Accuracy of IVUS-Based Machine Learning Segmentation Assessment of Coronary Artery Dimensions and Balloon Sizing.

Mitsuaki Matsumura^1,2, Gary S Mintz¹, Tomotaka Dohi², Wenguang Li³, Alexander Shang³, Khady Fall⁴, Takao Sato^1,4, Yoichiro Sugizaki^1,4, Yiannis S Chatzizisis⁵, Jeffery W Moses^1,4, Ajay J Kirtane^1,4, Hajime Sakamoto⁶, Hiroyuki Daida⁶, Tohru Minamino², Akiko Maehara^1,4

¹Clinical Trial Center, Cardiovascular Research Foundation, New York, New York, USA.

²Department of Cardiovascular Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan.

³Boston Scientific Corporation, Maple Grove, Minnesota, USA.

⁴NewYork-Presbyterian Hospital, Columbia University Irving Medical Center, New York, New York, USA.

⁵Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA.

⁶Department of Radiology Technology, Juntendo University Faculty of Health Science, Tokyo, Japan.

PubMed PMC DOI PDF

Abstract

BACKGROUND: Accurate intravascular ultrasound (IVUS) measurements are important in IVUS-guided percutaneous coronary intervention optimization by choosing the appropriate device size and confirming stent expansion. OBJECTIVES: The purpose of this study was to assess the accuracy of machine learning (ML) automatic segmentation of coronary artery vessel and lumen dimensions and balloon sizing. MET…

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