Abstract
The 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.
Highlights
The COVID-19 pandemic has emerged as a serious global health crisis, with the primary morbidity and mortality linked to pulmonary involvement
Two of the patients were positive for COVID-19 and exhibited significant infiltrate formation in their lungs, whereas the last patient was healthy with no lung abnormalities
We addressed the issue of potentially unavailable prior CT scans by developing a deep learning model for ribcage landmarks estimation
Summary
The COVID-19 pandemic has emerged as a serious global health crisis, with the primary morbidity and mortality linked to pulmonary involvement. Prompt and accurate diagnostic assessment is crucial for understanding and controlling the spread of the disease, with Point-of-Care Ultrasound scanning (POCUS) becoming one of the primary determinative methods for its diagnosis and staging (Buda et al, 2020). Tele-operated solutions allow medical experts to remotely control the positioning of an ultrasound (US) probe attached to a robotic system, reducing the distance between medical personnel and patients to a safer margin. Ye et al (2020) developed a 5G-based robotassisted remote US system for the assessment of the heart and lungs of COVID-19 patients, whereby the system successfully evaluated lung lesions and pericardial effusions in patients with varying levels of disease progression. The system could detect left ventricular systolic function, as well as other complications such as venous thrombosis (Wang et al, 2020). Yang et al (2020) developed a tele-operated system that, in addition to performing robotized US, is capable of medicine delivery, operation of medical instruments, and extensive disinfection of high-touch surfaces
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