About the project

Objective
The overall objective of this project is to develop a predictive model for the progression of Aortic Valve Stenosis (AVS) by integrating repeated CT imaging, fluid–structure interaction (FSI) simulations, and deep machine learning techniques. Through comprehensive analysis of large-scale clinical datasets and high-fidelity simulations, this project seeks to move beyond conventional imaging and hemodynamic metrics to enable early detection, individualized prognosis, and improved clinical decision-making in AVS management.

Background
Aortic valve stenosis (AVS) is dangerous because it leads to obstructed blood flow from the heart, causing increased pressure within the heart and raising the risk of heart failure, arrhythmias, and other severe complications. Over 2 million people worldwide are affected by the condition, with its prevalence increasing as the population ages, and it is often diagnosed through imaging techniques such as echocardiography and CT scans. Treatment options include surgical and transcatheter aortic valve replacement (SAVR/TAVR), both aiming to relieve symptoms and improve heart function, though there is uncertainty in selecting the best approach for individual patients based on their health condition and the severity of the disease.

Therefore, there is a critical need for a physics-based understanding of the progression of AVS, as current recommendations of AVS management and monitoring do not fully account for the complex interaction of hemodynamics (blood flow) and biomechanics (tissue deformation), which drive valve degeneration and calcification. In particular, oscillatory shear index and high shear stress in certain regions of the valve and aorta, along with altered cyclic loading, contribute to the pathological changes in the valve, exacerbating stenosis and complicating treatment outcomes.

Cross-disciplinary collaboration
This project brings together a new cross-disciplinary and complementary collaboration uniting experts in cardiovascular imaging, fluid dynamics, and machine learning to advance predictive modeling of aortic valve disease:

• Elias Sundström, a researcher in Fluid Mechanics at the Department of Engineering Mechanics, KTH, specializes in developing computational models to elucidate the complex fluid–structure interactions that occur within the human body.
• Magnus Bäck, is a senior physician and Professor in Cardiology at Karolinska Institutet, Translational cardiology. Research focus on Aortic Valve Stenosis. 
• Payam Esfahani, is a postdoctoral researcher at KI in Magnus Bäck’s research group. Research focus on using advanced AI/ML applied to biomedical imaging and will remain a key contributor to the development of the project’s methodologies.

About the project

Objective
The CARE-AI (Causal Adaptive Reasoning for Effective Hospital Admission Interventions) project aims to fundamentally change how hospitals prevent patient readmissions. Today’s AI systems can predict which patients are at high risk of readmission. However, they cannot tell doctors what to do to stop it.

This project moves beyond simple prediction to intervention optimization. We are developing a framework using ‘causal AI,’ a method that identifies cause-and-effect relationships, to answer the most important question: ‘Which specific intervention will most benefit this specific patient to avoid readmission?’ Rather than only marking a patient as high-risk, the CARE-AI system will guide clinicians in choosing the best, personalized steps. This could mean adjusting medication, setting up a follow-up appointment, or offering education tailored to the patient.

The two-year project has two main phases. In the first phase, we will develop the core causal AI algorithms—AI methods that determine which actions cause better outcomes for which group of patients—using comprehensive, anonymized health data from Region Stockholm’s electronic health records (EHR) and the VAL databases (VAL is a regional healthcare data). In the second phase, we will conduct a Randomized Controlled Trial (RCT) at Södersjukhuset. In this trial, 250-300 high-risk patients will be randomized to receive either standard care or personalized interventions guided by the new causal AI framework.

The ultimate goal of the project is a clinically validated AI tool that provides clear, interpretable recommendations to healthcare staff, leading to fewer readmissions, better patient outcomes, and more efficient use of healthcare resources.

Background
Hospital readmissions are a critical and costly challenge for healthcare systems worldwide. In Sweden, 15.2% of all patients are readmitted to the hospital within 30 days. This figure rises to an alarming 28% for elderly patients with multiple chronic conditions.

The core problem is that many of these readmissions are preventable. The “one-size-fits-all” approach to discharge and follow-up isn’t effective. Current predictive AI models fall short because they are based on correlation, not causation. They can identify that a patient looks like other patients who were readmitted. But they can’t determine why or identify the specific, modifiable factor that would change the outcome for that individual.

This project tackles that fundamental gap. We’re moving from asking “Who is likely to be readmitted?” to “Who is likely to benefit from which intervention to avoid readmission?” By identifying the causal drivers of readmission, we can target interventions that make a real difference. With each hospital bed costing approximately 12,000 SEK per day, preventing unnecessary readmissions is crucial for a sustainable healthcare system.

Cross-disciplinary collaboration
The CARE-AI project is built on a “triple-helix” collaboration. This approach brings together world-class expertise from academia, clinical healthcare, and industry. This structure is essential to ensure the project is not only scientifically rigorous but also clinically relevant and practically implementable.

• KTH Royal Institute of Technology (Academia): Karl Henrik Johansson, Prof., and Umar Niazi, researcher, provide the project’s methodological leadership. They will develop novel causal AI, machine learning, and reinforcement learning algorithms. These algorithms form the core of the CARE-AI framework.
• Karolinska Institutet / Södersjukhuset (Healthcare): Patrik Lyngå, Assoc. Prof., and Raffaele Scorza, M.D. and Ph.D., provide crucial clinical expertise and validation. They are responsible for designing and executing the RCT. This ensures the AI’s recommendations are medically sound, safe, and effective in a real-world hospital environment.
• Sirona Group (Industry): Jenny Censin, M.D. and Ph.D., provides deep implementation expertise and a pathway to real-world deployment. Sirona brings experience in healthcare data analytics and existing platforms like AINA. This will be key to integrating the new AI framework into current clinical workflows and health data systems.

This close collaboration ensures a constant feedback loop. Clinical needs from Södersjukhuset and implementation challenges from Sirona directly inform the AI development at KTH. At the same time, the new AI tools are immediately tested and refined in a clinical setting.

About the project

Objective
This project aims to advance, evaluate and demonstrate three interactive sonic prototypes designed to support sleep across three critical stages: sleep onset, the sleep period, and awakening. The SoundAsleep app recommends sleep soundscapes based on the user mood; the Sonic Blankets are interactive sonic textiles designed to support sleep through soothing auditory and tactile stimulation; and the SoundRise alarm fosters a gentle and natural awakening experience by emitting a gradually rising sound starting below the ambient room tone. Prototypes will be advanced through contextual adaptation, enhancing their personalisation and sonic versatility, and participatory design, engaging varied age groups and genders.

Background
Sleep disturbances are common conditions that impact both physical and mental health; yet few technologies exist to support sleep quality. Cultural and historical practices highlight sound’s therapeutic potential. In addition to singing and music, broadband noise, natural soundscapes, and specific tones are gaining popularity. But designing sound alone is insufficient. Interventions need to consider demographic and contextual factors, such as age, gender, and the physical environment, which substantially affect sleep quality. In recent years, sleep technologies have started to compete with traditional sleep interventions, yet many interventions remain under-evaluated.

This research aims to contribute to the development of non-pharmacological, low-risk, personalised and adaptive sleep interventions, while simultaneously enhancing public awareness of the role of sleep in health and wellbeing. 

Crossdisciplinary collaboration
The researchers in the team represent the KTH School of Electrical Engineering and Computer Science, Department of Media Technology and Interaction Design, and KTH School of Engineering Sciences in Chemistry Biotechnology and Health, Department of Biomediacal Engineering and Health Systems.

About the project

Objective
Develop a multiscale modelling framework that combines experimental imaging, high-resolution simulations, and machine learning to accurately and efficiently predict fluid transport in fuel cell porous layers, enhancing our understanding of the multiscale physics and enabling improved system-level design and performance optimization.

Background
The global shift toward sustainable energy requires rapid development of efficient hydrogen technologies, with fuel cells playing a central role. A major challenge is that key fuel cell components, such as porous transport layers, involve complex multiscale fluid and transport processes that are difficult to characterize and model. Advancing predictive modelling tools for these components can significantly accelerate innovation and strengthen Sweden’s emerging hydrogen value chain. This project addresses this need by developing a modern, physically grounded, data-driven modelling foundation that can support improved design of fuel cell systems and inspire broader interdisciplinary research in electrochemical energy technologies.

Crossdisciplinary collaboration
Collaboration between the FLOW group in the Engineering Mechanics department and the Applied Electrochemistry group in the Chemical Engineering department at KTH Royal Institute of Technology.

About the project

Objective

• Develop a high-fidelity simulator that replicates Swedish railway operations, including infrastructure, rolling stock, power supply, signalling, and realistic operating behaviour.
• Create and analyse operational scenarios using historical, live, or simulated data to support performance monitoring, strategic planning, and subsystem interactions.
• Design analysis and visualisation modules to evaluate railway performance, including delay propagation, energy use, track capacity, driver behaviour, and the impact of digital technologies (e.g., ATO, DAC, VC).
• Demonstrate use cases with external partners through performance analysis, disruption management, decision support, subsystem compatibility testing, and development of a scalable digital twin architecture.

Background
Railways are essential for achieving climate-neutral, energy-efficient, and resilient mobility. In Sweden, they are a key pillar of sustainable transport policy. However, increasing capacity demands, operational complexity, infrastructure ageing, and the need for digital transformation pose major challenges. Despite progress in technologies and predictive management, the sector still lacks integrated platforms for testing, real-time data use, and efficient planning.

The SPOT-Rail project addresses these gaps by developing a cross-system railway demonstrator that replicates Swedish rail operations. At its core is a high-fidelity train driving simulator connected to live and historical data, supporting research, education, and strategic planning. This enables the testing of new technologies, evaluation of operational strategies, and development of decision-support tools.

By bridging research and real-world operations, SPOT-Rail promotes safer, more efficient, and environmentally friendly rail transport. It contributes to Sweden’s and Europe’s green transition goals while fostering innovation, collaboration, and a skilled workforce for the future of sustainable mobility.

Crossdisciplinary collaboration
The project brings together expertise from multiple disciplines to enable efficient and realistic simulation of railway operations. Key fields involved include: Railway systems engineering and train operations; Human factors and interaction design; Data science and artificial intelligence; Systems engineering. These disciplines contribute various theories, simulation modelling approaches, and machine learning algorithms for predictive analysis and decision support. Furthermore, the project aims to engage students by integrating its outcomes into university courses and offering opportunities for course projects and final theses. This strengthens the link between research, education, and practical application.

About the project

Objective
Breast and prostate cancers are among the most diagnosed cancers worldwide and in Sweden, arising from a complex interplay of genetic, environmental, and epigenetic factors. This project aims to develop an AI-powered, open-source, explainable toolkit that provides clinicians with a holistic and personalized assessment of breast and prostate cancer risk. By leveraging Swedish national health registries, we will capture key risk dimensions and integrate them into advanced predictive models capable of handling complex, interacting factors.

Specifically, the project will design and validate advanced predictive models to quantify the combined and individual contributions of multiple risk factors; implement a user-friendly, open-source Python toolkit that translates complex model outputs into actionable clinical risk scores and visualizations for decision support; conduct a clinical pilot in a regional setting to evaluate usability, functionality, and preliminary clinical utility in identifying individuals at elevated risk; and finally, formulate a strategy for national scaling and holistic integration, including a roadmap for incorporating genetic risk profiles from parallel projects, thereby paving the way for a comprehensive cancer risk assessment platform.

Background
Although high-risk inherited mutations (such as in BRCA1/2) are well known, they only explain a fraction of breast and prostate cancer cases. A large share of risk is instead driven by modifiable lifestyle factors acting through epigenetic mechanisms over the life course. Current clinical practice and many existing risk models still treat these factors in isolation and struggle to capture their cumulative, non-linear, and interacting effects. At the same time, Sweden maintains exceptionally rich national registries covering cancer diagnoses, healthcare utilization, prescriptions, demographic and socioeconomic data, and environmental conditions.

These data sources are ideal for characterizing the totality of non-genetic exposures but remain underused in integrated, clinically oriented tools. Traditional statistical methods often cannot cope with the high dimensionality and complexity of such data, while state-of-the-art AI and machine-learning approaches tend to function as “black boxes” that clinicians may be reluctant to trust in high-stakes decisions. This situation creates a clear gap for explainable, registry-driven AI that can bridge genetic and non-genetic risk into a transparent and usable decision-support tool.

Crossdisciplinary collaboration
The project is built around a research pair that combines expertise in computer science and AI with medical bioinformatics, epidemiology, and clinical cancer research. 

Golnaz Taheri leads the development of advanced machine-learning models, explainable AI techniques, large-scale data processing, and the implementation of the open-source toolkit. 

Arian Lundberg contributes expertise in medical bioinformatics, biomarker and translational research, epidemiology, and public health, ensuring that evaluations are clinically relevant and aligned with real-world cancer care. Together, this collaboration exemplifies a cross-disciplinary fusion of AI, digital health, and clinical cancer research, aiming to deliver tools that are both technically robust and directly applicable in healthcare and public health practice.

PI: Assist Prof. Golnaz Taheri, PhD, Knut and Alice Wallenberg & SciLifeLab DDLS Fellow, School of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology.

PI: Assist Prof. Arian Lundberg, PhD, Knut and Alice Wallenberg & SciLifeLab DDLS Fellow, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), KTH Royal Institute of Technology.

About the project

Objective
Kidney diseases affects approximately 10% of the world’s population and is projected to become one of highest cause of life-lost-years within two decades. Despite this growing healthcare threat, diagnostic methods remain insufficiently precise, which unfortunately hinders early quantitative kidney disease staging. We aim to demonstrate a precision medicine pipeline that combines high-resolution optical 3D whole biopsy imaging with advanced AI-assisted 3D image analysis. Our approach eliminates preparation-induced biases by not slicing tissue biopsies, thereby preserving native 3D architecture and in-context spatial relationships. High-resolution imaging in 3D will spatially resolve key structural and molecular disease markers across scales in optically cleared biopsies. Moreover, our pipeline will automate 3D disease staging using AI-based image analysis, employing cutting-edge self-supervised learning to extract, segment, and quantify disease-relevant features objectively. 

Background
Pathology impacts all aspects of patient’s care from identifying a disease, to monitoring its progression and to make crucial treatment decisions. Clinical kidney pathology thus plays a central role as method for diagnostic support in healthcare to help renal physicians and patients. Present nanoscale kidney pathology analysis is done in ultra-thin biopsies by electron microscopy, losing precious in-context 3D information of morphological and molecular pathophysiology. Moreover, used workflows are highly labor intense across sample preparation, imaging and imaging analyzing steps.

Cross-disciplinary collaboration
Imaging and image analysis are inherent pathology tools that can deliver diagnostic support and guide patient care. Our developed kidney pathology workflow will be faster, more precise, save healthcare labor costs, and be less biased with automate AI analysis support. The exploration of self-supervised methods for functional structure analysis is furthermore a rapidly growing and impactful area in AI, as researchers seek more sophisticated ways to analyze high-dimensional biological data without manual annotations. Our work positions KTH at the forefront of this cutting-edge field, meeting the rising demand for robust, scalable AI tools that can improve diagnostic accuracy and consistency.

PI: Hans Blom, Applied Physics/SCI/KTH
Co-PI: Gisele Miranda, Computer Science/EECS/KTH