ERCIM News 118
July 2019
Special theme Digital Healthy
Guest editors Sara Colantonio (ISTI-CNR) and Nicholas Ayache (Inria)
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by Sara Colantonio (ISTI-CNR) and Nicholas Ayache (Inria)
This special theme focuses on research advances and perspectives in digital health, offering a glimpse into the diverse range of topics and challenges that the research community is facing in the field.
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by Anne-Laure Rousseau (Assistance Publique - Hôpitaux de Paris)
Machine learning has made a remarkable entry into the world of healthcare, but there remain some concerns about this technology. According to journalists, a revolution is upon us: One day the first artificial intelligence robot receives its medical degree, the next, new algorithms have surpassed the skill of medical experts. It seems that any day now, medical doctors will be unemployed, superseded by the younger siblings of Siri. But having worked on both medical imaging and machine learning, I think the reality is different, and, at the risk of disappointing some physicians’ colleagues who thought the holidays were close, there is still work for us doctors for many decades. The new technology, in fact, offers a great opportunity to enhance health services worldwide, if doctors and engineers collaborate better together.
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by Fanny Orlhac, Charles Bouveyron and Nicholas Ayache (Université Côte d’Azur, Inria)
Radiomics is the automatic extraction of numerous quantitative features from medical images and using these features to build, for instance, predictive models. It is anticipated that Radiomics enhanced by AI techniques will play a major role in patient management in a clinical setting. To illustrate developments in this field, we briefly present two ongoing projects in oncology.
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by Olivier Colliot (CNRS)
In artificial intelligence for medicine, more interpretable and reliable systems are needed. Here, we report on recent advances toward these aims in the field of brain diseases.
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by Maxime Sermesant (Inria and Université Côte d’Azur)
With medical imaging’s ability to provide a high level of detail about cardiac anatomy and pathology, it is high time for such information to be used during interventions. Technology to achieve this is now being made available to every cardiologist.
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by Gina Belmonte (AOUS), Vincenzo Ciancia (ISTI-CNR), Diego Latella (ISTI-CNR) and Mieke Massink (ISTI-CNR)
Glioblastomas are among the most common malignant intracranial tumours. Neuroimaging protocols are used before and after treatment to evaluate its effect and to monitor the evolution of the disease. In clinical studies and routine treatment, magnetic resonance images (MRI) are evaluated, largely manually, and based on qualitative criteria such as the presence of hyper-intense tissue in the image. VoxLogicA is an image analysis tool, designed to perform tasks such as identifying brain tumours in 3D magneto-resonance scans. The aim is to have a system that is portable, predictable and reproducible, and requires minimal computing expertise to operate.
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by Alessia Amelio (University of Calabria, Italy), Lucio Amelio (independant researcher) and Radmila Janković (Mathematical Institute of the S.A.S.A., Serbia)
New study directions are focused on the extraction and recognition of visual patterns from different types of medical images.
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by Sara Colantonio (ISTI-CNR), Andrea Barucci (IFAC-CNR) and Danila Germanese (ISTI-CNR)
Precision health, the future of patient care, is dependent on artificial intelligence. Of the information contained in a digital medical image, visual analysis can only extract about 10%. Radiomics aims to extract an enormous wealth of quantitative data from biomedical images, which could not be processed through simple visual analysis, but is capable of providing more information on the underlying pathophysiological phenomena and biological processes within human body. The subsequent mining of these quantitative data can offer very useful information on the aggressiveness of the disease under investigation, opening at the tailoring of the therapies based on a patient’s needs and at the monitoring of the response to care. Therefore, by using specific mathematical algorithms and artificial intelligence techniques, radiomics provides very powerful support for precision medicine, especially in oncology.
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by Francesca Lizzi (National Institute for Nuclear Physics, Scuola Normale Superiore, National Research Council, University of Pisa), Maria Evelina Fantacci (National Institute for Nuclear Physics, University of Pisa) and P. Oliva (National Institute for Nuclear Physics, University of Sassari)
Breast cancer is the most commonly diagnosed cancer among women worldwide. Survival rates strongly depend on early diagnosis, and for this reason mammographic screening is performed in developed countries. New artificial intelligence-based techniques have the potential to include and quantify fibroglandular (or dense) parenchyma in breast cancer risk models.
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by Andrea Manno-Kovacs (MTA SZTAKI / PPKE ITK), Csaba Benedek (MTA SZTAKI) and Levente Kovács (MTA SZTAKI)
Novel 3D sensors and augmented reality-based visualisation technology are being integrated for innovative healthcare applications to improve the diagnostic process, strengthen the doctor-patient relationship and open new horizons in medical education. Our aim is to help doctors and patients explain and visualise medical status using computer vision and augmented reality.
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by Marleen Balvert and Alexander Schoenhuth (CWI)
Many diseases that we cannot currently cure, such as cancer, Alzheimer’s and amyotrophic lateral sclerosis (ALS), are caused by variations in the DNA sequence. It is often unknown which characteristics caused the disease. Knowing these would greatly help our understanding of the underlying disease mechanisms, and would boost drug development. At CWI we develop methods based on artificial intelligence (AI) to help find the genetic causes of disease, with promising first results.
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by Alexander Schönhuth (CWI and Utrecht University) and Leen Stougie (CWI and VU Amsterdam)
Many life-threatening viruses populate their hosts with a cocktail of different strains, which may mutate insanely fast, protecting the virus from human immune response or medical treatment. Researchers at CWI have designed a method, named Virus-VariationGraph (Virus-VG) [3], that puts all strains onto a graphical map, which facilitates more reliable and convenient identification of potentially resistance-inducing or particularly lethal strains.
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by Anna Fomitcheva Khartchenko (ETH Zurich, IBM Research – Zurich), Aditya Kashyap and Govind V. Kaigala (IBM Research – Zurich)
The role of a pathologist is critical to the cancer diagnosis workflow: they need to understand patient pathology and provide clinicians with insights through result interpretation. To do so, pathologists and their laboratory teams perform various investigations (assays) on a biopsy tissue. One of the most common tests is immunohistochemistry (IHC), which probes the expression levels for certain proteins that characterise the tissue, called biomarkers. This test enables sub-classification of the disease and is critical for the selection of a treatment modality. However, the number of biomarkers is constantly increasing, while the size of the biopsy is reducing due to early testing and more sensitive methods.
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by Matteo Manica, Ali Oskooei, and Jannis Born (IBM Research)
Accelerating anticancer drug discovery is pivotal in improving therapies and patient prognosis in cancer medicine. Over the years, in-silico screening has greatly helped enhance the efficiency of the drug discovery process. Despite the advances in the field, there remains a need for explainable predictive models that can shed light onto the anticancer drug sensitivity problem. A team of scientists at the Computational Systems Biology group within IBM Research has now proposed a novel AI approach to bridge this gap.
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by Christophe Ponsard and Renaud De Landtsheer (CETIC)
Within a medical setting, clinical pathways enable efficient organisation of care processes, which benefit both the patient and hospital management. Digital health analytics plays a critical role in the successful deployment of clinical pathways. Two key aspects learned from our experience are the engineering of accurate workflow models and the use of online schedule optimisation, enforcing both care and resource constraints.
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by Mark van Gils (VTT)
Decision making for complex diseases requires efficient consideration of information from a large variety of very different data sources. We developed a machine-learning based decision support system for dementia diagnosis and evaluated it within four hospitals in Europe.
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by Fulvio Patara and Enrico Vicario (University of Florence)
The RACE project (Research on Evidence-based Appropriateness in Cardiology) exploits innovative infrastructures and integrated software services with the aim of “providing the right care, to the right subject, at the right time, by the right provider, in the right health facility”.
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by Kari Antila (VTT), Niku Oksala (Tampere University Hospital) and Jussi A. Hernesniemi (Tampere University)
We set out to find out if models developed with a hospital’s own data beat a current state-of-the art risk predictor for mortality in acute coronary syndrome. Our data of 9,066 patients was collected and integrated from operational clinical electronic health records. Our best classifier, XGBoost, achieved a performance of AUC 0.890 and beat the current generic gold standard, GRACE (AUC 0.822).
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by Dimitrios G. Katehakis and Angelina Kouroubali (FORTH-ICS)
When interoperability policies and consistent implementations are in place, digital tools for citizen empowerment can be developed and used to provide information that would otherwise be unavailable to citizens.
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by Chau Vo, (Ho Chi Minh City University of Technology, Vietnam National University), Bao Ho (John von Neumann Institute, Vietnam National University) and Hung Son Nguyen, University of Warsaw
Inspired by MIMIC-III [1], VNUMED is a unified intermediate database of electronic medical records that is being developed in Vietnam. Its purpose is to gather medical records from hospitals, which can be used to support medical research.
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by Artur Rocha, José Pedro Ornelas, João Correia Lopes, and Rui Camacho (INESC TEC)
Novel data collection tools, methods and new techniques in biotechnology can facilitate improved health strategies that are customised to each individual. One key challenge to achieve this is to take advantage of the massive volumes of personal anonymous data, relating each profile to health and disease, while accounting for high diversity in individuals, populations and environments. These data must be analysed in unison to achieve statistical power, but presently cohort data repositories are scattered, hard to search and integrate, and data protection and governance rules discourage central pooling.
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by Vlad Manea (University of Copenhagen) and Katarzyna Wac (University of Copenhagen and University of Geneva)
WellCo is a European H2020 project that aims to design and evaluate an engaging virtual coach to help older adults make positive behavioural choices that benefit their long-term health, wellbeing, and quality of life in physical, psychological and social interaction domains.
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by Otilia Kocsis, Nikos Fakotakis and Konstantinos Moustakas (University of Patras)
SmartWork is a European project addressing a key challenge facing today’s older generation, as they are living and working longer than their predecessors: the design and realisation of age-friendly living and working spaces. SmartWork is building a worker-centric AI system to support active and healthy ageing at work for older office workers. In SmartWork modelling of work ability, defined as the ability of an individual to balance work with other aspects of their life, will account for both the resources of the individual and factors related to work and the environment outside of work.
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by Marco Manca, Parvaneh Parvin, Fabio Paternò, Carmen Santoro and Eleonora Zedda (ISTI-CNR)
The AAL PETAL project has developed a platform for personalising remote assistance of older adults with mild cognitive impairments. The platform is targeted at caregivers without programming knowledge in order to help seniors in their daily activities at home.
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by Eleni Boumpa and Athanasios Kakarountas (University of Thessaly)
Rates of dementia are increasing, putting pressure on national health systems. Digital health can help both patients and national health systems in a range of ways. One technology that is being developed is AuDi-o-Mentia,an acoustic memory aid to help people in the early stages of dementia.
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by Eleonora Ciceri (MediaClinics Italia), Marco Mosconi (MediaClinics Italia), Melek Önen (EURECOM) and Orhan Ermis (EURECOM)
The PAPAYA project is developing a dedicated platform to address privacy concerns when data analytics tasks are performed by untrusted data processors. This platform regrouping will allow stakeholders to ensure their clients’ privacy and comply with the General Data Protection Regulation (GDPR) [L1] while extracting valuable and meaningful information from the analysed data. PAPAYA targets two digital health use cases, namely arrhythmia detection and stress detection, whereby patients’ data are protected through dedicated privacy enhancing technologies.
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by Emmanouil G. Spanakis and Vangelis Sakkalis (FORTH-ICS)
DAPHNE is aiming to develop a resilient networking service for critical related applications, as a novel approach for next generation mHealth information exchange. Our goal is to provide in-transit persistent information storage, allowing the uninterruptible provision of crucial services. Our system will overcome network instabilities, capacity efficiency problems, incompatibilities, or even absence of end-to-end homogeneous connectivity, with an emphasis on future networks and services (i.e. 5G). We aim to provide a set of tools for the appropriate management of communication networks during their design time and avoid the “build it first, manage later” paradigm.