Autonomous Robotic Point-of-Care Ultrasound Imaging for Monitoring of COVID-19-Induced Pulmonary Diseases
Bagherzadeh, Pooyan Sahbaee
JournalFrontiers in Robotics and AI
PublisherFrontiers Media S.A.
MetadataShow full item record
AbstractThe COVID-19 pandemic has emerged as a serious global health crisis, with the predominant morbidity and mortality linked to pulmonary involvement. Point-of-Care ultrasound (POCUS) scanning, becoming one of the primary determinative methods for its diagnosis and staging, requires, however, close contact of healthcare workers with patients, therefore increasing the risk of infection. This work thus proposes an autonomous robotic solution that enables POCUS scanning of COVID-19 patients’ lungs for diagnosis and staging. An algorithm was developed for approximating the optimal position of an ultrasound probe on a patient from prior CT scans to reach predefined lung infiltrates. In the absence of prior CT scans, a deep learning method was developed for predicting 3D landmark positions of a human ribcage given a torso surface model. The landmarks, combined with the surface model, are subsequently used for estimating optimal ultrasound probe position on the patient for imaging infiltrates. These algorithms, combined with a force–displacement profile collection methodology, enabled the system to successfully image all points of interest in a simulated experimental setup with an average accuracy of 20.6 ± 14.7 mm using prior CT scans, and 19.8 ± 16.9 mm using only ribcage landmark estimation. A study on a full torso ultrasound phantom showed that autonomously acquired ultrasound images were 100% interpretable when using force feedback with prior CT and 88% with landmark estimation, compared to 75 and 58% without force feedback, respectively. This demonstrates the preliminary feasibility of the system, and its potential for offering a solution to help mitigate the spread of COVID-19 in vulnerable environments. © Copyright © 2021 Al-Zogbi, Singh, Teixeira, Ahuja, Bagherzadeh, Kapoor, Saeidi, Fleiter and Krieger.
Rights/TermsCopyright © 2021 Al-Zogbi, Singh, Teixeira, Ahuja, Bagherzadeh, Kapoor, Saeidi, Fleiter and Krieger.
Keyword3D deep convolutional network
3D landmark estimation
Identifier to cite or link to this itemhttp://hdl.handle.net/10713/16009
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