This inventor holds 4 USPTO granted patents and 1 published patent application. Top assignees: University of Michigan, King's College London, Angioinsight, Inc.. Active years: 2022-2026.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile
Company Filing History:


Years Active: 2022-2026
Title: Innovations in Cardiovascular Assessment by Krishnakumar Garikipati
Introduction
Krishnakumar Garikipati is an accomplished inventor based in Ann Arbor, MI (US). He has made significant contributions to the field of cardiovascular health through his innovative approaches to assessing coronary artery disease (CAD). With a total of 4 patents, his work focuses on utilizing machine learning and computational modeling techniques to enhance non-invasive diagnostic methods.
Latest Patents
Garikipati's latest patents include groundbreaking methodologies for the anatomical and functional assessment of coronary artery disease using machine learning. His work emphasizes non-invasive Fractional Flow Reserve (FFR) quantification based on angiographically derived anatomy and hemodynamics data. The patents rely on advanced machine learning algorithms for image segmentation and flow assessment, as well as accurate physics-based computational fluid dynamics (CFD) simulations for the computation of FFR.
Career Highlights
Throughout his career, Garikipati has worked with prestigious institutions such as the University of Michigan and King's College London. His experience in these organizations has allowed him to collaborate with leading experts in the field and contribute to significant advancements in cardiovascular research.
Collaborations
Some of his notable coworkers include Kritika Iyer and Elizabeth Renee Livingston. Their collaborative efforts have further enriched the research environment and fostered innovation in the assessment of coronary artery disease.
Conclusion
Krishnakumar Garikipati's innovative work in the field of cardiovascular assessment exemplifies the potential of machine learning and computational modeling in improving healthcare outcomes. His contributions continue to pave the way for advancements in non-invasive diagnostic techniques.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile