering and discusses the differences with respect to healthcare. It analyzes the status of the basic technology, the needs of the eHealth environment and the successes of current projects in health and other relevant disciplines.

RESULTS

Information and communication technology (ICT) in healthcare is a promising area for the use of the Grid. There are many driving forces that are fostering the application of the secure, pervasive, ubiquitous and transparent access to information and computing resources that Grid technologies can provide. However, there are many barriers that must be solved. Many technical problems that arise in eHealth (standardization of data, federation of databases, content-based knowledge extraction, and management of personal data ...) can be solved with Grid technologies.

CONCLUSIONS

The article presents the development of successful and demonstrative applications as the key for the take-up of HealthGrids, where short-term future medical applications will surely be biocomputing-oriented, and the future of Grid technologies on medical imaging seems promising. Finally, exploitation of HealthGrid is analyzed considering the curve of the adoption of ICT solutions and the definition of business models, which are far more complex than in other e-business technologies such ASP.

Large medical datasets on the Grid

OBJECTIVE

This paper shows the use of the emerging Grid technology for gathering underused resources that are distributed among a corporate network. The work of these resources is coordinated for facing tasks which are not affordable by the individual usage of each of them.

METHODS

This paper shows an application for the projection, using Volume Rendering techniques, of huge medical volumes obtained from CTs and RMIs, adapted to Grid computing.

RESULTS

As a result the article shows the feasibility of the creation of an application based up on Grid technology, which solves problems that cannot be addressed by using common techniques. As an example, the article describes the projection of a huge medical dataset, which exceeds the resources of most common PCs, carried out by taking profit of idle CPU cycles from the computers of an organization.

CONCLUSIONS

Grid technology is emerging as a new framework which allows gathering and coordinating resources distributed among a network (LAN or WAN), for addressing problems which cannot be solved through the single use of any of these resources. Medical Imaging is a clear application area for this technology.

Parallel segmentation and rendering using clusters of PCs

Clinics have to deal currently with hundreds of 3D images a day. 3D Medical Images contain a huge amount of data, and thus, very expensive and powerful systems are required in order to process them. The present work shows the features of a software parallel computing package developed at the Universidad Politécnica de Valencia, under the European Project HIPERCIR. http:¿hiperttn.upv.es/hipercir. Project HIPERCIR is aimed at reducing the time and requirements for processing and visualising 3D images with low-cost solutions, such as networks of PCs running standard operating systems (Windows 95/98/NT). This project is being developed by a consortium formed by medical image processing and parallel computing experts from the Universidad Politécnica de Valencia (UPV), experts on biomedical software and radiology clinic experts.