Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

ImageNet: A large-scale hierarchical image database

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Rethinking the Inception Architecture for Computer Vision

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Clinical characteristics of 138 hospitalized patients with 2019 Novel Coronavirus-Infected pneumonia in Wuhan, China

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Generative Adversarial Nets

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GAN（Generative Adversarial Nets）

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La certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19

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The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration

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EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

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QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies

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The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration

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Decision curve analysis: a novel method for evaluating prediction models

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Radiomics: the bridge between medical imaging and personalized medicine

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Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases

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Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

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Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement

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Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR

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Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement

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Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection

Wynants, L., Van Calster, B., Bonten, M. M., Collins, G. S., Debray, T. P., De Vos, M., Haller, M. C., Heinze, G., Moons, K. G., Riley, R. D., et al. (2020) · doi:10.1101/2020.03.24.20041020 · Cited by 3,070

PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies

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COVID-19 and the cardiovascular system

Zheng, Y.-Y., Ma, Y.-T., Zhang, J.-Y., & Xie, X. (2020) · Nature Reviews Cardiology · doi:10.1038/s41569-020-0360-5 · Cited by 2,970

A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)

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Frequency and Distribution of Chest Radiographic Findings in Patients Positive for COVID-19

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Checklist for Artificial Intelligence in Medical Imaging (CLAIM): a guide for authors and reviewers

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Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19

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Artificial intelligence–enabled rapid diagnosis of patients with COVID-19

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Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT?

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Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

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COVID-19 Image Data Collection

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COVID-CT-Dataset: A CT Scan Dataset about COVID-19

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Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation

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AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system

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A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis

Wang, S., Zha, Y., Li, W., Wu, Q., Li, X., Niu, M., Wang, M., Qiu, X., Li, H., Yu, H., et al. (2020) · European Respiratory Journal · doi:10.1183/13993003.00775-2020 · Cited by 490

Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms

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COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs

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New machine learning method for image-based diagnosis of COVID-19

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CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

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Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT

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Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection

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Accurate Screening of COVID-19 Using Attention-Based Deep 3D Multiple Instance Learning

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Diagnosis of COVID-19 using CT scan images and deep learning techniques

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Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches

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Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects

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COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation

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A critic evaluation of methods for COVID-19 automatic detection from X-ray images

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Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning

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Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images

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Machine learning-based CT radiomics method for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: a multicenter study

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Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification

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An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization

Ezzat, D., Hassanien, A. E., & Aboul-Ella, H. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106742 · Cited by 227

Unveiling COVID-19 from CHEST X-ray with deep learning: A hurdles race with small data

Tartaglione, E., Barbano, C. A., Berzovini, C., Calandri, M., & Grangetto, M. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17186933 · Cited by 211

Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients

Lassau, N., Ammari, S., Chouzenoux, É., Gortais, H., Hérent, P., Devilder, M., Soliman, S., Meyrignac, O., Talabard, M.-P., Lamarque, J.-P., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20657-4 · Cited by 198

Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays

Wang, Z., Xiao, Y., Li, Y., Zhang, J., Lu, F., Hou, M., & Liu, X. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107613 · Cited by 190

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 185

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, E., Andre, F., Guillo, E., Halm, N., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101860 · Cited by 169

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

Li, M. D., Arun, N. T., Gidwani, M., Chang, K., Deng, F., 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 161

Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method

Huang, P., Lin, C. T., Li, Y., Tammemägi, M. C., Brock, M. V., Atkar-Khattra, S., Xu, Y., Hu, P., Mayo, J. R., Schmidt, H., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30159-1 · Cited by 160

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 149

Radiomics Analysis of Computed Tomography helps predict poor prognostic outcome in COVID-19

Wu, Q., Wang, S., Li, L., Wu, Q., Qian, W., Hu, Y., Li, L., Zhou, X., Ma, H., Li, H., et al. (2020) · Theranostics · doi:10.7150/thno.46428 · Cited by 131

A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study

Chen, X., Tang, Y., Mo, Y., Li, S., Lin, D., Yang, Z., Yang, Z., Sun, H., Qiu, J., Liao, Y., et al. (2020) · European Radiology · doi:10.1007/s00330-020-06829-2 · Cited by 127

Diagnosis of Coronavirus Disease 2019 Pneumonia by Using Chest Radiography: Value of Artificial Intelligence

Zhang, R., Tie, X., Qi, Z., Bevins, N., Zhang, C., Griner, D., Song, T. K., Nadig, J., Schiebler, M. L., Garrett, J. W., et al. (2020) · Radiology · doi:10.1148/radiol.2020202944 · Cited by 126

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 121

Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19

Schalekamp, S., Huisman, M., van Dijk, R. A., Boomsma, M. F., Jorge, P. F., de Boer, W. S., Herder, G. J. M., Bonarius, M., Groot, O. A., Jong, E., et al. (2020) · Radiology · doi:10.1148/radiol.2020202723 · Cited by 96

Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation

Wang, M., Chen, X., Huang, L., Xu, S., Qin, C., Liu, J., Cao, Y., Yu, P., Zhu, T., Zhu, H., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30199-0 · Cited by 88

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 86

Multi-task driven explainable diagnosis of COVID-19 using chest X-ray images

Malhotra, A., Mittal, S., Majumdar, P., Chhabra, S., Thakral, K., Mayank Vatsa, A., Singh, R., Chaudhury, S., Pudrod, A., & Agrawal, A. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108243 · Cited by 79

Integrative analysis for COVID-19 patient outcome prediction

Chao, H., Fang, X., Zhang, J., Homayounieh, F., Arru, C. D., Digumarthy, S. R., Babaei, R., Mobin, H. K., Mohseni, I., Saba, L., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101844 · Cited by 77

Prediction models for diagnosis and prognosis in Covid-19

Sperrin, M., Grant, S. W., & Peek, N. B. (2020) · BMJ · doi:10.1136/bmj.m1464 · Cited by 74

Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan

Zhu, X., Song, B., Shi, F., Chen, Y., Hu, R., Gan, J., Zhang, W., Li, M., Wang, L., Gao, Y., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101824 · Cited by 74

COVID-19 in CXR: From Detection and Severity Scoring to Patient Disease Monitoring

Frid-Adar, M., Amer, R., Gozes, O., Nassar, J., & Greenspan, H. K. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3069169 · Cited by 70

Identification of common and severe COVID-19: the value of CT texture analysis and correlation with clinical characteristics

Wei, W., Hu, X.-W., Cheng, Q., Zhao, Y.-M., & Ge, Y. (2020) · European Radiology · doi:10.1007/s00330-020-07012-3 · Cited by 67

Machine learning for COVID-19—asking the right questions

Bächtiger, P., Peters, N. S., & Walsh, S. L. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30162-x · Cited by 65

Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach

Xu, M. J., Ouyang, L., Gao, Y., Chen, Y., Yu, T., Li, Q., Sun, K., Bao, F. S., Safarnejad, L., Wen, J., et al. (2020) · Journal of Medical Internet Research · doi:10.2196/25535 · Cited by 64

COVID-MobileXpert: On-Device COVID-19 Screening using Snapshots of Chest X-RayPreprint

Li, X. S., Li, C., & Zhu, D. (2020) · arXiv (Cornell University) · Cited by 62

Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19

Guiot, J., Vaidyanathan, A., Deprez, L., Zerka, F., Danthine, D., Frix, A.-N., Thys, M., Henket, M. A., Canivet, G., Mathieu, S., et al. (2020) · Diagnostics · doi:10.3390/diagnostics11010041 · Cited by 58

A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19

Qin, L., Yang, Y., Cao, Q., Cheng, Z., Wang, X., Sun, Q., Yan, F., Qu, J., & Yang, W. (2020) · European Radiology · doi:10.1007/s00330-020-07022-1 · Cited by 57

COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images

Zhang, R., Guo, Z., Sun, Y., Lu, Q., Xu, Z., Yao, Z., Duan, M., Liu, S., Ren, Y., Huang, L., et al. (2020) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-020-00393-5 · Cited by 56

Improving effectiveness of different deep learning-based models for detecting COVID-19 from computed tomography (CT) images

Acar, E., Şahin, E., & Yılmaz, İ. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06344-5 · Cited by 52

An integrated framework with machine learning and radiomics for accurate and rapid early diagnosis of COVID-19 from Chest X-ray

Tamal, M., AlShammari, M. M., Alabdullah, M., Hourani, R., Alola, H. A., & Hegazi, T. M. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115152 · Cited by 48

Any unique image biomarkers associated with COVID-19?

Pu, J., Leader, J. K., Bandos, A. I., Shi, J., Du, P., Yu, J., Yang, B., Shi, K., Guo, Y., Field, J. B., et al. (2020) · European Radiology · doi:10.1007/s00330-020-06956-w · Cited by 43

A Quantitative and Radiomics approach to monitoring ARDS in COVID-19 patients based on chest CT: a retrospective cohort study

Chen, Y., Wang, Y., Zhang, Y., Zhang, N., Zhao, S., Zeng, H., Deng, W., Huang, Z., Liu, S., & Song, B. (2020) · International Journal of Medical Sciences · doi:10.7150/ijms.48432 · Cited by 42

GraphXCOVID: Explainable deep graph diffusion pseudo-Labelling for identifying COVID-19 on chest X-rays

Avilés-Rivero, A. I., Sellars, P., Schönlieb, C., & Papadakis, N. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108274 · Cited by 39

Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort

Barbosa, E. J. M., Georgescu, B., Chaganti, S., Bastarrika, G., Cabrero, J. B., Chabin, G., Flohr, T. G., Greniér, P., Grbić, S., Gupta, N., et al. (2021) · European Radiology · doi:10.1007/s00330-021-07937-3 · Cited by 35

Artificial intelligence techniques for Containment COVID-19 Pandemic: A Systematic ReviewPreprint

Alabool, H. M., Alarabiat, D., Abualigah, L., Habib, M., Al-Khasawneh, A., Alshinwan, M., & Shehab, M. (2020) · doi:10.21203/rs.3.rs-30432/v1 · Cited by 33

AIDCOV: An Interpretable Artificial Intelligence Model for Detection of COVID-19 from Chest Radiography Images

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Discrimination of pulmonary ground-glass opacity changes in COVID‐19 and non-COVID-19 patients using CT radiomics analysis

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