In this workshop, which will be held the 5th of November, we will discuss hybrid approaches, combining traditional bioinformatics and machine learning, with new generative AI approaches and mechanistically based digital twins. This combination of methods is ideal for dealing with a variety of different data types, and for developing AI models that are explainable and trustworthy. The event will feature keynote lectures, talks selected from abstracts, a poster session, and will be in hybrid format. Link to more info here:
A few weeks ago, on June 10, STRATIF-AI had its first meeting with fellow EU projects funded under the same call, all focusing on the use of AI for improved patient stratification. This initial two-hour meeting served as an introduction to each project and a discussion of key shared challenges (see Figure below). These challenges include data interoperability, federated learning, trustworthy and explainable AI (xAI), and MDR certifications.
During the meeting, we presented some core concepts of STRATIF-AI, such as our hybrid approach that combines multi-level, multi-timescale, and multi-organ simulations using mechanistic models as inputs to machine-learning models. This hybrid approach enhances the explainability of our models.
Furthermore, we explained shortly how STRATIF-AI uses data harmonization in two places: i) in connection to the federated learning set up by a collaboration between Catalina Martinez Costa (University of Murcia) and Lucia Gregorio Rodriguez (TREE), and ii) in connection to integrating a copy data from a variety of different sources into a personal data vault (Jesper Fellenius at Z2).
This collaboration and network of related projects will be useful for us to use in various related topics as we move forward, and we from STRATIF-AI are looking forward to new meetings. Thanks to the team at PREPARE for setting this meeting up.
On April 11-12, we had a new bi-yearly meeting in STRATIF-AI. This meeting was held in beautiful Nottwil, Switzerland, where the Swiss Paraplegic Research center (SPF) is situated. SPF is responsible for the work with policy and actions toward stakeholders to achieve ultimate clinical implementation, and they were also the hosts of this event.
We have these events every 6 months, and they serve both the purpose of team building and increased understanding of each others’ perspectives and serve to help us focus on the most urgent and timely topics. This time we focused primarily on the design of the 6 clinical studies, which ethical applications were to be submitted at the end of that month. Four of these studies are dedicated to the collection of patient data, aimed at training both the machine learning algorithms and the mechanistic aspects of digital twins. The remaining two studies focus on the real-world testing of eHealth apps within clinical settings. The largest of those will test whether the digital twin improves clinical health conversations, in 300 patients, compared with 300 matched controls.
Overall, the meeting was a success, and we have now passed the planning phase of the project, with requirement specifications and ethical plans, and are now moving into action: into prototype development of the apps, and towards the first pilot studies.
Our EU project STRATIF-AI (https://stratif-ai.eu/en), in which we develop digital twins for prevention, acute treatment and rehabilitation of stroke, was presented as part of “Clinical applications of computer modeling” at Latin American Summer School for Computational Neuroscience (LASCON) in Sao Paulo Brazil Jan 2024.
Figure shows how modern medicine has been changing and indicates role of modeling in modern (future) clinical practice — part of personalized medicine, data-mining, self-monitoring, omics, surgical planning, medical education, research, screening, etc
CompBioMed Conference 2023 (CBMC23) will address all aspects of computational biomedicine, from genome through organ to whole human and population levels, embracing data driven, mechanistic modelling and simulation, machine learning and combinations thereof. This year Gunnar will is one of the Plenary Speakers and he will discuss physiologically based digital twins: a digital and interactive copy of yourself that follows you throughout your health journey.
Join the VPH (Virtual Physiologocal Human) summer school in Barcelona, June 5-9, 2023. This summer school includes 15 morning lectures, one honorary VPH lecture, and a lot of networking opportunities. Gunnar will talk about about the digital twins, so don’t miss it!
Last day to register is 21 of May, and you find more information HERE
M4-health and digital twins: bring a digital copy of yourself with you throughout your health journey
Keynote Lecture, Wednesday June 7th, 2023, 09:30-11:00
Abstract: For the last 20 years, Cedersund has developed mechanistic mathematical models for all of the main organs in the human body: heart, liver, fat, brain, etc. Lately, these models have combined into an interconnected model for the body as a whole. This interconnected model can be made specifically for each individual, and is then called a digital twin. This digital twin technology employs a hybrid approach, which combines the mechanistic simulation models with machine learning and bioinformatics models. This allows a patient, doctor, or other end-user to look inside the body of a patient, as it is now, ranging from the whole-body to the intracellular level. This also allows for simulations of different future scenarios, ranging from ms to years, and can simulate e.g. the risk of a stroke, depending on choice of diets, exercise, and certain medications. The models are thus of an M4-nature: multi-level, multi-timescale, mechanistic, and multi-organ. The focus on this talk will be on how we systematically test mechanistically hypothesis on the intracellular and organ levels, using mechanistic modelling, optimization, and predictions with uncertainty – followed by corresponding model-designed experiments. I will also to some extent go through how we assemble the different organ sub-models together into the integrated digital twin, which in itself is a modelling problem, and how we then put all of this into a series of different eHealth apps.
Biosketche: Gunnar Cedersund (https://liu.se/en/employee/gunce57) heads a cross-disciplinary research group at the Department of Biomedical Engineering (IMT) at Linköping University. The heart of this group (15+ people) does hybrid mathematical modelling, combining machine learning with mechanistic small- and large-scale models. These models are developed using both pre-clinical and experimental data of various types, which are collected both by experimentalists and clininicians within the same group, and by numerous collaborators. These models are made available for preventive and patient-centric care, as well as for drug development and medical pedagogics, using innovative eHealth technologies. This is made possible, e.g., via the fact that Cedersund heads the 6MEuro EU project STRATIF-AI, which brings his digital twins into healthcare for all phases of stroke (prevention, acute treatment, and rehabilitation), using a series of different apps, which all are connected to the same backend.
We are pleased to invite you to our fourth Workshop on Modelling in Biology and Medicine (MBM 2023) on the 15-17th of May. We aim to gather all young researchers in Sweden working on modelling of biological systems. Our ambition is to give all participating PhD students and Postdocs the opportunity to present their work through an oral presentation or a poster. Further, we wish to provide an insight into how modelling in biology and medicine is practiced in academia and industry.
The workshop will be held in both plenary presentation sessions for larger talks as well as in smaller sessions for e.g. poster presentations. You can participate at MBM 2023 by
giving a plenary talk,
presenting a poster
or simply as an observer
The workshop will be in-person at Linköping University, Campus US.
When: 15-17th of May 2023 Where: Linköping
Why: Bringing together young researchers in Sweden working on the border of mathematics, biology, and medicine
How: Oral presentation, poster, or as an observer Abstract submission:https://forms.gle/jHNrg1QM6bGQhmx9A, deadline 14th of April Register: Registration will open soon
Our work “A quantitative model for human neurovascular coupling with translated mechanisms from animals”, previously available at bioRxiv, has now been published in PLOS Computational Biology. In this manuscript we explore if qualitative behaviors of the Neurovascular coupling (NVC), found in different species and data sets, can be applied to the model simulations of other data. Below follows a slightly more detailed summary of the manuscript.
The neurovascular coupling (NVC) is the basis for functional magnetic resonance imaging (fMRI), since the NVC connects neural activity with the observed hemodynamic changes. This connection is highly complex, which warrants a model-based analysis. However, even though NVC-data from several species and many relevant variables are available, a mathematical model for all these data is still missing. Herein, we combine experimental data from mice, monkeys, and humans, to develop a comprehensive model for NVC. Importantly, our new approach to modelling propagates the qualitative insights from each species to the subsequent analysis of data from other species. In mice, we unravel the role of different neuronal sub-populations when producing a biphasic response to prolonged sensory stimulations. The qualitative role of these sub-populations is preserved when analysing primate data. These primate data add knowledge on the interplay between local field potential (LFP) and vascular changes. Similarly, these pre-clinical qualitative insights are propagated to analysis of human data, which contain additional insights regarding blood flow and volume in arterioles and venules, during both positive and negative responses. This work illustrates how data with complementary information from different species can be combined, so that qualitative insights from animals are preserved in the quantitative analysis of human data.
