Purpose
Nowadays, the number of patients with COVID-19 pneumonia worldwide is still increasing. The clinical diagnosis of COVID-19 pneumonia faces challenges, such as the difficulty to perform RT-PCR tests in real time, the lack of experienced radiologists, clinical low-quality images, and the similarity of imaging features of community-acquired pneumonia and COVID-19. Therefore, we proposed an artificial intelligence model GARCD that uses chest CT images to assist in the diagnosis of COVID-19 in real time. It can show better diagnostic performance even facing low-quality CT images.
Methods
We used 14,129 CT images from 104 patients. A total of 12,929 samples were used to build artificial intelligence models, and 1200 samples were used to test its performance. The image quality improvement module is based on the generative adversarial structure. It improves the quality of the input image under the joint drive of feature loss and content loss. The enhanced image is sent to the disease diagnosis model based on residual convolutional network. It automatically extracts the semantic features of the image and then infers the probability that the sample belongs to COVID-19. The ROC curve is used to evaluate the performance of the model.
Results
This model can effectively enhance the low-quality image and make the image that is difficult to be recognized become recognizable. The model proposed in this paper reached 97.8% AUC, 96.97% sensitivity and 91.16% specificity in an independent test set. ResNet, GADCD, CNN, and DenseNet achieved 80.9%, 97.3%, 70.7% and 85.7% AUC in the same test set, respectively. By comparing the performance with related works, it is proved that the model proposed has stronger clinical usability.
Conclusion
The method proposed can effectively assist doctors in real-time detection of suspected cases of COVID-19 pneumonia even faces unclear image. It can quickly isolate patients in a targeted manner, which is of positive significance for preventing the further spread of COVID-19 pneumonia.
Abstract:A fast and cost-effective melamine detection approach has been developed based on surface enhanced Raman spectroscopy (SERS) using a novel hydrogen bonding-assisted supramolecular matrix. The detection utilizes was formed due to the strong multiple hydrogen bonding interactions between AOA and melamine. The complex was separated and concentrated to a pellet by an external magnet and used as a supramolecular matrix for the melamine detection. Laser excitation of the complex pellet produced a strong SERS signal diagnostic for RhB. The logarithmic intensity of the characteristic RhB peaks was found to be proportional to the concentration of melamine with a limit of detection of 2.5 µg/mL and a detection linearity range of 2.5~15.0 µg/mL in milk. As Fe 3 O 4 nanoparticles and AOA are thousands of times less expensive than the monoclonal antibody used in a traditional sandwich immunoassay, the current assay drastically cut down the cost of melamine detection. The current approach affords promise as a biosensor platform that cuts down sample pre-treatment steps and measurement expense.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.