The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, AI can improve work efficiency by accurate delination of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up.We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19.
The accurate diagnosis of Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment, is essential for timely treatment and possible delay of AD. Fusion of multimodal neuroimaging data, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), has shown its effectiveness for AD diagnosis. The deep polynomial networks (DPN) is a recently proposed deep learning algorithm, which performs well on both large-scale and small-size datasets. In this study, a multimodal stacked DPN (MM-SDPN) algorithm, which MM-SDPN consists of two-stage SDPNs, is proposed to fuse and learn feature representation from multimodal neuroimaging data for AD diagnosis. Specifically speaking, two SDPNs are first used to learn high-level features of MRI and PET, respectively, which are then fed to another SDPN to fuse multimodal neuroimaging information. The proposed MM-SDPN algorithm is applied to the ADNI dataset to conduct both binary classification and multiclass classification tasks. Experimental results indicate that MM-SDPN is superior over the state-of-the-art multimodal feature-learning-based algorithms for AD diagnosis.
1 Electromyography (EMG) has been widely used for the assessment of 2 musculoskeletal functions and the control of electrical prostheses, which make use of the 3 EMG signal generated by the contraction of the residual muscles. In spite of the successful 4 applications of EMG in different fields, it has some inherent limitations, such as the 5 difficulty to differentiate the actions of neighboring muscles and to collect signals from 6 deep muscles using the surface EMG. The majority of current EMG controlled prostheses 7 can only provide sequential on-off controls using signals from two groups of muscles, so 8 the users are required to put many conscious efforts in monitoring the speed and range of 9 motion of the terminal devices being controlled. Recently, many alternative signals based 10 on the detection of dimensional changes of muscles or tendons during actions have been 11 reported. The objective of this study was to investigate the potential of the dimensional 12 change of muscles detected using sonography for musculoskeletal assessment and control. 13 A portable B-mode ultrasound scanner was used to collect the dynamic ultrasound images 14 of the forearm muscles of six normally-limbed young adults and three amputee subjects. A 15 motion analysis system was used to collect the movement of the wrist angle during the 16 experiments for the normal subjects. It was demonstrated that the morphological changes of 17 forearm muscles during actions can be successfully detected by ultrasound and linearly 18 correlated (R 2 = 0.876±0.042, mean±SD) with the wrist angle. We named these 19 sonographically detected signals about the architectural change of the muscle as 20 sonomyography (SMG). The mean ratio between the wrist angle and the percentage 21 deformation of the forearm muscle was 7.2±3.7 deg/% for the normal subjects. The 22 intraclass correlation coefficient (ICC) of this ratio among the three repeated tests was 23 0.868. The SMG signals from the residual forearms were also successfully detected when 24 3 the three amputee subjects contracted their residual muscles. The results demonstrated that 1 SMG had potentials for the musculoskeletal control and assessment. 2 3 4
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