The application of artificial intelligence and data integration in COVID-19 studies: a scoping review

Journal of the American Medical Informatics Association, Journal Year: 2021, Volume and Issue: 28(9), P. 2050 - 2067

Published: May 6, 2021

To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling.

Language: Английский

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A Review of Deep Learning Applications in Lung Ultrasound Imaging of COVID-19 Patients

BME Frontiers, Journal Year: 2022, Volume and Issue: 2022

Published: Jan. 1, 2022

The massive and continuous spread of COVID-19 has motivated researchers around the world to intensely explore, understand, develop new techniques for diagnosis treatment. Although lung ultrasound imaging is a less established approach when compared other medical modalities such as X-ray CT, multiple studies have demonstrated its promise diagnose patients. At same time, many deep learning models been built improve diagnostic efficiency imaging. integration these initially parallel efforts led report applications in patients, most which demonstrate outstanding potential aid COVID-19. This invited review focused on provides comprehensive overview systems utilized data acquisition, associated datasets, models, comparative performance.

Language: Английский

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D2BOF-COVIDNet: A Framework of Deep Bayesian Optimization and Fusion-Assisted Optimal Deep Features for COVID-19 Classification Using Chest X-ray and MRI Scans

Diagnostics, Journal Year: 2022, Volume and Issue: 13(1), P. 101 - 101

Published: Dec. 29, 2022

In 2019, a corona virus disease (COVID-19) was detected in China that affected millions of people around the world. On 11 March 2020, WHO declared this pandemic. Currently, more than 200 countries world have been by disease. The manual diagnosis using chest X-ray (CXR) images and magnetic resonance imaging (MRI) is time consuming always requires an expert person; therefore, researchers introduced several computerized techniques computer vision methods. recent face some challenges, such as low contrast CTX images, initialization hyperparameters, redundant features mislead classification accuracy.

Language: Английский

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Integrating digital twins and deep learning for medical image analysis in the era of COVID-19

Virtual Reality & Intelligent Hardware, Journal Year: 2022, Volume and Issue: 4(4), P. 292 - 305

Published: Aug. 1, 2022

Digital twins is a virtual representation of device and process that captures the physical properties environment operational algorithms/techniques in context medical devices technology. It may allow facilitate healthcare organizations to determine ways improve processes, enhance patient experience, lower operating expenses, extend value care. Considering current pandemic situation COVID-19, various devices, e.g., X-rays CT scan machines are constantly being used collect analyze images. In this situation, while collecting processing an extensive volume data form images, processes sometimes suffer from system failures can create critical issues for hospitals patients. Thus, regard, we introduced digital twin based smart integrated with so it be utilized information about health condition, configuration, maintenance history device/machine/system. Furthermore, i.e., X-rays, further analyzed by deep learning model detect infection COVID-19. The designed on Cascade RCNN architecture. architecture, detector stages deeper more sequentially selective against close small false positives. multi stage extension Recurrent Convolution Neural Network (RCNN) trained using output one training other one. At each stage, bounding boxes adjusted order locate suitable nearest positives during different stages. way, arrangement detectors increase Intersection over Union (IoU) overcome problem overfitting. We X-ray images as was previously another set. developed achieves good accuracy detection phase Experimental outcomes reveal efficiency which gains mean Average Precision (mAP) rate 0.94.

Language: Английский

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MMHFNet: Multi-modal and multi-layer hybrid fusion network for voice pathology detection

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 223, P. 119790 - 119790

Published: March 14, 2023

Language: Английский

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