Das CAIM ist ein Vorreiter in der Forschung und Innovation rund um Digitalisierung und KI im Gesundheitswesen. Es baut auf die starke Partnerschaft der Medizinischen Fakultät der Universität Bern mit dem Inselspital (Universitätsspital Bern), welche einen transparenten Umgang mit klinisch relevanten Informationen und eine enge Zusammenarbeit aller Beteiligter Fachexpertinnen und -experten aus der Medizin und dem Ingenieurswesen einschliesst.

Das CAIM identifiziert neue Projekte, die ein hohes Potenzial haben, bahnbrechend für zukünftige therapeutische und klinische Ansätze zu sein, und die einen realistischen und umsetzbaren Weg zum direkten Patientennutzen aufzeigen. Das Zentrum bietet wettbewerbsfähigen, kollaborativen, bottom-up Forschungsprojekten Unterstützung in Form von Finanzierung und Ressourcen. Die unterstützten Projekte spezialisieren sich auf KI und Digitalisierung in der Medizin in den Bereichen Grundlagenforschung, Proof-of-Concept oder Translationsforschung.

Projekte 2022/23

Die folgenden fünf Projekte wurden von der Medizinischen Fakultät der Universität Bern aus 20 eingereichten Anträgen zur Finanzierung durch den CAIM-Forschungsfonds ausgewählt.


Artificial Intelligence Analysis of Quantitative CMR Output Data to Better Risk Stratify Patients with Suspected Myocarditis

Prof. Dr. Dr. med. Christoph Gräni / Yasaman Safarkhanlo

Universitätsklinik für Kardiologie, Inselspital, Universitätsspital Bern

Abstract: Patients with suspected myocarditis (an inflammation of the myocardium) can present very heterogeneously from being asymptomatic to heart failure or arrhythmia. The 'Inflammatory Cardiomyopathy Bern Registry (FlamBeR)’ and CMRMyo registry evaluates the diagnostic and prognostic value of cardiac magnetic resonance (CMR) images in patients with suspected myocarditis with a focus on risk improving stratification. In addition to traditional CMR parameters, this project will evaluate the role of modern CMR technologies, such as T1- and T2-mapping and CMR-feature tracking (CMR-FT). CMR-FT is a finely granulated technology, which better mirrors myocardial function (compared to traditional left ventricular ejection fraction). As a post-processing image analysis with ample quantitative secondary output data, CMR-FT may include important information regarding risk stratification.


Machine learning models in the prediction of kidney stone recurrence

Prof. Dr. med. Daniel Fuster / PD Dr. Rémy Bruggmann

Universitätsklinik für Nephrologie, Inselspital / Interfakultäre Bioinformatik, Universität Bern

Abstract: Kidney stones affect 10-15 % of the population worldwide and both prevalence and incidence are on the rise. The recurrence rate is high, necessitating frequent hospital visits with urological interventions that result in enormous healthcare expenditures. Risk of recurrence and stone type, which dictates prophylactic treatment, is currently poorly predictable. The aim of this project is to develop predictive machine learning (ML) tools for kidney stone type and recurrence risk using both demographic information and 24-hour urine biochemistry. 


Longitudinal follow-up of multiple sclerosis patients: effect of scanner and sequence changes using resolution adaptive neural networks

Dr. Richard McKinley / Dr. med. Piotr Radojewski

Universitätsinstitut für Diagnostische und Interventionelle Neuroradiologie & Support Center for Advanced Neuroimaging (SCAN), Inselspital / Translational Imaging Center (TIC), sitem-insel

Abstract: Magnetic Resonance Imaging is a vital tool in the follow-up of multiple sclerosis patients, allowing neurologists to observe disease-related changes in a patient`s brain. Of particular importance is the detection of new or enlarged lesions: this time-consuming task is currently performed by neuroradiologists. Automated tools provide a potential solution to this problem, but it is not yet known how such tools perform when a patient changes hospital or after a scanner upgrade. We will compare standard and state-of-the-art techniques in lesion detection, and analyse their robustness to these changes.


Development and evaluation of a digital care assistant for an old age psychiatry setting

Prof. Dr. Tobias Nef / Prof. Dr. med. Stefan Klöppel

ARTORG Center for Biomedical Engineering Research, Universität Bern / Alterspsychiatry, Universitäre Psychiatrische Dienste Bern (UPD)

Abstract: A shortage of skilled workers and pressure to reduce costs are characteristics of care for the elderly. In this project, we plan to develop and evaluate a digital care assistant to support nurses in acute care, old age, psychiatry settings. We will equip patient rooms at the UPD Waldau with sensors (e.g., pressure sensor, ambient sensors), subsequently training artificial intelligence (AI) algorithms to detect when nurses should intervene. We plan to test whether a digital nursing assistant a) is perceived as useful by nursing staff, b) reduces subjectively perceived stress, and c) reduces the distance traveled by nursing staff. 


Personalized quantification of risks in menopausal women with mobile data and statistical machine learning

Prof. Dr. med. Petra Stute / Prof. Dr. David Ginsbourger

Universitätsklinik für Frauenheilkunde, Inselspital / Institut für mathematische Statistik und Versicherungslehre, Universität Bern

Abstract: Menopause affects all women. It is a time when a woman’s body stops producing the sex hormones estrogen and progesterone which usually takes place around the age of 51. As a result, women can have noticeable symptoms like hot flashes, sleeplessness, achy joints, and weight gain. However, more critically menopause can also put women at a greater risk of chronic non-communicable diseases like cardiovascular disease, diabetes mellitus, cancer, osteoporosis, and dementia. This project will develop a digital medical device App called Navina+ for women in or after their menopause. The Navina+ App will work with data from a smart tracker, e.g. FitBit®, to drive statistical machine learning models to predict future risks of chronic disease. The woman will then be given suggestions of action, e.g. life-style changes, hormone replacement therapy etc., depending on the severity and type of risk and the informed self-management choices a woman wishes to make. 


Die erste Ausschreibung des CAIM-Forschungsfonds ist geschlossen.

Künftige Ausschreibungen werden hier publiziert.