Quantitative assessment of COVID‐19 pneumonia in neonates using lung ultrasound score

Li, Wei; Fu, Manli; Qian, Chao; Liu, Xin; Zeng, Lingkong; Peng, Xuehua; Hong, Yoo Jin; Zhou, Huan; Li, Yuan

doi:10.1002/ppul.25325

Cited by 10 publications

(25 citation statements)

References 20 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most common ultrasonographic changes found in the analyzed studies were the variations of physiological A-lines from decrease to disappearance, varying between 62.8% and 100% [ 17 , 18 , 22 ]. A-lines are described as hyperechogenic, horizontal, and equidistant ones that are parallel to the pleura [ 6 ].…”

Section: Resultsmentioning

confidence: 99%

“…Sparse B-lines were the next most frequent modification with an occurrence rate between 55.3% and 100%, reported in four reviewed studies [ 17 , 18 , 19 , 22 ].…”

Section: Resultsmentioning

confidence: 99%

“…Abnormal pleural lines, such as thickening, interrupted, or irregular pleural lines, were reported differently in the referenced studies, ranging between 21.9% and 100% [ 17 , 18 , 19 , 22 ].…”

Section: Resultsmentioning

confidence: 99%

“…Subpleural consolidation, recognized as a sign of viral pneumonia in association with variation of B-lines, were reported only in a few cases (between 1.5% and 66.6%) in the lung ultrasound exam in the target group [ 17 , 18 , 19 , 20 , 21 , 22 ]. No pleural effusion or pneumothorax was reported.…”

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Is Lung Ultrasound Helpful in COVID-19 Neonates?—A Systematic Review

et al. 2021

View full text Add to dashboard Cite

Background: The SARS-CoV-2 infection has occurred in neonates, but it is a fact that radiation exposure is not recommended given their age. The aim of this review is to assess the evidence on the utility of lung ultrasound (LUS) in neonates diagnosed with COVID-19. Methods: A systematic literature review was performed so as to find a number of published studies assessing the benefits of lung ultrasound for newborns diagnosed with COVID and, in the end, to make a comparison between LUS and the other two more conventional procedures of chest X-rays or CT exam. The key terms used in the search of several databases were: “lung ultrasound”, “sonography”, “newborn”, “neonate”, and “COVID-19′. Results: In total, 447 studies were eligible for this review, and after removing the duplicates, 123 studies referring to LU were further examined, but only 7 included cases of neonates. These studies were considered for the present research paper. Conclusions: As a non-invasive, easy-to-use, and reliable method for lung lesion detection in neonates with COVID-19, lung ultrasound can be used as a useful diagnosis tool for the evaluation of COVID-19-associated lung lesions. The benefits of this method in this pandemic period are likely to arouse interest in opening new research horizons, with immediate practical applicability.

show abstract

Section: Resultsmentioning

confidence: 99%

“…Sparse B-lines were the next most frequent modification with an occurrence rate between 55.3% and 100%, reported in four reviewed studies [ 17 , 18 , 19 , 22 ].…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Is Lung Ultrasound Helpful in COVID-19 Neonates?—A Systematic Review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The most common sign was represented by little subpleural consolidations, <1 cm. A similar score was used to assess COVID-19 affected neonates, showing a good correlation with the clinical severity of the disease [77]. The most common pathological LUS findings in this group of patients were B-lines abnormalities.…”

Section: Lus In Covid-19mentioning

confidence: 97%

Usefulness of Lung Ultrasound in Paediatric Respiratory Diseases

et al. 2021

View full text Add to dashboard Cite

Respiratory infection diseases are among the major causes of morbidity and mortality in children. Diagnosis is focused on clinical presentation, yet signs and symptoms are not specific and there is a need for new non-radiating diagnostic tools. Among these, lung ultrasound (LUS) has recently been included in point-of-care protocols showing interesting results. In comparison to other imaging techniques, such as chest X-ray and computed tomography, ultrasonography does not use ionizing radiations. Therefore, it is particularly suitable for clinical follow-up of paediatric patients. LUS requires only 5–10 min and allows physicians to make quick decisions about the patient’s management. Nowadays, LUS has become an early diagnostic tool to detect pneumonia during the COVID-19 pandemic. In this narrative review, we show the most recent scientific literature about advantages and limits of LUS performance in children. Furthermore, we discuss the major paediatric indications separately, with a paragraph fully dedicated to COVID-19. Finally, we mention potential future perspectives about LUS application in paediatric respiratory diseases.

show abstract

Automatic detection of A‐line in lung ultrasound images using deep learning and image processing

Xing

et al. 2022

Medical Physics

View full text Add to dashboard Cite

Background Auxiliary diagnosis and monitoring of lung diseases based on lung ultrasound (LUS) images is important clinical research. A‐line is one of the most common indicators of LUS that can offer support for the assessment of lung diseases. A traditional A‐line detection method mainly relies on experienced clinicians, which is inefficient and cannot meet the needs of these areas with backward medical level. Therefore, how to realize the automatic detection of A‐line in LUS image is important. Purpose In order to solve the disadvantages of traditional A‐line detection methods, realize automatic and accurate detection, and provide theoretical support for clinical application, we proposed a novel A‐line detection method for LUS images with different probe types in this paper. Methods First, the improved Faster R‐CNN model with a selection strategy of localization box was designed to accurately locate the pleural line. Then, the LUS image below the pleural line was segmented for independent analysis excluding the influence of other similar structures. Next, image‐processing methods based on total variation, matched filter, and gray difference were applied to achieve the automatic A‐line detection. Finally, the “depth” index was designed to verify the accuracy by judging whether the automatic measurement results belong to corresponding manual results (±5%). In experiments, 3000 convex array LUS images were used for training and validating the improved pleural line localization model by five‐fold cross validation. 850 convex array LUS images and 1080 linear array LUS images were used for testing the trained pleural line localization model and the proposed image‐processing‐based A‐line detection method. The accuracy analysis, error statistics, and Harsdorff distance were employed to evaluate the experimental results. Results After 100 epochs, the mean loss value of training and validation set of improved Faster R‐CNN model reached 0.6540 and 0.7882, with the validation accuracy of 98.70%. The trained pleural line localization model was applied in the testing set of convex and linear probes and reached the accuracy of 97.88% and 97.11%, respectively, which were 3.83% and 8.70% higher than the original Faster R‐CNN model. The accuracy, sensitivity, and specificity of A‐line detection reached 95.41%, 0.9244%, 0.9875%, and 94.63%, 0.9230%, and 0.9766% for convex and linear probes, respectively. Compared to the experienced clinicians’ results, the mean value and p value of depth error were 1.5342 ± 1.2097 and 0.9021, respectively, and the Harsdorff distance was 5.7305 ± 1.8311. In addition, the accumulated accuracy of the two‐stage experiment (pleural line localization and A‐line detection) was calculated as the final accuracy of the whole A‐line detection system. They were 93.39% and 91.90% for convex and linear probes, respectively, which were higher than these previous methods. Conclusions The proposed method combining image processing and deep learning can automatically and accurately detect A‐line in LUS ima...

show abstract

Quantitative assessment of COVID‐19 pneumonia in neonates using lung ultrasound score

Cited by 10 publications

References 20 publications

Is Lung Ultrasound Helpful in COVID-19 Neonates?—A Systematic Review

Is Lung Ultrasound Helpful in COVID-19 Neonates?—A Systematic Review

Usefulness of Lung Ultrasound in Paediatric Respiratory Diseases

Automatic detection of A‐line in lung ultrasound images using deep learning and image processing

Contact Info

Product

Resources

About