In medical collaboration with: |
Prof. Dr. Adnan Kastrati, Deutsches Herzzentrum, Munich
Scientific Director: Nassir Navab
Contact Person(s): Sebastian Pölsterl
In industrial collaboration with:
Ali Kamen, Siemens Corporate Research, Princeton, New Jersey, USA
Machine Learning for Medical Applications
Efficacious treatment and management of patient with the Coronary Artery Disease (CAD) has drawn a lot of attention especially because of the high prevalence and high mortality rate. Two major treatment options are Coronary Artery Bypass Grafting (CABG) and Percutaneous Coronary Intervention (PCI). CABG is more expensive and invasive, however, the overall prognosis is better than PCI, which is widely considered as a less expensive and minimally-invasive option. It is documented that a large percentage of patients (up to one third) undergoing PCI procedure require repeat procedure after several years. It is beneficial to both patients and payers to identify this high-risk patient group as early as possible based on a series of in-vitro and imaging tests and possibly choose an alternate treatment option. Moreover, the identification of this high-risk patient group within the early months of PCI intervention could also trigger better management of these patients through administering possibly invasive tests (e.g., optical coherence tomography-OCT) to characterize and quantify the disease process and the associated risk. Our project partner at German Heart Center has collected large amount of data from patient cohorts that includes results of commercially available in-vitro tests, imaging tests, and for subset of patients the plasma samples and the follow-up data with clinical outcomes. The goal of this project is to identify features such as blood biomarkers that are predictive of PCI prognosis, to devise an algorithm decision support system to aggregate the biomarker test (including imaging) results and optimize the correlation to the clinical outcome, characterize and validate the system built based on retrospective data.
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Please contact Sebastian Pölsterl for available student projects within this research project.