in the service of health

MIAI@Grenoble Alpes

structured around two main themes: AI for the future and AI for humans and the environment. In addition to more targeted projects, four dedicated chairs and a joint research laboratory put AI at the service of health in line with the 4Ps (predictive, personalised, preventive, participative) medicine.

Putting AI at the service of “care” in an integrative health logic: this is the challenge of the “Deep Care: Patient Empowerment via a Participatory Health Project” chair. Headed by public health professor Philippe Cinquin, it applies Grenoble’s expertise in capturing real-life data to the capture of new data on health determinants (environment, social interactions, nutrition, etc.). The capture is carried out by micro and nanosensors that enable patients to be the actors of their own health. Four tasks have been defined. The first task aims to capture relevant data through various means: for example, electric bicycles and smart waistcoats to adapt a rehabilitation programme, sensors to characterise cardiac effort, swallowable micro-robots with biological programming to analyse the microbiota with a view to diagnosing a chronic disease with the start-up Pelican, or an implantable medical device to analyse physiological variations in order to prevent a relapse of heart failure with the start-up SentinHealth. The second task is to merge massive and heterogeneous data thanks to PREDIMED, a health data warehouse designed at the Grenoble Alpes University Hospital, which makes it possible to identify health determinants that can improve patient care. The third task consists of correlating hospital data and human and social sciences (SHS) to propose a reorganisation of the emergency service of the Grenoble Alpes University Hospital, which has been weakened by the implementation of the T2A (fee-for-service). The fourth, more transverse task concerns the SHS: self-image and body anthropology are at the heart of tools co-constructed with patient associations. The Chair also collaborates with the MSA (agricultural social security - identification of new correlations between long-term illnesses and occupations), Thales (image processing for better management of undernutrition), ICAlps and ST Microelectronics (design of miniaturised intelligent implantable devices), several start-ups (Etisense, Texisense, eBikeLabs, Theia) and other industrial partners. A well-deserved recognition.

Computer-assisted medical intelligence

Co-directed by Jocelyne Troccaz, research director, and Sandrine Voros, research fellow, from the GMCAO team (Computer-Assisted Medical-Surgical Procedures - TIMC-IMAG Laboratory - UMR CNRS - UJF 5525), the “Computer-Assisted Medical Interventions (CAMI) Assistant” chair aims to develop a new generation of computerised intraoperative assistants based on AI, with the aim of treating patients more efficiently and less invasively. The fusion of big data, coupled with simulation and monitoring tools, could improve surgical strategy decisions and the quality of surgical procedures. Deep learning and machine learning are used to solve problems of medical image segmentation, to sort out relevant information from non-useable data or to better understand surgeons’ movements and to empower certain parts of the procedure where human added value is not essential. For example, in laparoscopic surgery, a questionnaire submitted to six surgeons recently showed that they tended to agree with the criteria chosen by the algorithms to make their decisions. Ultimately, these new techniques offer a new perspective on old problems. A potential to be exploited.

Telecom4Health, a joint University of Grenoble Alpes - Orange research laboratory

The result of a collaboration that began in 1999, this brand-new joint research

Automatic segmentation of laparoscopic images with deep learning methods / Segmentation automatique d’images laparoscopiques avec des méthodes d’apprentissage profond laboratory formalises a longer-term partnership. “It allows us to motivate teams for projects lasting more than one or two years, to develop research with uncertain results, not allowed by calls for tender, to go beyond the regional framework and to tackle societal issues,” emphasises Nicolas Vuillerme, teacher-researcher, honorary member of the Institut Universitaire de France, co-founder and co-director of the Telecom4Health labcom. Focused on the search for solutions for healthcare professionals, the current projects have been integrated into the laboratory’s roadmap with 4 well thought-out axes: the management of informed consent for patients in clinical trials thanks to a hyper-secure solution based on blockchain technology developed by Orange, the transmission of telephone sounds to improve remote diagnosis and patient care, the “Alloscope” project, which aims to develop tools to prevent the moral fragility of isolated people by studying the variation in behaviour via the use of the telephone (digital psychiatry), and the Maxwell application, an AI platform developed by Orange and dedicated to the classification of technical documents which is being tested on medical documents in order to offer diagnostic assistance. “Orange is Europe’s leading research operator and it is crucial to build trust in AI through the implementation of informed and secure consent management for patients and healthcare staff and the anonymisation of health data,” explains Roxane Adle Aiguier, in charge of the Digital Society research area at Orange.

AI for high throughput biomedical research

The “Artificial Intelligence for High throughput biomedical investigations” chair is headed by Prof. Julien Thévenon, head of the genetic medicine department at Grenoble University Hospital, and Dr. Thomas Burger, a biostatistics researcher at CNRS. Its scientific programme is composed of two main components. Co-directed by Dr Emmanuel Barbier, Director of Research at Inserm, and Julien Thévenon, the first one focuses on the evaluation of AI applications to rare diseases using medical data, brain imaging data and whole genome sequencing (neurogenomics). The second one, supervised by Thomas Burger and Bertrand Toussaint, professor of biochemistry at Grenoble University Hospital, is proteomics. “The objective is to explore the identification of biomarkers using a mass spectrometry approach,” explains Julien Thévenon. The chair is funded by Grenoble Alpes University, the ANR and four active industrial partners: the SMEs Pixyl (Grenoble) and SeqOne (Montpellier) as well as the medical biology laboratory Biomnis (Lyon) for neurogenomics and the SME Sinnovial for proteomics. In addition, neurogenomics is at the intersection of several national plans: “the 2018-2022 Rare Diseases Plan 3, with 3 reference centres labelled by the Ministry at the Grenoble University Hospital, France Médecine Génomique 2025, which benefits from the AURAGEN (Auvergne Rhône-Alpes Génomique) very high-speed sequencing platform and, of course, the Artificial Intelligence Plan,” explains Julien Thévenon. There is a lot of promise for synergies in the future.

Towards a “medicine of trajectories”

The “MyWaytoHealth” chair is chaired by Jean-Louis Pépin, professor of clinical physiology at Grenoble Alpes University, vice-dean of the Grenoble Faculty of Medicine in charge of research and director of the HP2 Laboratory (Inserm U1042; Physiopathology of hypoxia). His ambition: to use AI to develop a “trajectory medicine” applied to obstructive sleep apnoea (OSA), which affects one billion people worldwide. Its innovation: the concept of trajectome, which encompasses the conditions, risk factors, environmental and societal determinants that modulate the trajectory of the disease in the patient’s ecosystem. The Chair relies on AI and the integrative analysis of structured and unstructured data captured by individual digital health passports, international and national disease registries, longitudinal data generated by innovative sensors, national administrative databases on healthcare, socio-economic determinants, attitudes and behaviours. A rich harvest that will feed 3 research axes: large-scale treatment and interactive multidisciplinary exploration of patient trajectories; prediction of health evolution and aggregation of co-morbidities; integrated and patient-centred care, health system reform and cost-effectiveness evaluation. A valuable contribution to rethinking clinical decision making and therapeutic interventions for value-based reorganisation of care.

MIAI Grenoble Alpes Université de Grenoble Alpes Bâtiment IMAG 700, avenue Centrale - CS 40700 F-38058 Grenoble Cedex Tél. : +33 (0)4 57 42 15 00 E-mail : Eric.Gaussier@imag.fr https://miai.univ-grenoble-alpes.fr/