A case of pulmonary edema: The critical role of lung-heart integrated ultrasound examination

Lung Ultrasound in Coronary Care Unit, an Important Diagnostic Tool for Concomitant Pneumonia

Background: In the setting of a coronary care unit (CCU), the early detection of pneumonia is of paramount important to prevent severe complications. This study was designed aiming to evaluate the diagnostic accuracy of lung ultrasound (LUS) in the detection of pneumonia and compared with chest X-ray (CXR). Method: We enrolled 110 consecutive patients admitted to the CCU of Federico II University Hospital. Each patient underwent CXR and bedside LUS on admission. The final diagnosis (pneumonia vs. no pneumonia) was established by another clinician reviewing clinical and laboratory data independent of LUS results and possibly prescribing chest contrast-enhanced CT (n = 34). Results: The mean age was 70 ± 11 years old, and 68% were males. Pneumonia was clinically diagnosed in 26 (23%) patients. LUS was positive for pneumonia in 24 patients (sensitivity 92%, specificity 81%). Chest radiography was positive for pneumonia in nine patients (sensitivity 43%, specificity 95%). Using CT scan as a reference, LUS exhibited 92% sensitivity and a specificity of 96%. In ROC curve analysis, the diagnostic accuracy of CXR and LUS for the diagnosis of pneumonia was 0.86 (95% CI 0.77−0.94), which was higher than CXR 0.68 (95% CI 0.55−0.84), p < 0.05. Conclusion: Based on the findings of the present study, the accuracy of LUS in the detection of pneumonia was significantly higher than chest X-ray with comparable sensibility to CT scan.

Lung ultrasound: a narrative review and proposed protocol for patients admitted to Cardiac Rehabilitation Unit

Lung ultrasonography (LUS) has become in the last 10 years a technique that has reduced the need of second level diagnostic methods such as chest X-ray (CXR) and computerize tomography (CT) for the diagnostic imaging of lung and pleural space, throughout its diagnostic accuracy, radiation free, low cost, real time and bedside approach. The common use of LUS has been recently extend to cardiac and pulmonary disease even in context of Cardiac Rehabilitation Unit and it could be an additional tool for physiotherapist for the management of patients during Rehabilitation course. The authors performed a literature review in PubMed and suggested a new standardize protocol for LUS, based on guidelines and expert consensus document, for patients admitted to Cardiac Rehabilitation Unit. In this protocol, LUS should be performed in six scan each hemithorax, covering twelve imagine regions. For each scan will be noted a specific physiologic or pathological patterns. Furthermore, we suggest for each patient, the use of the Lung Ultrasound Score (LUS score) to obtain a global view of lung aeration and to monitor any changes during the hospitalization. An increase in score range indicates a more severe condition. This Lung Ultrasonography Protocol should be performed in all patients at the time of admission to Cardiac Rehabilitation Unit to monitoring the aeration of the lungs and the possible lung and/or pleura complications after a cardiac disease avoiding the use of second level surveys.

Deep learning based genome analysis and NGS-RNA LL identification with a novel hybrid model

The conventional image segmentation techniques have a lot of issues with highest computational cost and low level accuracy for medical image diagnosis and genome analysis. The deep learning based optimization models utilize to predict the liver cancer with RNA genome using CT images and the prediction of genome classification with NGS is a higher probable in recent medical disease classification. This paper proposes a hybrid deep learning technique constructs with SegNet, MultiResUNet, and Krill Herd optimization (KHO) algorithm to perform the extraction of the liver lesions and RNA sequencing that the optimization techniques used into the deep learning method. The proposed technique implements the SegNet for segregating the liver with genome from the CT scan; the MultiResUNet is constructed to perform the extractions of liver lesions. The KHO algorithm is combined with the deep learning approaches for tuning the hyper parameters to every Convolutional neural network model and enhances the segmentation process which may elaborately identifies the sequence that causes the liver classification disease. The proposed technique is compared with the related techniques on liver lesion classification (LL) for NGS in genome. The performance results show that the proposed technique is better to other algorithms on various performance metrics.

Point-of-care ultrasound with pocket-size devices in emergency department

Point-of-care ultrasound is a useful tool for clinicians in the management of patients. Particularly in emergency department, the role of point-of-care ultrasound is strongly increasing due to the need for a rapid assessment of critically ill patients and to speed up the diagnostic process. Hand-carried ultrasound devices are particularly useful in emergency setting and allow rapid assessment of patient even in prehospital setting. This article will review the role of point-of-care ultrasonography, performed with pocket-size devices, in the management of patients presenting with acute onset of undifferentiating dyspnea, chest pain, and shock in emergency department.