Projects
Pacertool - a patient specific biofeedback guiding tool to improve Cardiac resynchronization therapy
Cardiac Resynchronization Therapy (CRT) is one of the most important recent advancements in heart failure treatment. CRT consists of implantation of a resynchronization pacemaker and leads with electrodes that can stimulate both the right and left heart chamber. CRT therefore holds the potential to substantially improve patient care and reduce overall health care costs.
Several clinical trials have shown that use of CRT devices leads to long-term clinical benefits such as improved quality of life, exercise capacity, and reduction in hospitalization for heart failure and overall mortality. Despite these benefits, there remain a lot of unresolved questions and concerns, the most important being that up to 50 % of the patients treated with CRT do not derive any detectable benefit. There could be multiple reasons for such a substantial non-responder rate, with the most important being the positioning of the CRT device lead in left heart chamber. The determination of lead position is today doctor-dependent, and good positioning can be hard to achieve with limited access to physiological information. Therefore, lead optimisation strategies need to be developed which are patient-specific, have minor or no additional risks, guides the doctor to optimal positions and give immediate feedback on the effectiveness of pacing from lead location of choice.
The Pacertool project aims at development of the platform equipped with data analysis and visualization tools that provide feedback to the doctor in real-time during the procedure, aiming for a higher proportion of heart failure patients to have a positive effect of CRT.
Simula’s role
Simula will contribute with development of software and methods, such as for visualization, cardiac simulations, and data analysis, aimed at bringing the Pacertool software from the piloting phase to a complete state that enables clinical studies.
Partners
Oslo University Hospital
Inven2 AS
GE Vingmed Ultrasound
Medtronic Bakken Research Center
Funding Source:
Research Council of Norway (Biotek2021)
Coordinator: Hans Henrik Odland, Oslo University Hospital
Publications
Poster
B-PO02-022 Combining simulation and machine learning to accurately predict arrhythmic risk in post-infarction patients
In Heart Rhythm. Vol. 18. Boston, MA: Elsevier, 2021.Status: Published
B-PO02-022 Combining simulation and machine learning to accurately predict arrhythmic risk in post-infarction patients
| Afilliation | Scientific Computing |
| Project(s) | Department of Computational Physiology |
| Publication Type | Poster |
| Year of Publication | 2021 |
| Secondary Title | Heart Rhythm |
| Publisher | Elsevier |
| Place Published | Boston, MA |
Journal Article
Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients
Frontiers in physiology 12 (2021): 745349.Status: Published
Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients
| Afilliation | Scientific Computing |
| Project(s) | Department of Computational Physiology |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | Frontiers in physiology |
| Volume | 12 |
| Pagination | 745349 |
| Publisher | Frontiers |
Nationwide rollout reveals efficacy of epidemic control through digital contact tracing
Nature Communications 12 (2021).Status: Published
Nationwide rollout reveals efficacy of epidemic control through digital contact tracing
| Afilliation | Communication Systems, Scientific Computing, Machine Learning |
| Project(s) | The Center for Resilient Networks and Applications, Department of Data Science and Knowledge Discovery , Department of Computational Physiology |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | Nature Communications |
| Volume | 12 |
| Number | 5918 |
| Publisher | Springer Nature |
| DOI | 10.1038/s41467-021-26144-8 |
Talks, contributed
A Combined In-Silico and Machine Learning Approach towards Predicting Arrhythmic Risk in Post-Infarction Patients
In Computing in Cardiology, Singapore, 2019.Status: Published
A Combined In-Silico and Machine Learning Approach towards Predicting Arrhythmic Risk in Post-Infarction Patients
| Afilliation | Scientific Computing |
| Project(s) | MI-RISK: Risk factors for sudden cardiac death during acute myocardial infarction , Department of Computational Physiology |
| Publication Type | Talks, contributed |
| Year of Publication | 2019 |
| Location of Talk | Computing in Cardiology, Singapore |
Proceedings, refereed
Automated and objective segmentation of medical image using machine learning techniques: all models are wrong, but some are useful
In Computational and Mathematical Biomedical Engineering. Sendai, Japan: CMBE, 2019.Status: Published
Automated and objective segmentation of medical image using machine learning techniques: all models are wrong, but some are useful
Medical images are the basis of ”patient-specific” simulations but come with severe limitations, most notably through operator dependencies like image segmentation. The aim was to develop an open- source pipeline for automated and objective segmentation. Combining latest advances from machine learning and signal processing, we demonstrate that the pipeline preserve all major characteristic features of a test image and identify minor branches, which can be further modified by the user. In conclusion, the default pipeline will in the majority of cases offer labor free automated and objective segmentation, or at worst provide an optimal starting point for manual segmentation.
| Afilliation | Scientific Computing, Machine Learning |
| Project(s) | Simula Metropolitan Center for Digital Engineering, Department of Computational Physiology |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | Computational and Mathematical Biomedical Engineering |
| Publisher | CMBE |
| Place Published | Sendai, Japan |
