Deep Learning-Based Assessment of Coronary Artery Morphologies for Predicting Responsiveness to Intravascular Lithotripsy.

Yiqing Liu¹, Farhad R Nezami², Elazer R Edelman³

¹Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. Electronic address: lyiqing@mit.edu.

²Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; Division of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

³Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

PubMed DOI

Abstract

BACKGROUND: Although intravascular lithotripsy (IVL) has proven effective in facilitating luminal expansion through calcification modification, its effectiveness in luminal expansion across different coronary morphologies remains poorly understood. OBJECTIVES: The authors sought to identify predictive lumen and calcium morphologies for luminal expansion following IVL through artificial intelligen…

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