https://doi.org/10.1016/j.talanta.2022.123409

Review of COVID-19 testing and diagnostic methods

Following the innovations and new discoveries of the last 10 years in the field of lung ultrasound (LUS), a multidisciplinary panel of international LUS experts from six countries and from different fields (clinical and technical) reviewed and updated the original international consensus for point‐of‐care LUS, dated 2012. As a result, a total of 20 statements have been produced. Each statement is complemented by guidelines and future developments proposals. The statements are furthermore classified based on their nature as technical (5), clinical (11), educational (3), and safety (1) statements.

New International Guidelines and Consensus on the Use of Lung Ultrasound

Abstract In early March 2020, the World Health Organization (WHO) proclaimed the novel COVID-19 as a global pandemic. The coronavirus went on to be a life-threatening infection and is still wreaking havoc all around the globe. Though vaccines have been rolled out, a section of the population (the elderly and people with comorbidities) still succumb to this deadly illness. Hence, it is imperative to diagnose this infection early to prevent a potential severe prognosis. This contagious disease is usually diagnosed using a conventional technique called the Reverse Transcription Polymerase Chain Reaction (RT-PCR). However, this procedure leads to a number of wrong and false-negative results. Moreover, it might also not diagnose the newer variants of this mutating virus. Artificial Intelligence has been one of the most widely discussed topics in recent years. It is widely used to tackle various issues across multiple domains in the modern world. In this extensive review, the applications of Artificial Intelligence in the detection of coronavirus using modalities such as CT-Scans, X-rays, Cough sounds, MRIs, ultrasound and clinical markers are explored in depth. This review also provides data enthusiasts and the broader health community with a complete assessment of the current state-of-the-art approaches in diagnosing COVID-19. The key issues and future directions are also provided for upcoming researchers. 

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Diagnosing COVID-19 using artificial intelligence: a comprehensive review

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https://doi.org/10.1016/j.bspc.2022.103561

Automated lung ultrasound scoring for evaluation of coronavirus disease 2019 pneumonia using two-stage cascaded deep learning model

As being radiation-free, portable, and capable of repetitive use, ultrasonography is playing an important role in diagnosing and evaluating the COVID-19 Pneumonia (PN) in this epidemic By virtue of lung ultrasound scores (LUSS), lung ultrasound (LUS) was used to estimate the excessive lung fluid that is an important clinical manifestation of COVID-19 PN, with high sensitivity and specificity However, as a qualitative method, LUSS suffered from large interobserver variations and requirement for experienced clinicians Considering this limitation, we developed a quantitative and automatic lung ultrasound scoring system for evaluating the COVID-19 PN A total of 1527 ultrasound images prospectively collected from 31 COVID-19 PN patients with different clinical conditions were evaluated and scored with LUSS by experienced clinicians All images were processed via a series of computer-aided analysis, including curve-to-linear conversion, pleural line detection, region-of-interest (ROI) selection, and feature extraction A collection of 28 features extracted from the ROI was specifically defined for mimicking the LUSS Multilayer fully connected neural networks, support vector machines, and decision trees were developed for scoring LUS images using the fivefold cross validation The model with $128\times256$ two fully connected layers gave the best accuracy of 87% It is concluded that the proposed method could assess the ultrasound images by assigning LUSS automatically with high accuracy, potentially applicable to the clinics

/pdf/quantitative-analysis-and-automated-lung-ultrasound-scoring-38j33vufsh.pdf

Quantitative Analysis and Automated Lung Ultrasound Scoring for Evaluating COVID-19 Pneumonia With Neural Networks

The morbidity and mortality of the fetus is related closely with the neonatal respiratory morbidity, which was caused by the immaturity of the fetal lung primarily. The amniocentesis has been used in clinics to evaluate the maturity of the fetal lung, which is invasive, expensive and time-consuming. Ultrasonography has been developed to examine the fetal lung quantitatively in the past decades as a non-invasive method. However, the contour of the fetal lung required by existing studies was delineated in manual. An automated segmentation approach could not only improve the objectiveness of those studies, but also offer a quantitative way to monitor the development of the fetal lung in terms of morphological parameters based on the segmentation. In view of this, we proposed a deep learning model for automated fetal lung segmentation and measurement. The model was constructed based on the U-Net. It was trained by 3500 data sets augmented from 250 ultrasound images with both the fetal lung and heart manually delineated, and then tested on 50 ultrasound data sets. With the proposed method, the fetal lung and cardiac area were automatically segmented with the accuracy, average IoU, sensitivity and precision being 0.98, 0.79, 0.881 and 0.886, respectively.

/pdf/ultrasonographic-segmentation-of-fetal-lung-with-deep-1v2fq05yq0.pdf

Ultrasonographic Segmentation of Fetal Lung with Deep Learning

As two important features of COVID-19 pneumonia ultrasound, the B-line and white lung are easily confused in clinics. To classify the two features, a radiomics analysis technology was developed on a set of ultrasound images collected from patients with COVID-19 pneumonia in the study. A total of 540 filtered images were divided into a training set and a test set in the ratio of 7:3. A machine learning model was proposed to perform automated classification of the B-line and white lung, which included image segmentation, feature extraction, feature screening, and classification. The radiomic analysis was applied to extract 1688 high-throughput features. The principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) were used to perform feature screening for redundancy reduction. The support vector machine (SVM) was utilized to make the final classification. The confusion matrix was used to visualize the prediction performance of the model. In the result, the model with features selected using LASSO outperformed the model with PCA in terms of classification effectiveness. The number of high-throughput features closely related to the classification under the model with LASSO was 11, with the value of AUC, accuracy, specificity, precision and recall being 0.92, 0.92, 0.91, 0.92 and 0.92, respectively. Compared to the model with PCA, the values of the evaluation indicators of the model with LASSO increased by 13.94%, 13.26%, 15.79%, 22.23% and 5.66%, respectively. As a conclusion, the proposed models showed good performance in differentiation of the B-line and white lung, with potential application value in the clinics.

Yucheng Cao

Papers

Quantitative Analysis and Automated Lung Ultrasound Scoring for Evaluating COVID-19 Pneumonia With Neural Networks

Ultrasonographic Segmentation of Fetal Lung with Deep Learning

Intelligent classification of B-line and white lung from COVID-19 pneumonia ultrasound images using radiomics analysis

Intelligent interpretation of four lung ultrasonographic features with split attention based deep learning model