A deep learning model for mass screening of <scp>COVID</scp>‐19

Dhaka, Vijaypal Singh; Oza, Meet Ganpatlal; Sharma, Tarushi; Misra, Ankit

doi:10.1002/ima.22544

Cited by 23 publications

(7 citation statements)

References 22 publications

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“…These features include regional volumes and cortical thickness segmented from brain regions known to be involved/implicated with memory loss and accelerated neurodegeneration that accompany Alzheimer’s disease 17 – 19 . Newer machine learning methods based on deep convolutional neural networks (CNNs) make it possible to extract features directly from image data in a data-driven fashion 20 – 26 . These methods have been shown to outperform traditional techniques based on predefined features in most image processing and computer vision tasks 27 , 28 .…”

Section: Introductionmentioning

confidence: 99%

Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs

Liu

Masurkar

Rusinek

et al. 2022

Sci Rep

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Early diagnosis of Alzheimer’s disease plays a pivotal role in patient care and clinical trials. In this study, we have developed a new approach based on 3D deep convolutional neural networks to accurately differentiate mild Alzheimer’s disease dementia from mild cognitive impairment and cognitively normal individuals using structural MRIs. For comparison, we have built a reference model based on the volumes and thickness of previously reported brain regions that are known to be implicated in disease progression. We validate both models on an internal held-out cohort from The Alzheimer's Disease Neuroimaging Initiative (ADNI) and on an external independent cohort from The National Alzheimer's Coordinating Center (NACC). The deep-learning model is accurate, achieved an area-under-the-curve (AUC) of 85.12 when distinguishing between cognitive normal subjects and subjects with either MCI or mild Alzheimer’s dementia. In the more challenging task of detecting MCI, it achieves an AUC of 62.45. It is also significantly faster than the volume/thickness model in which the volumes and thickness need to be extracted beforehand. The model can also be used to forecast progression: subjects with mild cognitive impairment misclassified as having mild Alzheimer’s disease dementia by the model were faster to progress to dementia over time. An analysis of the features learned by the proposed model shows that it relies on a wide range of regions associated with Alzheimer's disease. These findings suggest that deep neural networks can automatically learn to identify imaging biomarkers that are predictive of Alzheimer's disease, and leverage them to achieve accurate early detection of the disease.

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Section: Introductionmentioning

confidence: 99%

Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs

Liu

Masurkar

Rusinek

et al. 2022

Sci Rep

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“…Coronavirus is an animal-origin pathogen that can cause disease in humans [ 31 , 32 ]. A model for phenotype identification of notorious disease is urgently needed to be developed [ 33 , 34 ]. In the paper, we present a deep learning model of cross-species coronavirus infection that combines a one-dimensional convolutional neural network with a bidirectional gated recurrent unit network.…”

Section: Discussionmentioning

confidence: 99%

Prediction of pandemic risk for animal-origin coronavirus using a deep learning method

et al. 2021

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Background Coronaviruses can be isolated from bats, civets, pangolins, birds and other wild animals. As an animal-origin pathogen, coronavirus can cross species barrier and cause pandemic in humans. In this study, a deep learning model for early prediction of pandemic risk was proposed based on the sequences of viral genomes. Methods A total of 3257 genomes were downloaded from the Coronavirus Genome Resource Library. We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution. The genome sequence of animal-origin coronavirus was directly input to extract features and predict pandemic risk. The best performances were explored with the use of pre-trained DNA vector and attention mechanism. The area under the receiver operating characteristic curve (AUROC) and the area under precision-recall curve (AUPR) were used to evaluate the predictive models. Results The six specific models achieved good performances for the corresponding virus groups (1 for AUROC and 1 for AUPR). The general model with pre-training vector and attention mechanism provided excellent predictions for all virus groups (1 for AUROC and 1 for AUPR) while those without pre-training vector or attention mechanism had obviously reduction of performance (about 5–25%). Re-training experiments showed that the general model has good capabilities of transfer learning (average for six groups: 0.968 for AUROC and 0.942 for AUPR) and should give reasonable prediction for potential pathogen of next pandemic. The artificial negative data with the replacement of the coding region of the spike protein were also predicted correctly (100% accuracy). With the application of the Python programming language, an easy-to-use tool was created to implements our predictor. Conclusions Robust deep learning model with pre-training vector and attention mechanism mastered the features from the whole genomes of animal-origin coronaviruses and could predict the risk of cross-species infection for early warning of next pandemic. Graphical Abstract

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“…In this section, the authors present the architectures, training parameters, and working of the CNN and LSTM based multi-modal. The multi-modal comprises the ‘GenomeSimilarityPredictor’ and COVID-Screen-Net [ 29 ]. The ‘GenomeSimilarityPredictor’ is applied for the prediction of the genomic similarity of ‘SARS-CoV-2’ with other viruses and the classification of genomes into infected and non-infected.…”

Section: Methodsmentioning

confidence: 99%

“…The ‘GenomeSimilarityPredictor’ is applied for the prediction of the genomic similarity of ‘SARS-CoV-2’ with other viruses and the classification of genomes into infected and non-infected. Whereas, the architecture of COVID-Screen-Net is adopted from the work presented in [ 29 ] to classify the chest radiographs into healthy, bacterial pneumonia, and COVID-19.…”

Section: Methodsmentioning

confidence: 99%

Applying deep learning-based multi-modal for detection of coronavirus

et al. 2021

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Amidst the global pandemic and catastrophe created by 'COVID-19', every research institution and scientist are doing their best efforts to invent or find the vaccine or medicine for the disease. The objective of this research is to design and develop a deep learning-based multi-modal for the screening of COVID-19 using chest radiographs and genomic sequences. The modal is also effective in finding the degree of genomic similarity among the Severe Acute Respiratory Syndrome-Coronavirus 2 and other prevalent viruses such as Severe Acute Respiratory Syndrome-Coronavirus, Middle East Respiratory Syndrome-Coronavirus, Human Immunodeficiency Virus, and Human T-cell Leukaemia Virus. The experimental results on the datasets available at National Centre for Biotechnology Information, GitHub, and Kaggle repositories show that it is successful in detecting the genome of 'SARS-CoV-2' in the host genome with an accuracy of 99.27% and screening of chest radiographs into COVID-19, non-COVID pneumonia and healthy with a sensitivity of 95.47%. Thus, it may prove a useful tool for doctors to quickly classify the infected and non-infected genomes. It can also be useful in finding the most effective drug from the available drugs for the treatment of 'COVID-19'.

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A deep learning model for mass screening of COVID‐19

Cited by 23 publications

References 22 publications

Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs

Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs

Prediction of pandemic risk for animal-origin coronavirus using a deep learning method

Applying deep learning-based multi-modal for detection of coronavirus

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