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Coronary vessel detection methods for organ‐mounted robots
Author(s) -
Rasmussen Eric T.,
Shiao Eric C.,
Zourelias Lee,
Halbreiner Michael S.,
Passineau Michael J.,
Murali Srinivas,
Riviere Cameron N.
Publication year - 2021
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.2297
Subject(s) - imaging phantom , computer science , convolutional neural network , medicine , coronary arteries , biomedical engineering , ultrasound , artificial intelligence , cardiology , radiology , artery
Background HeartLander is a tethered robot walker that utilizes suction to adhere to the beating heart. HeartLander can be used for minimally invasive administration of cardiac medications or ablation of tissue. In order to administer injections safely, HeartLander must avoid coronary vasculature. Methods Doppler ultrasound signals were recorded using a custom‐made cardiac phantom and used to classify different coronary vessel properties. The classification was performed by two machine learning algorithms, the support vector machines and a deep convolutional neural network. These algorithms were then validated in animal trials. Results Accuracy of identifying vessels above turbulent flow reached greater than 92% in phantom trials and greater than 98% in animal trials. Conclusions Through the use of two machine learning algorithms, HeartLander has shown the ability to identify different sized vasculature proximally above turbulent flow. These results indicate that it is feasible to use Doppler ultrasound to identify and avoid coronary vasculature during cardiac interventions using HeartLander.