Applications of artificial intelligence in battling against covid-19: A literature review

Development and Evaluation of an AI System for COVID-19 DiagnosisPreprint

Jin, C., Chen, W., Cao, Y., et al. (2020) · medRxiv · doi:10.1101/2020.03.20.20039834 · Cited by 194

Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images

Chen, X., Yao, L., Zhou, T., Dong, J., & Zhang, Y. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107826 · Cited by 191

The Impact of the Wuhan Covid-19 Lockdown on Air Pollution and Health: A Machine Learning and Augmented Synthetic Control Approach

Cole, M., Elliott, R., & Liu, B. (2020) · Environmental and Resource Economics · doi:10.1007/s10640-020-00483-4 · Cited by 189

Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks

Saba, A. I., & Elsheikh, A. H. (2020) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2020.05.029 · Cited by 189

Deep learning COVID-19 detection bias: accuracy through artificial intelligence

Vaid, S., Kalantar, R., & Bhandari, M. (2020) · International Orthopaedics · doi:10.1007/s00264-020-04609-7 · Cited by 189

Detection of Coronavirus (COVID-19) Associated Pneumonia Based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model Using Chest X-ray Dataset

Khalifa, N. E. M., Taha, M. H. N., Hassanien, A. E., & Elghamrawy, S. M. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-20601-6_22 · Cited by 188

Artificial Intelligence (AI) Provided Early Detection of the Coronavirus (COVID-19) in China and Will Influence Future Urban Health Policy Internationally

Allam, Z., Dey, G., & Jones, D. S. (2020) · AI · doi:10.3390/ai1020009 · Cited by 187

Early prediction of mortality risk among patients with severe COVID-19, using machine learning

Hu, C., Liu, Z., Jiang, Y., Shi, O., Zhang, X., Xu, K., Suo, C., Wang, Q., Song, Y., Yu, K., et al. (2020) · International Journal of Epidemiology · doi:10.1093/ije/dyaa171 · Cited by 183

POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)Preprint

Born, J., Brändle, G., Cossio, M., Disdier, M., Goulet, J., Roulin, J., & Wiedemann, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.12084 · Cited by 182

COVID faster R–CNN: A novel framework to Diagnose Novel Coronavirus Disease (COVID-19) in X-Ray images

Shibly, K. H., Dey, S. K., Islam, M. T.-U., & Rahman, M. M. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100405 · Cited by 182

COVID-19 diagnosis by routine blood tests using machine learning

Kukar, M., Gunčar, G., Vovko, T., Podnar, S., Černelč, P., Brvar, M., Zalaznik, M., Notar, M., Moškon, S., & Notar, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-90265-9 · Cited by 179

AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia

Chassagnon, G., Vakalopoulou, M., Battistella, E., Christodoulidis, S., Hoang-Thi, T.-N., Dangeard, S., Deutsch, É., André, F., Guillo, E., Halm, N., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101860 · Cited by 179

Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive mannerPreprint

Wang, Y., Hu, M., Li, Q., Zhang, X.-P. S., Zhai, G., & Yao, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.05534 · Cited by 178

Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test resultsPreprint

Wu, J., Zhang, P., Zhang, L., Meng, W., Li, J., Tong, C., Li, Y., Cai, J., Yang, Z., Zhu, J., et al. (2020) · medRxiv · doi:10.1101/2020.04.02.20051136 · Cited by 178

Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis

Li, W. T., Ma, J., Shende, N., Castaneda, G., Chakladar, J., Tsai, J. C., Apostol, L., Honda, C. O., Xu, J., Wong, L. M., et al. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01266-z · Cited by 176

ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India

Khan, F. M., & Gupta, R. (2020) · Journal of Safety Science and Resilience · doi:10.1016/j.jnlssr.2020.06.007 · Cited by 176

Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population

Banerjee, A., Ray, S., Vorselaars, B., Kitson, J., Mamalakis, M., Weeks, S., Baker, M., & Mackenzie, L. S. (2020) · International Immunopharmacology · doi:10.1016/j.intimp.2020.106705 · Cited by 174

Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices

Rahman, M. A., Zaman, N., Asyhari, A. T., Al-Turjman, F. M., Bhuiyan, M. Z. A., & Zolkipli, M. F. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102372 · Cited by 173

Deep Learning System for COVID-19 Diagnosis Aid Using X-ray Pulmonary Images

Civit-Masot, J., Luna-Perejón, F., Domínguez-Morales, M., & Civit, A. (2020) · Applied Sciences · doi:10.3390/app10134640 · Cited by 172

A deep convolutional neural network for COVID-19 detection using chest X-rays

Bassi, P. R. A. S., & Attux, R. (2021) · Research on Biomedical Engineering · doi:10.1007/s42600-021-00132-9 · Cited by 171

Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States

Mollalo, A., Rivera, K. M., & Vahedi, B. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17124204 · Cited by 169

SARS-CoV-2 CT-scan dataset:A large dataset of real patients CT scans for SARS-CoV-2 identification

Soares, E., Angelov, P. P., Biaso, S., Froes, M. H., & Abe, D. K. (2020) · Lancaster EPrints (Lancaster University) · Cited by 169

A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19Preprint

Ge, Y., Tian, T., Huang, S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.11.986836 · Cited by 168

Automated Assessment and Tracking of COVID-19 Pulmonary Disease Severity on Chest Radiographs Using Convolutional Siamese Neural Networks

Li, M., Arun, N., Gidwani, M., Chang, K., Deng, F. M., Little, B. P., Mendoza, D. P., Lang, M., Lee, S. I., O’Shea, A., et al. (2020) · Radiology Artificial Intelligence · doi:10.1148/ryai.2020200079 · Cited by 167

Neural Network Based Country Wise Risk Prediction of COVID-19

Pal, R., Sekh, A. A., Kar, S., & Prasad, D. K. (2020) · Applied Sciences · doi:10.3390/app10186448 · Cited by 166

COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

Zebin, T., Rezvy, S., & Pang, W. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01867-1 · Cited by 166

A Hybrid COVID-19 Detection Model Using an Improved Marine Predators Algorithm and a Ranking-Based Diversity Reduction Strategy

Abdel‐Basset, M., Mohamed, R., Elhoseny, M., Chakrabortty, R. K., & Ryan, M. J. (2020) · IEEE Access · doi:10.1109/access.2020.2990893 · Cited by 166

Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico

Melín, P., Monica, J. C., Sánchez, D., & Castillo, O. (2020) · Healthcare · doi:10.3390/healthcare8020181 · Cited by 166

Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation

Fellous, J., Sapiro, G., Rossi, A. F., Mayberg, H., & Ferrante, M. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01346 · Cited by 165

2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS)

(2020) · doi:10.1109/iciccs48265.2020 · Cited by 165

COVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks

Makris, A., Kontopoulos, I., & Tserpes, K. (2020) · Hellenic Conference on Artificial Intelligence · doi:10.1145/3411408.3411416 · Cited by 165

Modeling and prediction of COVID-19 in Mexico applying mathematical and computational models

Torrealba-Rodriguez, O., Conde-Gutiérrez, R. A., & Hernández-Javier, A. L. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109946 · Cited by 163

Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT

Chaganti, S., Greniér, P., Balachandran, A., Chabin, G., Cohen, S. L., Flohr, T. G., Georgescu, B., Grbić, S., Liu, S., Mellot, F., et al. (2020) · Radiology Artificial Intelligence · doi:10.1148/ryai.2020200048 · Cited by 163

CRISPR-based surveillance for COVID-19 using genomically-comprehensive machine learning designPreprint

Metsky, H. C., Freije, C. A., Kosoko-Thoroddsen, T.-S. F., Sabeti, P. C., & Myhrvold, C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.26.967026 · Cited by 162

Coronavirus disease 2019 (covid-19): A guide for UK GPs

Razai, M. S., Doerholt, K., Ladhani, S., & Oakeshott, P. (2020) · BMJ · doi:10.1136/bmj.m800 · Cited by 162

Analysis on novel coronavirus (COVID-19) using machine learning methods

Yadav, M., Perumal, M., & Srinivas, M. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110050 · Cited by 160

Blockchain and Artificial Intelligence Technology for Novel Coronavirus Disease 2019 Self-Testing

Mashamba-Thompson, T. P., & Crayton, E. D. (2020) · Diagnostics · doi:10.3390/diagnostics10040198 · Cited by 159

Automatic X-ray COVID-19 Lung Image Classification System based on Multi-Level Thresholding and Support Vector MachinePreprint

Mahdy, L. N., Ezzat, K. A., Elmousalami, H. H., Aboul-Ella, H., & Hassanien, A. E. (2020) · medRxiv · doi:10.1101/2020.03.30.20047787 · Cited by 158

Pollution, economic growth, and COVID-19 deaths in India: a machine learning evidence

Mele, M., & Magazzino, C. (2020) · Environmental Science and Pollution Research · doi:10.1007/s11356-020-10689-0 · Cited by 157

A survey on deep transfer learning to edge computing for mitigating the COVID-19 pandemic

Sufian, A., Ghosh, A., Sadiq, A. S., & Smarandache, F. (2020) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2020.101830 · Cited by 156

A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks

Pham, T. D. (2020) · Scientific Reports · doi:10.1038/s41598-020-74164-z · Cited by 156

A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

Chen, J., Li, K., Zhang, Z., Li, K., & Yu, P. S. (2021) · ACM Computing Surveys · doi:10.1145/3465398 · Cited by 154

Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods

Nishio, M., Noguchi, S., Matsuo, H., & Murakami, T. (2020) · Scientific Reports · doi:10.1038/s41598-020-74539-2 · Cited by 154

Emerging Technologies for Use in the Study, Diagnosis, and Treatment of Patients with COVID-19

Tsikala Vafea, M., Atalla, E., Georgakas, J., Shehadeh, F., Mylona, E. K., Kalligeros, M., & Mylonakis, E. (2020) · Cellular and Molecular Bioengineering · doi:10.1007/s12195-020-00629-w · Cited by 153

Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features

Hasan, A. M., Al-Jawad, M. M. H., Jalab, H. A., Shaiba, H. A., Ibrahim, R. W., & Al-shamasneh, A. R. M. (2020) · Entropy · doi:10.3390/e22050517 · Cited by 153

Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A ReviewPreprint

Shoeibi, A., Khodatars, M., Jafari, M., Ghassemi, N., Sadeghi, D., Moridian, P., Khadem, A. A., Alizadehsani, R., Hussain, S., Zare, A., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.10785 · Cited by 149

Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy

Castiglioni, I., Ippolito, D., Interlenghi, M., Monti, C. B., Salvatore, C., Schiaffino, S., Polidori, A., Gandola, D. G., Messa, C., & Sardanelli, F. (2021) · European Radiology Experimental · doi:10.1186/s41747-020-00203-z · Cited by 145

Deep learning for predicting COVID-19 malignant progression

Fang, C., Bai, S., Chen, Q., Zhou, Y., Xia, L., Qin, L., Gong, S., Xie, X., Zhou, C., Tu, D., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102096 · Cited by 144

Analysis of Spatial Spread Relationships of Coronavirus (COVID-19) Pandemic in the World using Self Organizing Maps

Melín, P., Monica, J. C., Sánchez, D., & Castillo, O. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109917 · Cited by 143

Artificial intelligence cooperation to support the global response to COVID-19

Luengo-Oroz, M. A., Pham, K. H., Aylett-Bullock, J., Kirkpatrick, R., Luccioni, A. S., Rubel, S., Wachholz, C., Chakchouk, M., Biggs, P., Nguyen, T., et al. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0184-3 · Cited by 143

Weakly Labeled Data Augmentation for Deep Learning: A Study on COVID-19 Detection in Chest X-Rays

Rajaraman, S., & Antani, S. (2020) · Diagnostics · doi:10.3390/diagnostics10060358 · Cited by 143

Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study

Mackey, T., Purushothaman, V., Li, J., Shah, N., Nali, M., Bardier, C., Liang, B., Cai, M., & Cuomo, R. (2020) · JMIR Public Health and Surveillance · doi:10.2196/19509 · Cited by 142

Covid-twitter-bert: A natural language processing model to analyse covid-19 content on twitter

Müller, M., Salathé, M., & Kummervold, P. E. (2022) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.7074654 · Cited by 141

Covid-19: Open-Data Resources for Monitoring, Modeling, and Forecasting the Epidemic

Álamo, T., Reina, D. G., Mammarella, M., & Abella, A. (2020) · Electronics · doi:10.3390/electronics9050827 · Cited by 141

Machine-Learning Approaches in COVID-19 Survival Analysis and Discharge-Time Likelihood Prediction Using Clinical Data

Nemati, M., Ansary, J., & Nemati, N. (2020) · Patterns · doi:10.1016/j.patter.2020.100074 · Cited by 139

Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods

Nishio, M., 西尾, 瑞., ニシオ, ミ., Noguchi, S., Matsuo, H., Murakami, T., 村上, 卓., & ムラカミ, タ. (2020) · Institutional Repositories DataBase (IRDB) · Cited by 139

MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation

Qiu, Y., Liu, Y., Li, S., & Xu, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i6.16617 · Cited by 138

Finding COVID-19 from Chest X-rays using Deep Learning on a Small DatasetPreprint

Hall, L., Goldgof, D. B., Paul, R., & Goldgof, G. M. (2020) · doi:10.36227/techrxiv.12083964.v4 · Cited by 137

Covid-twitter-bert: A natural language processing model to analyse covid-19 content on twitter

Müller, M., Salathé, M., & Kummervold, P. E. (2022) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.7074653 · Cited by 136

Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter studyPreprint

Qi, X., Jiang, Z., Yu, Q., et al. (2020) · medRxiv · doi:10.1101/2020.02.29.20029603 · Cited by 135

An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data

Peng, Y., & Nagata, M. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110055 · Cited by 135

Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests

Yao, H., Zhang, N., Zhang, R., Duan, M., Xie, T., Pan, J., Peng, E., Huang, J., Zhang, Y., Xu, X., et al. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.00683 · Cited by 134

Quantifying COVID-19 Content in the Online Health Opinion War Using Machine Learning

Sear, R. F., Velásquez, N., Leahy, R., Restrepo, N. J., Oud, S. E., Gabriel, N., Lupu, Y., & Johnson, N. F. (2020) · IEEE Access · doi:10.1109/access.2020.2993967 · Cited by 133

Chest X-ray Classification Using Deep Learning for Automated COVID-19 Screening

Shelke, A., Inamdar, M., Shah, V., Tiwari, A., Hussain, A., Chafekar, T., & Mehendale, N. D. (2021) · SN Computer Science · doi:10.1007/s42979-021-00695-5 · Cited by 133

COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning ApproachesPreprint

Alom, M. Z., Rahman, M. M. S., Nasrin, M. S., Taha, T. M., & Asari, V. K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.03747 · Cited by 133

Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT ImagesPreprint

Tang, Z., Zhao, W., Xie, X., Zheng, Z., Shi, F., Liu, J., & Shen, D. G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11988 · Cited by 132

Artificial intelligence and COVID-19: A multidisciplinary approach

Ahuja, A. S., Reddy, V. P., & Marques, O. (2020) · Integrative Medicine Research · doi:10.1016/j.imr.2020.100434 · Cited by 130

Augmented curation of clinical notes from a massive EHR system reveals symptoms of impending COVID-19 diagnosis

Wagner, T., Shweta, F., Murugadoss, K., Awasthi, S., Venkatakrishnan, A., Bade, S., Puranik, A., Kang, M., Pickering, B. W., O’Horo, J. C., et al. (2020) · eLife · doi:10.7554/elife.58227 · Cited by 130

Convolutional Sparse Support Estimator-Based COVID-19 Recognition From X-Ray Images

Yamaç, M., Ahishali, M., Degerli, A., Serkan Kiranyaz, B., Chowdhury, M. E. H., & Gabbouj, M. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3070467 · Cited by 129

Analysis, Prediction and Evaluation of COVID-19 Datasets using Machine Learning Algorithms

Prakash, K. B. (2020) · International Journal of Emerging Trends in Engineering Research · doi:10.30534/ijeter/2020/117852020 · Cited by 129

Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables

da Silva, R. G., Ribeiro, M. H. D. M., Mariani, V. C., & Coelho, L. D. S. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110027 · Cited by 129

COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rays

Li, X. S., Li, C., & Zhu, D. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313217 · Cited by 129

CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images

Javaheri, T., Homayounfar, M., Amoozgar, Z., Reiazi, R., Homayounieh, F., Abbas, E., Laali, A., Radmard, A. R., Gharib, M. H., Mousavi, S. A., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00399-3 · Cited by 129

Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research DirectionsPreprint

Nguyen, T. T. (2020) · doi:10.36227/techrxiv.12743933.v1 · Cited by 128

A novel artificial intelligence-assisted triage tool to aid in the diagnosis of suspected COVID-19 pneumonia cases in fever clinics

Feng, C., Wang, L., Chen, X., Zhai, Y., Zhu, F., Hua, C., Wang, Y., Su, X., Huang, S., Tian, L. H., et al. (2021) · Annals of Translational Medicine · doi:10.21037/atm-20-3073 · Cited by 128

Considerations for development and use of AI in response to COVID-19

Sipior, J. C. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102170 · Cited by 127

Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea

Al‐qaness, M. A. A., Ewees, A. A., Fan, H. M., Abualigah, L., & Elaziz, M. A. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17103520 · Cited by 125

Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in ChinaPreprint

Huang, C., Chen, Y., Ma, Y., & Kuo, P. (2020) · medRxiv · doi:10.1101/2020.03.23.20041608 · Cited by 124

Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software

Zhang, H.-T., Zhang, H., Zhang, H.-H., Nan, Y., Zhao, Y., Fu, E.-Q., Xie, Y.-H., Liu, W., Li, W.-P., Zhang, H.-J., et al. (2020) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-020-04953-1 · Cited by 124

Optimal policies for control of the novel coronavirus disease (COVID-19) outbreak

Yousefpour, A., Jahanshahi, H., & Bekiros, S. D. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109883 · Cited by 124

Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing

Alonso, J. L. I., Ancochea, J., & Soriano, J. B. (2020) · Journal of Medical Internet Research · doi:10.2196/21801 · Cited by 124

“Fast deep learning computer-aided diagnosis of COVID-19 based on digital chest x-ray images”

Al–antari, M. A., Hua, C.-H., Bang, J., & Lee, S. (2020) · Applied Intelligence · doi:10.1007/s10489-020-02076-6 · Cited by 124

Use of CT and artificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology

Neri, E., Miele, V., Coppola, F., & Grassi, R. (2020) · La radiologia medica · doi:10.1007/s11547-020-01197-9 · Cited by 122

Shallow Convolutional Neural Network for COVID-19 Outbreak Screening Using Chest X-rays

Mukherjee, H., Ghosh, S., Dhar, A., Obaidullah, S. M., Santosh, K., & Roy, K. (2021) · Cognitive Computation · doi:10.1007/s12559-020-09775-9 · Cited by 122

Time series prediction of COVID-19 by mutation rate analysis using recurrent neural network-based LSTM model

Pathan, R. K., Biswas, M., & Khandaker, M. U. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110018 · Cited by 122

Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly DetectionPreprint

Zhang, J., Xie, Y., Pang, G., Liao, Z., Verjans, J. W. H., Li, W., Sun, Z., He, J., Li, Y., Shen, C., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.12338 · Cited by 122

Experiments of Federated Learning for COVID-19 Chest X-ray Images

Yan, B., Wang, J., Cheng, J., Zhou, Y., Zhang, Y., Yang, Y., Liu, L., Zhao, H., Wang, C., & Liu, B. (2021) · Communications in Computer and Information Science · doi:10.1007/978-3-030-78618-2_4 · Cited by 122

Coronavirus Disease (COVID-19): A Machine Learning Bibliometric Analysis

Felice, F. D., & Polimeni, A. (2020) · In Vivo · doi:10.21873/invivo.11951 · Cited by 120

Interpretable artificial intelligence framework for COVID‑19 screening on chest X‑rays

Tsiknakis, N., Trivizakis, E., Vassalou, E. E., Papadakis, G. Z., Spandidos, D. A., Tsatsakis, A. M., Sánchez‑García, J., López‐González, R., Papanikolaou, N. K., Karantanas, A. H., et al. (2020) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2020.8797 · Cited by 118

An Early Study on Intelligent Analysis of Speech Under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety

Han, J., Qian, K., Song, M., Yang, Z., Ren, Z. X., Liu, S., Liu, J., Zheng, H., Ji, W., Koike, T., et al. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2223 · Cited by 117

Automatic Text Summarization of COVID-19 Medical Research Articles using BERT and GPT-2Preprint

Kieuvongngam, V., Tan, B., & Niu, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.01997 · Cited by 117

Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images

Dey, N., Rajinikanth, V., Fong, S. J., Kaiser, M. S., & Mahmud, M. (2020) · Cognitive Computation · doi:10.1007/s12559-020-09751-3 · Cited by 115

Potential neutralizing antibodies discovered for novel corona virus using machine learning

Magar, R., Yadav, P., & Barati Farimani, A. (2021) · Scientific Reports · doi:10.1038/s41598-021-84637-4 · Cited by 114

A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic modelsPreprint

Liu, D., Clemente, L., Poirier, C., Ding, X., Chinazzi, M., Davis, J. T., Vespignani, A., & Santillana, M. (2020) · PubMed · doi:10.48550/arxiv.2004.04019 · Cited by 114

India nudges to contain COVID-19 pandemic: A reactive public policy analysis using machine-learning based topic modelling

Debnath, R., & Bardhan, R. (2020) · PLoS ONE · doi:10.1371/journal.pone.0238972 · Cited by 114

PDCOVIDNet: a parallel-dilated convolutional neural network architecture for detecting COVID-19 from chest X-ray images

Chowdhury, N. K., Rahman, M. M., & Kabir, M. A. (2020) · Health Information Science and Systems · doi:10.1007/s13755-020-00119-3 · Cited by 113

Artificial intelligence to codify lung CT in Covid-19 patients

Belfiore, M. P., Urraro, F., Grassi, R., Giacobbe, G., Patelli, G., Cappabianca, S., & Reginelli, A. (2020) · La radiologia medica · doi:10.1007/s11547-020-01195-x · Cited by 112

Use of Health Belief Model–Based Deep Learning Classifiers for COVID-19 Social Media Content to Examine Public Perceptions of Physical Distancing: Model Development and Case Study

Raamkumar, A. S., Tan, S. G., & Wee, H. L. (2020) · JMIR Public Health and Surveillance · doi:10.2196/20493 · Cited by 112

Forecasting the dynamics of COVID-19 Pandemic in Top 15 countries in April 2020: ARIMA Model with Machine Learning ApproachPreprint

Kumar, P., Kalita, H., Patairiya, S., Sharma, Y. D., Nanda, C., Rani, M., Rahmani, J., & Bhagavathula, A. S. (2020) · medRxiv · doi:10.1101/2020.03.30.20046227 · Cited by 110

Quantifying the effect of quarantine control in Covid-19 infectious spread using machine learningPreprint

Dandekar, R., & Barbastathis, G. (2020) · medRxiv · doi:10.1101/2020.04.03.20052084 · Cited by 110

Drawing insights from COVID-19-infected patients using CT scan images and machine learning techniques: a study on 200 patients

Sharma, S. (2020) · Environmental Science and Pollution Research · doi:10.1007/s11356-020-10133-3 · Cited by 110

The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges

Ahmad, A., Garhwal, S., Ray, S. K., Kumar, G., Malebary, S. J., & Barukab, O. (2020) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09472-8 · Cited by 110

Identifying novel factors associated with COVID-19 transmission and fatality using the machine learning approach

Li, M., Zhang, Z., Cao, W., Liu, Y., Du, B., Chen, C., Liu, Q., Uddin, M. N., Jiang, S., Chen, C., et al. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.142810 · Cited by 110

Transfer learning–based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data

Singh, M. P., Bansal, S., Ahuja, S., Dubey, R., Panigrahi, B. K., & Dey, N. (2021) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02299-2 · Cited by 109

COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution

Yan, Q., Wang, B., Gong, D., Luo, C., Zhao, W., Shen, J., Shi, Q., Jin, S., Zhang, L., & You, Z. (2020) · arXiv · doi:10.48550/arxiv.2004.10987 · Cited by 109

From community-acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysis of COVID-19 on thick-section CT scans

Li, Z., Zheng, Z., Li, Y., Zhang, T., Gao, L., Jin, D., Sun, Y., Ye, X., Yu, L., Hu, Z.-Y., et al. (2020) · European Radiology · doi:10.1007/s00330-020-07042-x · Cited by 109

CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information Management

Su, D., Xu, Y., Yu, T., Siddique, F. B., Barezi, E. J., & Fung, P. (2020) · Workshop on NLP for COVID-19 (NLP-COVID19) · doi:10.18653/v1/2020.nlpcovid19-2.14 · Cited by 108

A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images

Voulodimos, A. S., Protopapadakis, E. E., Katsamenis, I., Doulamis, A. D., & Doulamis, N. D. (2021) · Sensors · doi:10.3390/s21062215 · Cited by 108

COVID-19 Detection using Artificial Intelligence

Salman, F. M., Abu-Naser, S. S., Alajrami, E., Abu-Nasser, B. S., & Alashqar, B. A. M. (2020) · PhilPapers (PhilPapers Foundation) · Cited by 108

2020 5th International Conference on Communication and Electronics Systems (ICCES)

(2020) · doi:10.1109/icces48766.2020 · Cited by 108

Network medicine framework for identifying drug-repurposing opportunities for COVID-19

Morselli Gysi, D., do Valle, Í., Zitnik, M., Ameli, A., Gan, X., Varol, O., Ghiassian, S. D., Patten, J. J., Davey, R. A., Loscalzo, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2025581118 · Cited by 107

Longitudinal Assessment of COVID-19 Using a Deep Learning–based Quantitative CT Pipeline: Illustration of Two Cases

Cao, Y., Xu, Z., Feng, J., Jin, C., Han, X., Wu, H., & Shi, H. (2020) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2020200082 · Cited by 106

Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical Decision-MakingPreprint

Pourhomayoun, M., & Shakibi, M. (2020) · medRxiv · doi:10.1101/2020.03.30.20047308 · Cited by 106

Advanced bioinformatics rapidly identifies existing therapeutics for patients with coronavirus disease-2019 (COVID-19)

Kim, J., Zhang, J., Cha, Y., Kolitz, S. E., Funt, J. M., Escalante-Chong, R., Barrett, S., Kusko, R. L., Zeskind, B., & Kaufman, H. L. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-020-02430-9 · Cited by 106

Forecast and evaluation of COVID-19 spreading in USA with reduced-space Gaussian process regression

Velásquez, R. M. A., & Lara, J. V. M. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109924 · Cited by 106

CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks

Hassantabar, S., Novati, S., Ghanakota, V., Ferrari, A., Nicola, G. N., Bruno, R., Marino, I. R., Hamidouche, K., & Jha, N. K. (2021) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2021.3130228 · Cited by 106

Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies

Batra, R., Chan, H., Kamath, G., Ramprasad, R., Cherukara, M. J., & Sankaranarayanan, S. K. R. S. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c02278 · Cited by 106

Predicting CoVID-19 community mortality risk using machine learning and development of an online prognostic tool

Das, A., Mishra, S. R., & Gopalan, S. S. (2020) · PeerJ · doi:10.7717/peerj.10083 · Cited by 106

Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey

Tayarani-N., M.-H., Yao, X., & Xu, H. (2014) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2014.2355174 · Cited by 105

Machine learning to assist clinical decision-making during the COVID-19 pandemic

Debnath, S., Barnaby, D. P., Coppa, K., Makhnevich, A., Kim, E. J., Chatterjee, S., Tóth, V., Levy, T. J., Paradis, M. D., Cohen, S. L., et al. (2020) · Bioelectronic Medicine · doi:10.1186/s42234-020-00050-8 · Cited by 104

COVID-DenseNet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology ImagesPreprint

Sarker, L., Islam, M. M., Hannan, T., & Ahmed, Z. (2020) · Preprints.org · doi:10.20944/preprints202005.0151.v1 · Cited by 104

Forecasting and Evaluating Multiple Interventions for COVID-19 Worldwide

Hu, Z., Ge, Q., Li, S., Boerwinkle, E., Li, J., & Xiong, M. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.00041 · Cited by 103

X-Ray Image based COVID-19 Detection using Pre-trained Deep Learning ModelsPreprint

Horry, M. J., Chakraborty, S., Paul, M., Ulhaq, A., Pradhan, B., Saha, M., & Shukla, N. (2020) · doi:10.31224/osf.io/wx89s · Cited by 103

Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural NetworksPreprint

Asif, S., Yi, W., Hou, J., Tao, Y., & Si, J. (2020) · medRxiv · doi:10.1101/2020.05.01.20088211 · Cited by 103

Review on machine and deep learning models for the detection and prediction of Coronavirus

Salehi, A. W., Baglat, P., & Gupta, G. (2020) · Materials Today Proceedings · doi:10.1016/j.matpr.2020.06.245 · Cited by 103

An Overview on Audio, Signal, Speech, & Language Processing for COVID-19Preprint

Deshpande, G., & Schuller, B. W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.08579 · Cited by 102

Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods

Albahri, O. S., Al‐Obaidi, J. R., Zaidan, A. A., Albahri, A. S., Zaidan, B. B., Salih, M. M., Abdulhadi, Q., Dawood, K., Mohammed, R. T., Abdulkareem, K. H., et al. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105617 · Cited by 102

Effects of the Disastrous Pandemic COVID 19 on Learning Styles, Activities and Mental Health of Young Indian Students - A Machine Learning Approach

Khattar, A., Jain, P. R., & Quadri, S. M. K. (2020) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs48265.2020.9120955 · Cited by 102

Multi-Biological Laboratory Examination Framework for the Prioritization of Patients with COVID-19 Based on Integrated AHP and Group VIKOR Methods

Albahri, A. S., Al‐Obaidi, J. R., Zaidan, A. A., Albahri, O. S., Hamid, R. A., Zaidan, B. B., Alamoodi, A. H., & Hashim, M. (2020) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622020500285 · Cited by 102

A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images

Laradji, I., Rodríguez, P., Mañas, O., Lensink, K., Law, M., Kurzman, L., Parker, W., Vázquez, D., & Nowrouzezahrai, D. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00250 · Cited by 98

The diagnostic evaluation of Convolutional Neural Network (CNN) for the assessment of chest X-ray of patients infected with COVID-19Preprint

Bukhari, S. U. K., Bukhari, S. S. K., Syed, A., & Shah, S. S. H. (2020) · medRxiv · doi:10.1101/2020.03.26.20044610 · Cited by 97

A Combined Deep CNN-LSTM Network for the Detection of Novel Coronavirus (COVID-19) Using X-ray ImagesPreprint

Islam, M. Z., Islam, M. M., & Asraf, A. (2020) · medRxiv · doi:10.1101/2020.06.18.20134718 · Cited by 97

Predicting COVID-19 Malignant Progression with AI TechniquesPreprint

Bai, X., Fang, C., Zhou, Y., Bai, S., Liu, Z., Xia, L., Chen, Q., Xu, Y., Xia, T., Gong, S., et al. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3557984 · Cited by 97

Early risk assessment for COVID-19 patients from emergency department data using machine learning

Heldt, F. S., Vizcaychipi, M. P., Peacock, S., Cinelli, M., McLachlan, L., Andreotti, F., Jovanović, S., Dürichen, R., Lipunova, N., Fletcher, R. A., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-83784-y · Cited by 97

Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices

de Moura, J., Ramos, L., Vidal, P. L., Cruz, M., Abelairas, L., Lopez, E. C., Novo, J., & Ortega, M. (2020) · IEEE Access · doi:10.1109/access.2020.3033762 · Cited by 97

A Machine Learning-Aided Global Diagnostic and Comparative Tool to Assess Effect of Quarantine Control in COVID-19 Spread

Dandekar, R., Rackauckas, C., & Barbastathis, G. (2020) · Patterns · doi:10.1016/j.patter.2020.100145 · Cited by 97

Machine Learning Approach for Confirmation of COVID-19 Cases: Positive, Negative, Death and ReleasePreprint

Dutta, S., & Bandyopadhyay, S. K. (2020) · medRxiv · doi:10.1101/2020.03.25.20043505 · Cited by 96

Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United StatesPreprint

Zou, D., Wang, L., Xu, P., Chen, J., Zhang, W., & Gu, Q. (2020) · medRxiv · doi:10.1101/2020.05.24.20111989 · Cited by 96

Deep‐learning artificial intelligence analysis of clinical variables predicts mortality in COVID‐19 patients

Zhu, J., Ge, P., Jiang, C., Zhang, Y., Li, X., Zhao, Z., Zhang, L., & Duong, T. Q. (2020) · Journal of the American College of Emergency Physicians Open · doi:10.1002/emp2.12205 · Cited by 96

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative ModelsPreprint

Chenthamarakshan, V., Das, P., Hoffman, S. C., Strobelt, H., Padhi, I., Lim, K. W., Hoover, B., Manica, M., Born, J., Laino, T., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.01215 · Cited by 96

Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19

Misra, S., Jeon, S., Lee, S., Managuli, R., Jang, I.-S., & Kim, C. (2020) · Electronics · doi:10.3390/electronics9091388 · Cited by 95

A Methodological Approach for Predicting COVID-19 Epidemic Using EEMD-ANN Hybrid Model

Hasan, N. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100228 · Cited by 95

Coronavirus Detection and Analysis on Chest CT with Deep LearningPreprint

Gozes, O., Frid-Adar, M., Sagie, N., Zhang, H., Ji, W., & Greenspan, H. K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.02640 · Cited by 94

Predicting the Disease Outcome in COVID-19 Positive Patients Through Machine Learning: A Retrospective Cohort Study With Brazilian Data

de Souza, F. S. H., Hojo-Souza, N. S., dos Santos, E. B., Silva, C. M., & Guidoni, D. L. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.579931 · Cited by 94

COVID-19 Detection in Chest X-ray Images using a Deep Learning Approach.

Saiz, F. A., & Barandiarán, Í. (2020) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2020.04.003 · Cited by 94

Conspiracy in the Time of Corona: Automatic detection of Emerging Covid-19 Conspiracy Theories in Social Media and the NewsPreprint

Shahsavari, S., Holur, P., Wang, T., et al. (2020) · Research Square · doi:10.21203/rs.3.rs-52079/v1 · Cited by 92

Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and SegmentationPreprint

Amyar, A., Modzelewski, R., & Ruan, S. (2020) · medRxiv · doi:10.1101/2020.04.16.20064709 · Cited by 90

Using Computer Vision to enhance Safety of Workforce in Manufacturing in a Post COVID WorldPreprint

Khandelwal, P., Khandelwal, A., Agarwal, S., Thomas, D., Xavier, N., & Raghuraman, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.05287 · Cited by 90

COVID-19-CT-CXR: A Freely Accessible and Weakly Labeled Chest X-Ray and CT Image Collection on COVID-19 From Biomedical Literature

Peng, Y., Tang, Y., Lee, S., Zhu, Y., Summers, R. M., & Lu, Z. (2020) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2020.3035935 · Cited by 90

Exponentially Increasing Trend of Infected Patients with COVID-19 in Iran: A Comparison of Neural Network and ARIMA Forecasting Models

Moftakhar, L., Seif, M., & Safe, M. S. (2020) · Iranian Journal of Public Health · doi:10.18502/ijph.v49is1.3675 · Cited by 89

Evaluating the Traditional Chinese Medicine (TCM) Officially Recommended in China for COVID-19 Using Ontology-Based Side-Effect Prediction Framework (OSPF) and Deep Learning

Wang, Z., Li, L., Song, M., Yan, J., Shi, J., & Yao, Y. (2021) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2021.113957 · Cited by 89

Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19

Kavadi, D. P., Patan, R., Manikandan, R., & Gandomi, A. H. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110056 · Cited by 88

Digital pathology and artificial intelligence will be key to supporting clinical and academic cellular pathology through COVID-19 and future crises: the PathLAKE consortium perspective

Browning, L., Colling, R. T., Rakha, E. A., Rajpoot, N., Rittscher, J., James, J. A., Salto‐Tellez, M., Snead, D. R. J., & Verrill, C. L. (2020) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2020-206854 · Cited by 88

A Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19)

Farid, A. A., Selim, G. I., & Khater, H. (2020) · International Journal of Scientific and Engineering Research · doi:10.14299/ijser.2020.03.02 · Cited by 88

Artificial intelligence in prediction of mental health disorders induced by the COVID-19 pandemic among health care workers

Ćosić, K., Popović, S., Šarlija, M., Kesedžić, I., & Jovanović, T. (2020) · Croatian Medical Journal · doi:10.3325/cmj.2020.61.279 · Cited by 87

Issues associated with deploying CNN transfer learning to detect COVID-19 from chest X-rays

Majeed, T. F., Rashid, R. D., Ali, D., & Asaad, A. (2020) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-020-00934-8 · Cited by 87

Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study

Yang, S., Jiang, L., Cao, Z., Wang, L., Cao, J., Feng, R., Zhang, Z., Xue, X., Shi, Y., & Shan, F. (2020) · Annals of Translational Medicine · doi:10.21037/atm.2020.03.132 · Cited by 86

CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification

Goncharov, M., Pisov, M., Shevtsov, A. E., Shirokikh, B., Kurmukov, A. G., Blokhin, I. A., Chernina, V., Solovev, A. V., Gombolevskiy, V. А., Morozov, S. P., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102054 · Cited by 85

Artificial intelligence applied on chest X-ray can aid in the diagnosis of COVID-19 infection: a first experience from Lombardy, ItalyPreprint

Castiglioni, I., Ippolito, D., Interlenghi, M., et al. (2020) · medRxiv · doi:10.1101/2020.04.08.20040907 · Cited by 84

4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection

Abbas, A., Abdelsamea, M. M., & Gaber, M. M. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3082015 · Cited by 84

Heg.IA: an intelligent system to support diagnosis of Covid-19 based on blood tests

Barbosa, V. A. D. F., Gomes, J. C., de Santana, M. A., Albuquerque, J. E. D. A., de Souza, R. G., de Souza, R. E., & dos Santos, W. P. (2021) · Research on Biomedical Engineering · doi:10.1007/s42600-020-00112-5 · Cited by 83

Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death

Chatterjee, A., Gerdes, M. W., & Martínez, S. (2020) · Sensors · doi:10.3390/s20113089 · Cited by 83

COMPUTER VISION FOR COVID-19 CONTROL: A SURVEYPreprint

Ulhaq, A., Khan, A. A., Gomes, D. P. S., & Paul, M. (2020) · doi:10.31224/osf.io/yt9sx · Cited by 82

COVID-19 Detection on Chest X-Ray and CT Scan Images Using Multi-image Augmented Deep Learning Model

Purohit, K., Kesarwani, A., Kisku, D. R., & Dalui, M. (2022) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-6890-6_30 · Cited by 82

Machine learning model to project the impact of COVID-19 on US motor gasoline demand

Ou, S. S., He, X., Ji, W., Chen, W., Sui, L., Gan, Y., Lü, Z., Lin, Z., Deng, S., Przesmitzki, S. V., et al. (2020) · Nature Energy · doi:10.1038/s41560-020-0662-1 · Cited by 81

Hypergraph learning for identification of COVID-19 with CT imaging

Di, D., Shi, F., Yan, F., Xia, L., Mo, Z., Ding, Z., Shan, F., Song, B., Li, S., Wei, Y., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101910 · Cited by 81

Analyzing Crude Oil Prices under the Impact of COVID-19 by Using LSTARGARCHLSTM

Bildirici, M. E., Bayazıt, N. G., & Uçan, Y. (2020) · Energies · doi:10.3390/en13112980 · Cited by 80

Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia—Challenges, strengths, and opportunities in a global health emergency

Ferrari, D., Milić, J., Tonelli, R., Ghinelli, F., Meschiari, M., Volpi, S., Faltoni, M., Franceschi, G., Iadisernia, V., Yaacoub, D., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0239172 · Cited by 80

Automated Systems for Detection of COVID-19 Using Chest X-ray Images and Lightweight Convolutional Neural NetworksPreprint

Alqudah, A. M., Qazan, S., & Alqudah, A. (2020) · doi:10.21203/rs.3.rs-24305/v1 · Cited by 80

A First Instagram Dataset on COVID-19Preprint

Zarei, K., Farahbakhsh, R., Crespi, N., & Gareth Tyson, A. (2020) · arXiv (Cornell University) · doi:10.13140/rg.2.2.13968.15364 · Cited by 80

The role of Artificial Intelligence in Management of Critical COVID-19 patients

Rahmatizadeh, S., Valizadeh-Haghi, S., & Dabbagh, A. (2020) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.22037/jcma.v5i1.29752 · Cited by 79

Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-ArtsPreprint

Pham, Q., Nguyen, D. C., Huynh‐The, T., et al. (2020) · Preprints.org · doi:10.20944/preprints202004.0383.v1 · Cited by 79

Online hate network spreads malicious COVID-19 content outside the control of individual social media platforms

Velásquez, N., Leahy, R., Restrepo, N. J., Lupu, Y., Sear, R. F., Gabriel, N., Jha, O. K., Goldberg, B., & Johnson, N. F. (2021) · Scientific Reports · doi:10.1038/s41598-021-89467-y · Cited by 79

Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis

Borkowski, A., Viswanadham, N. A., Thomas, L. B., Guzman, R. D., Deland, L. A., & Mastorides, S. (2020) · Federal Practitioner · doi:10.12788/fp.0045 · Cited by 78

CoroNet: A Deep Network Architecture for Semi-Supervised Task-Based Identification of COVID-19 from Chest X-ray ImagesPreprint

Khobahi, S., Agarwal, C., & Soltanalian, M. (2020) · medRxiv · doi:10.1101/2020.04.14.20065722 · Cited by 77

Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19

Zhu, G., Li, J., Meng, Z., Yu, Y., Li, Y., Tang, X., Dong, Y., Sun, G., Zhou, R., Wang, H., et al. (2020) · Discrete Dynamics in Nature and Society · doi:10.1155/2020/6152041 · Cited by 77

Repurposing therapeutics for COVID-19: Rapid prediction of commercially available drugs through machine learning and docking

Mohapatra, S., Nath, P., Chatterjee, M., Das, N., Kalita, D., Roy, P., & Satapathi, S. (2020) · PLoS ONE · doi:10.1371/journal.pone.0241543 · Cited by 77

ai-corona: Radiologist-assistant deep learning framework for COVID-19 diagnosis in chest CT scans

Yousefzadeh, M., Esfahanian, P., Movahed, M. S., Gorgin, S., Rahmati, D., Kiani, A., Nadji, S. A., Haseli, S., Hoseinyazdi, M., Roshandel, J., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0250952 · Cited by 77

Automatic Detection of COVID-19 Infection from Chest X-ray using Deep LearningPreprint

Medhi, K., Jamil, M., & Hussain, M. I. (2020) · medRxiv · doi:10.1101/2020.05.10.20097063 · Cited by 76

Deep learning based diagnosis of COVID-19 using chest CT-scan images

Anwar, T., & Zakir, S. (2020) · IEEE International Multitopic Conference (INMIC) · doi:10.1109/inmic50486.2020.9318212 · Cited by 76

A multicriteria approach for risk assessment of Covid-19 in urban district lockdown

Sangiorgio, V., & Parisi, F. (2020) · Safety Science · doi:10.1016/j.ssci.2020.104862 · Cited by 76

A machine learning application for raising WASH awareness in the times of COVID-19 pandemic

Pandey, R., Gautam, V., Pal, R., Bandhey, H., Dhingra, L. S., Misra, V., Sharma, H., Jain, C., Bhagat, K., Arushi, et al. (2022) · Scientific Reports · doi:10.1038/s41598-021-03869-6 · Cited by 76

Forecasting Covid-19 Dynamics in Brazil: A Data Driven Approach

Pereira, I. G., Guerin, J. M., Silva, A., Garcia, G. S., Piscitelli, P., Miani, A., Distante, C., & Gonçalves, L. M. G. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17145115 · Cited by 74

A Novel Triage Tool of Artificial Intelligence-Assisted Diagnosis Aid System for Suspected COVID-19 Pneumonia in Fever ClinicsPreprint

Feng, C., Wang, L., Chen, X., et al. (2020) · medRxiv · doi:10.1101/2020.03.19.20039099 · Cited by 74

IKONOS: an intelligent tool to support diagnosis of COVID-19 by texture analysis of X-ray images

Gomes, J. C., Barbosa, V. A. D. F., de Santana, M. A., Bandeira, J., Valença, M., de Souza, R. E., Ismael, A. M., & dos Santos, W. P. (2020) · Research on Biomedical Engineering · doi:10.1007/s42600-020-00091-7 · Cited by 74

Deep Learning Localization of Pneumonia

Hurt, B., Kligerman, S. J., & Hsiao, A. (2020) · Journal of Thoracic Imaging · doi:10.1097/rti.0000000000000512 · Cited by 73

Detection of Covid-19 From Chest X-ray Images Using Artificial Intelligence: An Early ReviewPreprint

Ilyas, M., Rehman, H., & Naït-Ali, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.05436 · Cited by 72

Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach

Carrillo-Larco, R. M., & Castillo‐Cara, M. (2020) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15819.3 · Cited by 71

COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19Preprint

Zhang, Y., Niu, S., Qiu, Z., Wei, Y., Zhao, P., Yao, J., Huang, J., Wu, Q., & Tan, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.01577 · Cited by 71

Artificial intelligence-enabled rapid diagnosis of COVID-19 patientsPreprint

Mei, X., Lee, H.-C., Diao, K., et al. (2020) · medRxiv · doi:10.1101/2020.04.12.20062661 · Cited by 71

A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation

Chen, Y., Ouyang, L., Bao, F. S., Li, Q., Han, L., Zhang, H., Zhu, B., Ge, Y., Robinson, P. A., Xu, M. J., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/23948 · Cited by 71

Machine Learning for COVID-19 needs global collaboration and data-sharing

Peiffer-Smadja, N., Maatoug, R., Lescure, F.-X., D’Ortenzio, E., Pineau, J., & King, J.-R. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0181-6 · Cited by 70

Machine learning for clinical trials in the era of COVID-19

Zame, W. R., Bica, I., Shen, C., Curth, A., Lee, H.-S., Bailey, S., Weatherall, J. H., Wright, D. N. M., Bretz, F., & van der Schaar, M. (2020) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2020.1797867 · Cited by 70

Prediction of the number of COVID-19 confirmed cases based on K-means-LSTM

Vadyala, S. R., Betgeri, S. N., Sherer, E. A., & Amritphale, A. (2021) · Array · doi:10.1016/j.array.2021.100085 · Cited by 70

COVID-19 Control by Computer Vision Approaches: A Survey

Ulhaq, A., Born, J., Khan, A. A., Gomes, D. P. S., Chakraborty, S., & Paul, M. (2020) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000446917 · Cited by 70

Neural Network Based Country Wise Risk Prediction of COVID-19Preprint

Pal, R., Sekh, A. A., Kar, S., et al. (2020) · Preprints.org · doi:10.20944/preprints202004.0421.v1 · Cited by 69

Determinants of the infection rate of the COVID-19 in the U.S. using ANFIS and virus optimization algorithm (VOA)

Behnood, A., Golafshani, E. M., & Hosseini, S. M. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110051 · Cited by 69

End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19: A Theoretical Framework

Belkacem, A. N., Ouhbi, S., Lakas, A., Benkhelifa, E., & Chen, C. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.585578 · Cited by 69

Identification of potential treatments for COVID-19 through artificial intelligence-enabled phenomic analysis of human cells infected with SARS-CoV-2Preprint

Heiser, K., McLean, P., Davis, C. T., Fogelson, B., Gordon, H. B., Jacobson, P., Hurst, B. L., Miller, B., Alfa, R. W., Earnshaw, B. A., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.21.054387 · Cited by 68

Classification aware neural topic model for COVID-19 disinformation categorisation

Song, X., Petrak, J., Jiang, Y., Singh, I., Maynard, D., & Bontcheva, K. (2021) · PLoS ONE · doi:10.1371/journal.pone.0247086 · Cited by 68

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906

Coronavirus disease (COVID-19) cases analysis using machine-learning applications

Kwekha-Rashid, A. S., Abduljabbar, H. N., & Alhayani, B. (2021) · Applied Nanoscience · doi:10.1007/s13204-021-01868-7 · Cited by 348

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Rahmani, A. M., Yousefpoor, E., Yousefpoor, M. S., Mehmood, Z., Haider, A., Hosseinzadeh, M., & Naqvi, R. A. (2021) · Mathematics · doi:10.3390/math9222970 · Cited by 289

Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review

Alballa, N., & Al-Turaiki, I. M. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100564 · Cited by 232

Surveillance and response strategies for zoonotic diseases: a comprehensive review

Sharan, M., Vijay, D., Yadav, J. P., Bedi, J. S., & Dhaka, P. (2023) · Science in One Health · doi:10.1016/j.soh.2023.100050 · Cited by 191

Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research DirectionsPreprint

Nguyen, T. T. (2020) · Figshare · doi:10.6084/m9.figshare.12127020

A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

Chen, J., Li, K., Zhang, Z., Li, K., & Yu, P. S. (2021) · ACM Computing Surveys · doi:10.1145/3465398 · Cited by 154

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

El-Rashidy, N. M., Abdelrazek, S. M., Abuhmed, T., Amer, E., Ali, F., Hu, J. W., & El–Sappagh, S. (2021) · Diagnostics · doi:10.3390/diagnostics11071155 · Cited by 62

Applications of Artificial Intelligence in Combating Covid-19: A Systematic Review

Enughwure, A. A., & Febaide, I. C. (2020) · OALib · doi:10.4236/oalib.1106628 · Cited by 17

A STUDY ON ARTIFICIAL INTELLIGENCE IN THE TIMES OF COVID-19

(2020) · International Journal for Research in Engineering Application & Management · doi:10.35291/2454-9150.2020.0469 · Cited by 1