scholarly journals Evaluation of Lung Involvement in COVID-19 Pneumonia Based on Ultrasound Images

2020 ◽  
Author(s):  
Zhaoyu Hu ◽  
Zhenhua Liu ◽  
Yijie Dong ◽  
Jianjian Liu ◽  
Bin Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lung Ultrasound ◽  
Ultrasound Image ◽  
Assessment Model ◽  
Diagnostic Model ◽  
Ultrasound Images ◽  
Lung Involvement ◽  
Selective Learning ◽  
Imaging Tool ◽  
Multi Mode

Abstract Background: Lung ultrasound (LUS) can be an important imaging tool for the diagnosis and assessment of lung involvement. In this study, we determined the ultrasound manifestations of the lung associated with COVID-19 pneumonia, and obtained the ultrasound image changes of the patients from the initial diagnosis to rehabilitation. Methods: The purpose of this study is to establish a lung involvement assessment model based on deep learning. A channel attention classification method based on squeeze-and-excitation network combining with ResNeXt (SE_ResNeXt) is proposed, which can automatically learn the importance of different channel features, so as to achieve selective learning of channels and further achieve more accurate classification results. Results and conclusion: Among 104 patients' data from multicenter and multi-mode ultrasound, the diagnostic model can achieve 97.11% accuracy. The lung involvement severity of COVID-19 pneumonia and the trend of lesion were evaluated quantitatively.

scholarly journals Evaluation of Lung Involvement in COVID-19 Pneumonia Based on Ultrasound Images

2020 ◽  
Author(s):  
Zhaoyu Hu ◽  
Zhenhua Liu ◽  
Yijie Dong ◽  
Jianjian Liu ◽  
Bin Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Receptive Field ◽  
Lung Ultrasound ◽  
Assessment Model ◽  
Diagnostic Model ◽  
Ultrasound Images ◽  
Correct Classification ◽  
Lung Involvement ◽  
Attention Network ◽  
Imaging Tool

Abstract 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.

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

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Zhaoyu Hu ◽  
Zhenhua Liu ◽  
Yijie Dong ◽  
Jianjian Liu ◽  
Bin Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Receptive Field ◽  
Lung Ultrasound ◽  
Assessment Model ◽  
Diagnostic Model ◽  
Ultrasound Images ◽  
Correct Classification ◽  
Lung Involvement ◽  
Attention Network ◽  
Imaging Tool

Abstract 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.

scholarly journals Development of a deep learning classifier to accurately distinguish COVID-19 from look-a-like pathology on lung ultrasound

2020 ◽  
Author(s):  
Robert Arntfield ◽  
Blake VanBerlo ◽  
Thamer Alaifan ◽  
Nathan Phelps ◽  
Matt White ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lung Ultrasound ◽  
Low Cost ◽  
Ultrasound Images ◽  
Diagnostic Specificity ◽  
Imaging Tool ◽  
Learning Classifier ◽  
Learning Techniques ◽  
Deep Learning Model ◽  
B Lines

AbstractObjectivesLung ultrasound (LUS) is a portable, low cost respiratory imaging tool but is challenged by user dependence and lack of diagnostic specificity. It is unknown whether the advantages of LUS implementation could be paired with deep learning techniques to match or exceed human-level, diagnostic specificity among similar appearing, pathological LUS images.DesignA convolutional neural network was trained on LUS images with B lines of different etiologies. CNN diagnostic performance, as validated using a 10% data holdback set was compared to surveyed LUS-competent physicians.SettingTwo tertiary Canadian hospitals.Participants600 LUS videos (121,381 frames) of B lines from 243 distinct patients with either 1) COVID-19, Non-COVID acute respiratory distress syndrome (NCOVID) and 3) Hydrostatic pulmonary edema (HPE).ResultsThe trained CNN performance on the independent dataset showed an ability to discriminate between COVID (AUC 1.0), NCOVID (AUC 0.934) and HPE (AUC 1.0) pathologies. This was significantly better than physician ability (AUCs of 0.697, 0.704, 0.967 for the COVID, NCOVID and HPE classes, respectively), p < 0.01.ConclusionsA deep learning model can distinguish similar appearing LUS pathology, including COVID-19, that cannot be distinguished by humans. The performance gap between humans and the model suggests that subvisible biomarkers within ultrasound images could exist and multi-center research is merited.

scholarly journals Ultrasound Medical Images Classification Based on Deep Learning Algorithms: A Review

2021 ◽  
pp. 29-42
Author(s):  
admin admin ◽  
◽  
◽  
Adnan Mohsin Abdulazeez
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Ultrasound Image ◽  
Smart Devices ◽  
Learning Approach ◽  
Ultrasound Images ◽  
Medical Field ◽  
Modern Methods

With the development of technology and smart devices in the medical field, the computer system has become an essential part of this development to learn devices in the medical field. One of the learning methods is deep learning (DL), which is a branch of machine learning (ML). The deep learning approach has been used in this field because it is one of the modern methods of obtaining accurate results through its algorithms, and among these algorithms that are used in this field are convolutional neural networks (CNN) and recurrent neural networks (RNN). In this paper we reviewed what have researchers have done in their researches to solve fetal problems, then summarize and carefully discuss the applications in different tasks identified for segmentation and classification of ultrasound images. Finally, this study discussed the potential challenges and directions for applying deep learning in ultrasound image analysis.

scholarly journals Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning study

BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e045120
Author(s):  
Robert Arntfield ◽  
Blake VanBerlo ◽  
Thamer Alaifan ◽  
Nathan Phelps ◽  
Matthew White ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Lung Ultrasound ◽  
Low Cost ◽  
Characteristic Curve ◽  
Ultrasound Images ◽  
Diagnostic Specificity ◽  
Multicentre Research ◽  
B Lines

ObjectivesLung ultrasound (LUS) is a portable, low-cost respiratory imaging tool but is challenged by user dependence and lack of diagnostic specificity. It is unknown whether the advantages of LUS implementation could be paired with deep learning (DL) techniques to match or exceed human-level, diagnostic specificity among similar appearing, pathological LUS images.DesignA convolutional neural network (CNN) was trained on LUS images with B lines of different aetiologies. CNN diagnostic performance, as validated using a 10% data holdback set, was compared with surveyed LUS-competent physicians.SettingTwo tertiary Canadian hospitals.Participants612 LUS videos (121 381 frames) of B lines from 243 distinct patients with either (1) COVID-19 (COVID), non-COVID acute respiratory distress syndrome (NCOVID) or (3) hydrostatic pulmonary edema (HPE).ResultsThe trained CNN performance on the independent dataset showed an ability to discriminate between COVID (area under the receiver operating characteristic curve (AUC) 1.0), NCOVID (AUC 0.934) and HPE (AUC 1.0) pathologies. This was significantly better than physician ability (AUCs of 0.697, 0.704, 0.967 for the COVID, NCOVID and HPE classes, respectively), p<0.01.ConclusionsA DL model can distinguish similar appearing LUS pathology, including COVID-19, that cannot be distinguished by humans. The performance gap between humans and the model suggests that subvisible biomarkers within ultrasound images could exist and multicentre research is merited.

scholarly journals Multi-Features-Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhemin Zhuang ◽  
Zengbiao Yang ◽  
Shuxin Zhuang ◽  
Alex Noel Joseph Raj ◽  
Ye Yuan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Breast Tumor ◽  
Ultrasound Image ◽  
Breast Ultrasound ◽  
Tumor Diagnosis ◽  
Support Vector ◽  
Ultrasound Images ◽  
Deep Learning Features ◽  
Characteristic Features

Breast ultrasound examination is a routine, fast, and safe method for clinical diagnosis of breast tumors. In this paper, a classification method based on multi-features and support vector machines was proposed for breast tumor diagnosis. Multi-features are composed of characteristic features and deep learning features of breast tumor images. Initially, an improved level set algorithm was used to segment the lesion in breast ultrasound images, which provided an accurate calculation of characteristic features, such as orientation, edge indistinctness, characteristics of posterior shadowing region, and shape complexity. Simultaneously, we used transfer learning to construct a pretrained model as a feature extractor to extract the deep learning features of breast ultrasound images. Finally, the multi-features were fused and fed to support vector machine for the further classification of breast ultrasound images. The proposed model, when tested on unknown samples, provided a classification accuracy of 92.5% for cancerous and noncancerous tumors.

scholarly journals Deep Learning-Based Three-dimensional Transvaginal Ultrasound in Diagnosis of Intrauterine Adhesion

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Ji Li ◽  
Dan Liu ◽  
Xiaofeng Qing ◽  
Lanlan Yu ◽  
Huizhen Xiang
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Transvaginal Ultrasound ◽  
Three Dimensional ◽  
Ultrasound Image ◽  
Ultrasound Images ◽  
Diagnostic Efficiency ◽  
Research Subjects ◽  
Partial Differential ◽  
Intrauterine Adhesion

This study was aimed to enhance and detect the characteristics of three-dimensional transvaginal ultrasound images based on the partial differential algorithm and HSegNet algorithm under deep learning. Thereby, the effect of quantitative parameter values of optimized three-dimensional ultrasound image was analyzed on the diagnosis and evaluation of intrauterine adhesions. Specifically, 75 patients with suspected intrauterine adhesion in hospital who underwent the hysteroscopic diagnosis were selected as the research subjects. The three-dimensional transvaginal ultrasound image was enhanced and optimized by the partial differential equation algorithm and processed by the deep learning algorithm. Subsequently, three-dimensional transvaginal ultrasound examinations were performed on the study subjects that met the standards. The March classification method was used to classify the patients with intrauterine adhesion. Finally, the results by the three-dimensional transvaginal ultrasound were compared with the diagnosis results in hysteroscope surgery. The results showed that the HSegNet algorithm model realized the automatic labeling of intrauterine adhesion in the transvaginal ultrasound image and the final accuracy coefficient was 97.3%. It suggested that the three-dimensional transvaginal ultrasound diagnosis based on deep learning was efficient and accurate. The accuracy of the three-dimensional transvaginal ultrasound was 97.14%, the sensitivity was 96.6%, and the specificity was 72%. In conclusion, the three-dimensional transvaginal examination can effectively improve the diagnostic efficiency of intrauterine adhesion, providing theoretical support for the subsequent diagnosis and grading of intrauterine adhesion.

scholarly journals DEEP LEARNING TO PREDICT DEGREE OF INTERSTITIAL FIBROSIS AND TUBULAR ATROPHY FROM KIDNEY ULTRASOUND IMAGES - AN ARTIFICIAL INTELLIGENCE APPROACH

2020 ◽  
Author(s):  
Ambarish M Athavale ◽  
Peter D Hart ◽  
Matthew Itteera ◽  
Tushar Patel ◽  
David J Cymbaluk ◽  
...  
Keyword(s):  
Deep Learning ◽  
Kidney Biopsy ◽  
Interstitial Fibrosis ◽  
Strong Predictor ◽  
Ultrasound Image ◽  
Ultrasound Images ◽  
Tubular Atrophy ◽  
Invasive Test ◽  
Learning Classifier ◽  
Validation Set

Background: Interstitial fibrosis and tubular atrophy (IFTA) is a strong predictor of decline in kidney function. Non-invasive test to assess IFTA is not available. Methods: We trained, validated and tested a deep learning (DL) system to classify IFTA grade from 6,135 ultrasound images obtained from 352 patients who underwent kidney biopsy. Of 6,135 ultrasound images, 5,523 were used for training (n = 5,122) and validation (n = 401) and 612 to test the accuracy of the DL system. IFTA grade scored by nephropathologist on trichrome stained kidney biopsy slide was used as reference standard. Results: There were 159 patients (2,701 ultrasound images), 74 patients (1,239 ultrasound images), 41 patients (701 ultrasound images) and 78 patients (1,494 ultrasound images) with IFTA grades 1, 2, 3 and 4, respectively. The deep-learning classification system used masked images based on a 91% accurate kidney segmentation routine. The performance matrices for the deep learning classifier algorithm in the validation set showed excellent precision (90%), recall (76%), accuracy (84%) and F1-score (80%). In the independent test set also, performance matrices showed excellent precision (90%), recall (80%), accuracy (87%) and F1-score of (84%). Accuracy was highest for IFTA grade 1 (98%) and IFTA grade 4 (82%). Conclusion: A DL system can accurately predict IFTA from kidney ultrasound image.

scholarly journals Sonographic Diagnosis of COVID-19: A Review of Image Processing for Lung Ultrasound

2021 ◽  
Vol 4 ◽  
Author(s):  
Conor McDermott ◽  
Maciej Łącki ◽  
Ben Sainsbury ◽  
Jessica Henry ◽  
Mihail Filippov ◽  
...  
Keyword(s):  
Image Processing ◽  
Lung Ultrasound ◽  
Image Interpretation ◽  
Ultrasound Image ◽  
Daily Basis ◽  
Ultrasound Images ◽  
Term Care ◽  
The World ◽  
Small Hand ◽  
Detection And Diagnosis

The sustained increase in new cases of COVID-19 across the world and potential for subsequent outbreaks call for new tools to assist health professionals with early diagnosis and patient monitoring. Growing evidence around the world is showing that lung ultrasound examination can detect manifestations of COVID-19 infection. Ultrasound imaging has several characteristics that make it ideally suited for routine use: small hand-held systems can be contained inside a protective sheath, making it easier to disinfect than X-ray or computed tomography equipment; lung ultrasound allows triage of patients in long term care homes, tents or other areas outside of the hospital where other imaging modalities are not available; and it can determine lung involvement during the early phases of the disease and monitor affected patients at bedside on a daily basis. However, some challenges still remain with routine use of lung ultrasound. Namely, current examination practices and image interpretation are quite challenging, especially for unspecialized personnel. This paper reviews how lung ultrasound (LUS) imaging can be used for COVID-19 diagnosis and explores different image processing methods that have the potential to detect manifestations of COVID-19 in LUS images. Then, the paper reviews how general lung ultrasound examinations are performed before addressing how COVID-19 manifests itself in the images. This will provide the basis to study contemporary methods for both segmentation and classification of lung ultrasound images. The paper concludes with a discussion regarding practical considerations of lung ultrasound image processing use and draws parallels between different methods to allow researchers to decide which particular method may be best considering their needs. With the deficit of trained sonographers who are working to diagnose the thousands of people afflicted by COVID-19, a partially or totally automated lung ultrasound detection and diagnosis tool would be a major asset to fight the pandemic at the front lines.

scholarly journals Deep-Learning-Based Color Doppler Ultrasound Image Feature in the Diagnosis of Elderly Patients with Chronic Heart Failure Complicated with Sarcopenia

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Peng Bian ◽  
Xiyu Zhang ◽  
Ruihong Liu ◽  
Huijie Li ◽  
Qingqing Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Doppler Ultrasound ◽  
Color Doppler ◽  
Ultrasound Image ◽  
Color Doppler Ultrasound ◽  
Control Group ◽  
Learning Technology ◽  
Ultrasound Images ◽  
Before And After ◽  
Experimental Group

The neural network algorithm of deep learning was applied to optimize and improve color Doppler ultrasound images, which was used for the research on elderly patients with chronic heart failure (CHF) complicated with sarcopenia, so as to analyze the effect of the deep-learning-based color Doppler ultrasound image on the diagnosis of CHF. 259 patients were selected randomly in this study, who were admitted to hospital from October 2017 to March 2020 and were diagnosed with sarcopenia. Then, all of them underwent cardiac ultrasound examination and were divided into two groups according to whether deep learning technology was used for image processing or not. A group of routine unprocessed images was set as the control group, and the images processed by deep learning were set as the experimental group. The results of color Doppler images before and after processing were analyzed and compared; that is, the processed images of the experimental group were clearer and had higher resolution than the unprocessed images of the control group, with the peak signal-to-noise ratio (PSNR) = 20 and structural similarity index measure (SSIM) = 0.09; the similarity between the final diagnosis results and the examination results of the experimental group (93.5%) was higher than that of the control group (87.0%), and the comparison was statistically significant ( P < 0.05 ); among all the patients diagnosed with sarcopenia, 88.9% were also eventually diagnosed with CHF and only a small part of them were diagnosed with other diseases, with statistical significance ( P < 0.05 ). In conclusion, deep learning technology had certain application value in processing color Doppler ultrasound images. Although there was no obvious difference between the color Doppler ultrasound images before and after processing, they could all make a better diagnosis. Moreover, the research results showed the correlation between CHF and sarcopenia.

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