Endovascular Implant Decision Support in Medical Imaging
Summary
The USPTO granted patent US12605206B2 to Siemens Healthineers AG covering a vascular implant decision support system using medical imaging. The system employs physics-based modeling of endovascular implants to simulate deployment in patient-specific vessel models, and uses machine-learned networks to select implants and predict deployment outcomes. The patent names Viorel Mihalef and Saikiran Rapaka as inventors and contains 19 claims across multiple CPC classifications.
What changed
The USPTO issued patent US12605206B2 to Siemens Healthineers AG on April 21, 2026, covering systems and methods for endovascular implant decision support in medical imaging. The patent claims a system that uses physics-based modeling to simulate implant deployment within a patient-specific vessel model derived from medical imaging, incorporates machine learning to select implant configurations, and predicts hemodynamic parameters to support clinical treatment decisions.
Entities in the medical device and healthcare technology space should be aware that this patent establishes intellectual property protection around the use of patient-specific simulation and machine learning for endovascular implant selection and planning. Companies developing similar simulation-based or AI-driven vascular implant planning tools may need to assess freedom-to-operate considerations.
Archived snapshot
Apr 21, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Endovascular implant decision support in medical imaging
Grant US12605206B2 Kind: B2 Apr 21, 2026
Assignee
Siemens Healthineers AG
Inventors
Viorel Mihalef, Saikiran Rapaka
Abstract
A vascular implant decision support uses a medical imaging system. A physics-based model of the endovascular implant is used to simulate deployment in a vessel model of a patient based on medical imaging. A porosity of the deployed implant and the simulation are used to determine a value for each of one or more hemodynamic parameters to support the decision for endovascular treatment. A machine-learned network uses patient-specific information to select the endovascular implant, placement, and/or other implant configuration used to simulate deployment and/or to predict outcome from deployment for the patient. The clinician may use the decision support to select among options for implanting and/or to confirm adequacy of a plan. Various of these approaches may be used alone or in combination.
CPC Classifications
A61B 34/10 A61B 2034/104 A61B 2034/105 A61B 2034/374 A61B 2034/101 A61B 2090/3762 G16H 50/20 G16H 50/30 G16H 30/40 G16H 50/50 A61F 2/82 G06T 19/20
Filing Date
2018-09-25
Application No.
17257592
Claims
19
Parties
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Source
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