and has rapidly spread worldwide. 1, 2 As of April 25, 2020, more than 2,700,000 confirmed cases and nearly 190,000 COVID-19-related deaths were reported. Viral nucleic acid testing verifies infection of SARS-CoV-2, with varying sensitivities (37-71%) due to differences in specimen type, site and time of collection, and disease course and severity. [3] [4] [5] [6] High-resolution computed tomography (HRCT) has been widely used for COVID-19 diagnosis because of its high spatial resolution.
However, it is potentially harmful as the patient is exposed to ionizing radiation, transporting severe cases is risky, and the disinfection of the scanner is cumbersome. 7, 8 The international consensus on diagnosis and differential diagnosis of lung diseases indicates that bedside ultrasound is an important method of examining and evaluating patients with severe acute illnesses, due to its simplicity of the technique and the absence of ionizing radiations. 9,10 Bedside ultrasound has been proposed as a potential diagnostic tool for COVID-19, based on the disease predilection for subpleural regions. But it has not been implemented widely, because bedside ultrasound requires close proximity between the sonographer and the patient, increasing infection risk. [11] [12] Over the past 20 years, advances in computer networks, multimedia, and communication technologies have led to the development of remote ultrasonic robot technology for clinical applications, and a wealth of experience has been accumulated in abdominal, cardiac, pelvic, obstetric, vascular, and thyroid examinations. [13] [14] [15] [16] [17] [18] [19] [20] [21] The lack of contact requirement and continuous improvements in safety performance have led to its application for examination of patients in isolation wards, as it can help to protect examiners against viral infection. In this study, a 5G-based robot-assisted remote-operated ultrasound system was used for remote examination of COVID-19 patients. We assessed the feasibility of this approach, established an examination and evaluation protocol, and summarized ultrasound characteristics to expand the application of this technology in diagnostics.
The study adhered to the tenets of the Declaration of Helsinki, was approved by the institutional review board of the Zhongnan Hospital of Wuhan University, and was performed with the informed consent of the patients or their families. We retrospectively analyzed a total of 23 patients diagnosed with COVID-19 by nucleic acid testing between March 6 and April 1, 2020, who were hospitalized in the Zhongnan Hospital and were examined using the 5G-based robot-assisted remote ultrasound system. All patients were classified based on the clinical stage determined by the Guidelines for the Diagnosis and Treatment of Novel Coronavirus Pneumonia (Trial version 7) before the study started. 22 The first was the non-severe group who satisfied the diagnostic criteria of fever, respiratory tract symptoms, and imaging manifestations of pneumonia. The second was the severe group who satisfied any one of the following diagnostic criteria: (1) 
A robotic ultrasound system, MGIUS-R3 (MGI Tech Co., Ltd., Shenzhen, China), which integrated robotics, teleoperation, and ultrasound imaging, was used. It could achieve remote robotic control, ultrasound examination, and audio-visual communication. MGIUS-R3 consists of a doctor-side subsystem ( Figure 1A) and a patient-side subsystem (Figure 1B) , which were paired and connected through a 5G network, with a downlink rate of 930 Mbps and an uplink rate of 132 Mbps. The doctor-side subsystem comprised the ultrasound display system, audio-visual communication system, and control system, located at Zhejiang Provincial People's Hospital in Hangzhou, Zhejiang Province.
A senior sonographer with 15-20 years' experience, trained as per standardized examination protocol, operated this subsystem. The patient-side subsystem comprised the ultrasound imaging system, audio-visual communication system, and a robotic arm with six degrees-of-freedoms (DOFs), which located next to the patient's bed in the isolation ward of Zhongnan Hospital of Wuhan University, Hubei Province. The ultrasound imaging system was manufactured by Wisonic Medical Technology Co., Ltd., China, and had a 1.0 -5.5 MHz convex array probe, which was equipped on the robotic arm.
The MGIUS-R3 had multiple protection measures to ensure patient safety: (1) the start reminder was prompted simultaneously on both terminals when the robotic arm started; (2) an emergency stop button was installed next to the ultrasound probe socket of the robotic arm on the patient-side; (3) the robotic arm had speed (≤ 0.675 m/s for the convex array probe and ≤ 0.275 m/s for the linear array probe) and pressure (3-20 N) limit settings, with parameter changes taking effect synchronously; the robotic arm stopped moving once the set value exceeded the standard.
Medical alcohol (75% concentration) kills SARS-CoV-2 and evaporates quickly, and was thus used to disinfect the patient-side instrument surface. It was the first choice in disinfectant, although it would damage the machine. Then, the surface residues were removed with disinfectant paper. The probe was disinfected once per patient. If it contacted mucous membranes or wounds, a medical ultrasonic coupling agent with a disinfection function or special disinfectant was used. Finally, the instrument was disinfected regularity with ultraviolet light once daily for 1 h each time.
The robot-assisted remote ultrasound examination was performed for COVID-19 patients as follows.
First, the clinician initiated a consultation request through the remote system after a comprehensive evaluation of COVID-19 patients in the isolated wards. The patient information was registered in the system at the same time.
Second, the remote consultation expert switched on the doctor-side subsystem, and ensured 5G network connection and patient-side subsystem recognition. Through the audio-visual communication system, the expert guided the patient to adopt the appropriate examination posture.
Then, the expert started the robotic arm, performed the examination of the lungs and hearts of the COVID-19 patient, and saved the ultrasound images or videos. The probe scanned vertically along the intercostal space in the following order: inside to outside, top to bottom, and front to back. Each lung was divided into upper and lower parts, totaling 12 zones, using the parasternal, front axillary, posterior axillary, and paraspinal lines as boundaries. 23, 24 The 12 zones were marked as R or L, for right or left, respectively, with corresponding numbers, for easy recording and data analysis ( Figure   2 ). Multipoint examination was performed for each zone. Cardiac ultrasound evaluation was simultaneously performed using the left ventricular short-axis view.
Finally, patient data, such as patient characteristics, medical history, clinical manifestations, and laboratory markers, were presented after examination completion. After storing and analyzing the examination results, the expert issued a diagnostic report to the attending clinicians to guide further treatment.
An assistant was needed during the exam, who assisted in disinfecting the instrument, guiding patients to check, applying medical coupling agents, and adjusting the position of severely ill patients appropriately.
The acquired ultrasound images included the following information 25 Lung ultrasound characteristics were classified into four categories (N, B1, B2, and C), and scored 0-3 points (Figure 4 ). [27] [28] [29] [30] The worst ultrasound characteristic was considered representative of a particular lung zone and given the corresponding score. 31, 32 Scores of the 12 examined zones were summed to calculate the lung ultrasound score (LUS), ranging from 0 to 36. LUS videos were analyzed offline by two sonographers, who were blinded to the clinical data and each other's ultrasound diagnoses.
Statistical analyses were performed using IBM Statistical Package for Social Sciences software (version 23; IBM, Armonk, NY). Continuous variables are presented as mean±standard deviation (normal distribution) or median and interquartile range (non-normal distribution). Categorical variables are presented as percentages of the total. Prevalence data are reported using the 95% confidence interval. Comparisons between groups are made using either parametric t-tests or nonparametric Mann-Whitney U-tests.The statistical significance of differences among categorical variables was determined using the chi-square test or Fisher's exact test. P < 0.05 was defined as statistically significant.
Of the twenty-three patients, twelve were non-severe cases, four males and eight females, with a mean age of 56.6 years (SD 12.0), and eleven were severe cases, eight males and three females, with a mean age of 70.6years (SD 12.0) ( Table 1) . There were 40 different comorbidities ( Table 1) . Mean oxygen saturation was lower in severe than non-severe cases (mean ± SD, 93.0% ± 1.9 vs. 99.8% ± 0.6, P < 0.05) ( Table 1) .
On admission, all patients had a white blood cell count in the normal range. Neutrophils were mostly in the normal range. Almost half of the severe patients had a below-normal eosinophil count, and more than half of them had a below-normal lymphocyte count. A few non-severe and more than half of severe patients had cardiac markers exceeding the normal range. A quarter of non-severe and all severe patients had C-reactive protein above the normal range. Almost half the non-severe and almost three-quarters of the severe patients had an erythrocyte sedimentation rate above normal. Few patients in both groups had excessive procalcitonin levels. A quarter of non-severe and almost three-quarters of severe patients had excessive cytokine levels ( Table 1) .
Differences in age, oxygen saturation, eosinophils, lymphocytes, cardiac markers, CRP, and cytokines between the groups were statistically significant (P < 0.05; Table 1 ).
A standard examination protocol was established ( Figure 5) . According to the protocol, a cardiopulmonary assessment was completed successfully for all patients using the 5G-based robot-assisted remote ultrasound system. Each examination took 10-20 minutes, on average, and there was no noticeable delay in scanning. We obtained ultrasound image information, such as distribution characteristics, morphology of the lungs and surrounding tissue lesions, LVEF, RVEDA/LVEDA, pericardial and pleural effusion, and LUS. No patient had significant examination-related complications.
Peripheral lung lesions could be evaluated clearly and effectively using ultrasonography ( Table 2) . The LVEF and ventricular area ratio of the heart were normal in all 23 cases. Four severe cases (36.4%, 95%CI, 15.2-64.6%) were complicated by pericardial effusions 3-10mm wide (vs 0% of the non-severe cases, P < 0.05) (Figure 7 ).
Cardiopulmonary assessment was successfully and safely completed in all COVID-19 patients using the 5G-based robot-assisted remote ultrasound system. Image acquisition, labeling, and analysis were performed as per established examination protocol, without complications. This underscores that the advances and safety of current computer networks and communication technology allows long-distance ultrasonic image acquisition, transmission, analysis, and processing, with high-precision synchronization of multiple audio-visual signals. It can assist in the construction of remote real-time ultrasound collaborations, interactive operability, and consultation modes. Current ultrasonic robot technology has made considerable advances, and rich experience has been accumulated through many clinical applications. [13] [14] [15] [16] [17] [18] [19] [20] [21] 33 Studies have indicated that the quality of images captured by robot-assisted remote ultrasound systems correlate well with those captured by conventional ultrasound. [34] [35] [36] Robot technology has been reported for assistance in the diagnosis and treatment of lung diseases previously, [37] [38] [39] but the application of a 5G-based robot-assisted remote ultrasound system for use in lung disorders has not been reported to date. 5G networks have a high data transmission rate (peak rate up to 20 Gbps) and low network delay (approximately 1-10 ms). Consequently, no noticeable delay occurred during scanning, and each examination was completed quickly, facilitating further clinical implementation of 5G-based robot-assisted remote ultrasound system. 40. In addition, we used strict infection control practices, including the hand hygiene of assistants, cleaning and disinfection of the floor, object surfaces, and the patient-side instrument. In summary, the application of this system allowed us to surpass time and space restrictions and guaranteed minimization of cross-infection risk in the assessment of COVID-19 patients.
HRCT is widely used for COVID-19 diagnosis, offering advantages of high spatial resolution and multiplanar and multidirectional display of lesion details, but also has unavoidable disadvantages, such as the potential harm of ionizing radiations, the risk of critically ill patients transferring. patients, CT shows diffuse lung lesions, with extensive exudation and lung consolidation mainly in the lower lobe, along with pleural effusion. 41, 42 The accumulated CT diagnostic experience and lesion distribution characteristics provided a theoretical and technical reference for the application of lung ultrasound in COVID-19. 43 Ultrasonography has advantages of convenience and dynamics. 44 It facilitates the diagnosis of lung diseases, rapid confirmation of acute respiratory failure causes, shock classification, qualitative assessment of pleural effusion (free or wrapped), and dynamic monitoring of diaphragm activity to predict offline extubation success. 9-10 Studies have shown that COVID-19 patients have characteristic lung ultrasound manifestations, such as coalescent B lines and subpleural lung consolidation, mostly in the posterior and lower parts of the lung. 12 Consequently, lung ultrasound has played a potential role in the COVID-19 epidemic.
Additionally, the rapid development of remote ultrasonic and 5G communication technology has improved the feasibility of using a 5G-based robot-assisted remote ultrasound system in COVID-19 diagnosis. In this retrospective ultrasound study, B lines, lung consolidation or atelectasis were mainly distributed in the lung periphery, and significantly in the dorsal lung region, consistent with reported CT results. 45 The number of diseased lung regions, incidence and number of B lines, and incidence of lung consolidation were significantly higher in the severe than the non-severe group.
Ultrasound found no abnormalities in the lungs of four non-severe cases. One non-severe case and three severe cases had pleural effusion. Thus, ultrasonography might be helpful for evaluating COVID-19 severity, and the 5G-based robot-assisted remote ultrasound system could achieve the same effect as a face-to-face, close-range ultrasound examination.
The LUS was used for overall lung evaluation of COVID-19 patients in our study. The LUS was significantly higher in the severe than in the non-severe group, probably because vascular and inflammatory reactions were more exaggerated in severe cases, causing some bronchial embolisms to block the bronchiole and terminal bronchiole partially or completely, triggering lung atelectasis or consolidation, eventually leading to ventilation dysfunction. [46] [47] [48] [49] [50] Current literature 29 indicates that, in a controlled human model of lung air content variation, the LUS can reliably record lung aeration changes. This method was successfully applied to assess extravascular lung water (EVLW), which predicts mechanical ventilation weaning failure and allows monitoring aeration in patients undergoing extracorporeal membrane oxygenation. The LUS is closely related to several acute respiratory distress syndrome diagnostic and prognostic indices, such as the EVLW index, lung injury score, respiratory system compliance, and PaO2/FiO2, and serves as a death-risk prediction index. 41, 42, 51 . In this study, one non-severe case (8.3%, 1/12) and four severe cases (36.4%, 4/11) also had pulmonary bacterial infections, with LUSs of 14 and 4-29, respectively. Compared with COVID-19 patients with non-pulmonary bacterial infections, these patients might have displayed aggravated lung lesions, but this requires further research. Some studies 51 have shown that COVID-19 patients with comorbidities have significantly higher risks of ICU admission, invasive ventilation, and death than that in patients without comorbidities, with risk increasing proportionately to the number of comorbidities.
The 5G-based robot-assisted remote ultrasound system was also used for cardiac examination in this study, although it was limited by the patient's position, probe selection (only a 1.0-5.5-MHz convex array probe), and the robotic arm's operating angle. The left ventricular short-axis view offered the only clear view in this study that could be used to assess the size and function of the ventricle.We measured the areas of the right and left ventricles at end-diastole and calculated the area ratio (RVEDA/LVEDA). When the ratio is greater than 0.6, it indicates that the right ventricle is dilated (i.e., potential impaired function). When accompanied by contradictory interventricular septal movements, this indicates pulmonary heart disease. Moreover, we used the'eyeballing' visual estimation method to quickly assess the LVEF and left ventricular wall motion. Studies have shown that this method has a good correlation with radionuclide scanning and other quantitative methods.
As this is readily and quickly performed, it could be used in routine echocardiography. The examination results could help to rule out lung diseases caused by cardiogenic factors (e.g., cardiogenic pulmonary edema), clarify the cause and classification of shock, and assess fluid responsiveness. [52] [53] [54] Some COVID-19 patients have obvious cardiac dysfunction, but all our patients had normal RVEDA/LVEDA and LVEF, perhaps due to the limited viral damage to myocardial cells or damage control by early treatment. 45, 46, 55 Four severe COVID-19 patients (36.4%, 4/11) had pericardial effusions (3-10-mm wide), and one of these had increased α-hydroxybutyrate dehydrogenase (291 U/L) and lactic acid dehydrogenase (375 U/L). There were no such cases in the non-severe group; thus, the underlying mechanism requires investigation.
In conclusion, the 5G-based robot-assisted remote ultrasound system offers a feasible option for cardiopulmonary evaluation of COVID-19 patients. The establishment of an examination protocol helped in performing standardized examinations, as well as in the learning, training, and promotion of the technology. But, the study had some limitations. Since we conducted ultrasound examination for COVID-19 patients only at a specific disease stage, dynamic evaluation of disease progression was not possible. Hence, further follow-up ultrasound data must be collected, and their correlations with clinical findings should be analyzed to observe the evolution and outcome of the disease. Lung diseases of different etiologies can have similar ultrasound characteristics; consequently, this method can not be used to determine etiology. Differences in age, oxygen saturation, eosinophils, lymphocytes, cardiac markers, CRP, and cytokines between the two groups were statistically significant (P < 0.05). Therefore, a comprehensive assessment needs to be conducted in conjunction with the medical history, clinical manifestation, and laboratory test data. In addition, the 5G-based robot-assisted remote ultrasound system is still in its infancy, and requires further improvements: (1) Restrictions of the examination position of the patient (especially, critically ill patients) and operating angle of the robotic arm made some body parts difficult for the robotic arm to reach. The use of only one convex array probe markedly affects the quality of cardiac images, due to the frequency limitation. Although the initial application of the 5G-based robot-assisted remote ultrasound system in the COVID-19 epidemic has achieved good results, it can not wholly replace CT and other examinations.
With the in-depth application of artificial intelligence in the medical field, integrating artificial intelligence in robot-assisted remote ultrasound systems would greatly increase the scope of use for this technology, facilitate the diagnosis of lung lesions objectively, and accurately, and implement automatic switching between probes on the ultrasonic robot system to facilitate optimal imaging of multiple organs and improve image quality. 56 The pleural line is thick and rough (red arrows) and has a score of two. C is characterized by tissue echogenicity (green circle), known as pulmonary consolidation. The pleural line is broken (red arrows) and has a score of three. Data are presented as number of patients (%) or mean ± SD unless otherwise indicated. *P < 0.05
Cardiac markers include AST, CK, CK-MB, α-HBDH, LDH, HSTNI, and ProBNP. An increase in any indicator indicates the abnormality of the cardiac marker.
Cytokine includes IFN-γ, IL2, IL4, IL6, and IL10. An increase in any indicator indicates abnormality of the cytokine.