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

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.

COVID-19-The Role of Artificial Intelligence, Machine Learning, and Deep Learning: A Newfangled

“…Utilizing multicentre and multimodal ultrasound information from 104 patients, the indicative model accomplished 94.39% exactness, 82.28% accuracy, 76.27% affectability, and 96.44% explicitness. A multi-layer fusion functionality of each block is preferred by Ghulam Muhammad et al [ 108 ] to improve the effectiveness of Coronavirus screening. The recommended fusion technique has 92.5% exactness, 91.8% precision, and 93.2% recovery utilizing the information assortment.…”

“…The COVIDX-Net incorporates seven unique designs of profound convolutional neural network models, for example, the adjusted Visual Geometry Group Network (VGG19) and the second form of Google MobileNet. The VGG19 and Dense Convolutional [108] to improve the effectiveness of Coronavirus screening. The recommended fusion technique has 92.5% exactness, 91.8% precision, and 93.2% recovery utilizing the information assortment.…”

Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic

“…On the other hand, Zheng et al built multimodal knowledge graphs from fused CT, X-ray, ultrasound, and text modalities, reaching a classification accuracy of 0.98 [ 99 ]. A multimodal channel and receptive field attention network combined with ResNeXt was proposed to process multicenter and multimodal data and achieved 0.94 accuracy [ 100 ].…”

Multi-modal lung ultrasound image classification by fusing image-based features and probe information

“…Zheng et al [24] proposed a multi-modal approach that combines imaging and text data using a neural network that can classify COVID-19 vs. non-COVID-19 cases, achieving an accuracy of 0.98 and a F1-score of 0.99. It must be noted that in many research works that focused on COVID-19 detection using DL approaches, multimodal approaches have proven to be more effective, achieving better classification performance than methods relying on a single modality [6], [24], [25]. Despite numerous research works on automated LUS image classification, the performance achieved is not yet on par with the level required for real-world clinical practice.…”