Fully Automatic Dual-Probe Lung Ultrasound Scanning Robot for Screening Triage

Two-dimensional lung ultrasound (LUS) has widely emerged as a rapid and noninvasive imaging tool for the detection and diagnosis of coronavirus disease 2019 (COVID-19). However, image differences will be magnified due to changes in ultrasound (US) imaging experience, such as US probe attitude control and force control, which will directly affect the diagnosis results. In addition, the risk of virus transmission between sonographer and patients is increased due to frequent physical contact. In this study, a fully automatic dual-probe US scanning robot for the acquisition of LUS images is proposed and developed. Furthermore, the trajectory was optimized based on the velocity look-ahead strategy, the stability of contact force of the system and the scanning efficiency were improved by 24.13% and 29.46%, respectively. Also, the control ability of the contact force of robotic automatic scanning was 34.14 times higher than that of traditional manual scanning, which significantly improves the smoothness of scanning. Importantly, there was no significant difference in image quality obtained by robotic automatic scanning and manual scanning. Furthermore, the scanning time for a single person is less than 4 min, which greatly improves the efficiency of screening triage of group COVID-19 diagnosis and suspected patients and reduces the risk of virus exposure and spread.

Association Between Lung Ultrasound Patterns and Pneumonia

ABSTRACT

Pneumonia is a common respiratory infection that affects the lungs. Lung ultrasound (LUS) is a portable, cost-effective imaging method, which is free of ionizing radiation and has been shown to be useful for evaluating pneumonia. The aim of this retrospective analytical study was to determine the association between lung ultrasound patterns and pneumonia. For the purpose of performing the required analysis, LUS patterns including consolidations, pleural line irregularities, A lines and B lines from 90 subjects (44 patients with confirmed pneumonia and 46 controls) were retrieved from a published open-access data set, which was reviewed and approved by medical experts. A χ 2 test was used for the comparison of categorical variables to determine the association between each LUS pattern and the presence of pneumonia. There is a significant association between LUS consolidation and the presence of pneumonia ( P < 0.0001). Lung ultrasound A lines are significantly associated with the absence of pneumonia ( P < 0.0001), whereas there are no associations between B lines or pleural line irregularities with pneumonia. Lung ultrasound consolidation is found to be associated with the presence of pneumonia. A lines are associated with healthy lungs, and there is no association of B lines and pleural irregularities with the presence of pneumonia. Further studies investigating LUS patterns with clinical information and symptoms of patients with pneumonia are required.

Evaluation of lung involvement in COVID-19 pneumonia based on ultrasound images

BACKGROUND

Lung ultrasound (LUS) can be an important imaging tool for the diagnosis and assessment of lung involvement. Ultrasound sonograms have been confirmed to illustrate damage to a person's lungs, which means that the correct classification and scoring of a patient's sonogram can be used to assess lung involvement.

METHODS

The purpose of this study was to establish a lung involvement assessment model based on deep learning. A novel multimodal channel and receptive field attention network combined with ResNeXt (MCRFNet) was proposed to classify sonograms, and the network can automatically fuse shallow features and determine the importance of different channels and respective fields. Finally, sonogram classes were transformed into scores to evaluate lung involvement from the initial diagnosis to rehabilitation.

RESULTS AND CONCLUSION

Using multicenter and multimodal ultrasound data from 104 patients, the diagnostic model achieved 94.39% accuracy, 82.28% precision, 76.27% sensitivity, and 96.44% specificity. The lung involvement severity and the trend of COVID-19 pneumonia were evaluated quantitatively.

Lung ultrasound for the diagnosis of pneumonia in adults: A meta-analysis

BACKGROUND

Pneumonia is a common and serious infectious disease that can cause high mortality. The role of lung ultrasound (LUS) in the diagnosis of pneumonia is becoming more and more important.

METHODS

In the present study, we collected existing evidence regarding the use of LUS to diagnose pneumonia in adults and conducted a systematic review to summarize the technique's diagnostic accuracy. We specifically searched the Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, and Embase databases and retrieved outcome data to evaluate the efficacy of LUS for the diagnosis of pneumonia compared with chest radiography or chest computed tomography. The pooled sensitivity (SEN) and specificity (SPE) were determined using the Mantel-Haenszel method, and the pooled diagnostic odds ratio (DOR) was determined using the DerSimonian-Laird method. We also assessed heterogeneity of sensitivity, specificity, and diagnostic odds ratio using the Q and I statistics.

RESULTS

Twelve studies containing 1515 subjects were included in our meta-analysis. The SEN and SPE were 0.88 (95% CI: 0.86-0.90) and 0.86 (95% CI: 0.83-0.88), respectively. The pooled negative likelihood ratio (LR) was 0.13 (95% CI: 0.08-0.23), the positive LR was 5.37 (95% CI: 2.76-10.43), and the DOR was 65.46 (95% CI: 29.24-146.56). The summary receiver operating characteristic curve indicated a relationship between sensitivity and specificity. The area under the curve for LUS was 0.95.

CONCLUSION

LUS can help to diagnose adult pneumonia with high accuracy.