AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review

Since the first case of coronavirus disease 2019 (COVID-19) was discovered in December 2019, COVID-19 swiftly spread over the world. By the end of March 2021, more than 136 million patients have been infected. Since the second and third waves of the COVID-19 outbreak are in full swing, investigating effective and timely solutions for patients’ check-ups and treatment is important. Although the SARS-CoV-2 virus-specific reverse transcription polymerase chain reaction test is recommended for the diagnosis of COVID-19, the test results are prone to be false negative in the early course of COVID-19 infection. To enhance the screening efficiency and accessibility, chest images captured via X-ray or computed tomography (CT) provide valuable information when evaluating patients with suspected COVID-19 infection. With advanced artificial intelligence (AI) techniques, AI-driven models training with lung scans emerge as quick diagnostic and screening tools for detecting COVID-19 infection in patients. In this article, we provide a comprehensive review of state-of-the-art AI-empowered methods for computational examination of COVID-19 patients with lung scans. In this regard, we searched for papers and preprints on bioRxiv, medRxiv, and arXiv published for the period from January 1, 2020, to March 31, 2021, using the keywords of COVID, lung scans, and AI. After the quality screening, 96 studies are included in this review. The reviewed studies were grouped into three categories based on their target application scenarios: automatic detection of coronavirus disease, infection segmentation, and severity assessment and prognosis prediction. The latest AI solutions to process and analyze chest images for COVID-19 treatment and their advantages and limitations are presented. In addition to reviewing the rapidly developing techniques, we also summarize publicly accessible lung scan image sets. The article ends with discussions of the challenges in current research and potential directions in designing effective computational solutions to fight against the COVID-19 pandemic in the future.

[1]  J. Crowcroft,et al.  Leveraging Data Science to Combat COVID-19: A Comprehensive Review , 2020, IEEE Transactions on Artificial Intelligence.

[2]  Fatemeh Homayounieh,et al.  CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image , 2020, ArXiv.

[3]  Lina Yao,et al.  Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images , 2020, ArXiv.

[4]  Mamun Bin Ibne Reaz,et al.  Can AI Help in Screening Viral and COVID-19 Pneumonia? , 2020, IEEE Access.

[5]  Mesut Toğaçar,et al.  COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches , 2020, Computers in Biology and Medicine.

[6]  François Laviolette,et al.  Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..

[7]  Mahmood Fathy,et al.  Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

[8]  A. Abbas,et al.  4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[9]  Asif Iqbal Khan,et al.  CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images , 2020, Computer Methods and Programs in Biomedicine.

[10]  Deniz Korkmaz,et al.  COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images , 2020, Medical Hypotheses.

[11]  Jianming Wang,et al.  AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system , 2020, Applied Soft Computing.

[12]  Farnoosh Naderkhani,et al.  COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images , 2020, Pattern Recognition Letters.

[13]  Taban F. Majeed,et al.  Problems of Deploying CNN Transfer Learning to Detect COVID-19 from Chest X-rays , 2020 .

[14]  Kazuhiro Takemoto,et al.  Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks , 2020, PloS one.

[15]  D. Dong,et al.  The Role of Imaging in the Detection and Management of COVID-19: A Review , 2020, IEEE Reviews in Biomedical Engineering.

[16]  Alexander Wong,et al.  COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images , 2020, Scientific reports.

[17]  Jianjiang Feng,et al.  Development and evaluation of an artificial intelligence system for COVID-19 diagnosis , 2020, Nature Communications.

[18]  Ioannis D. Apostolopoulos,et al.  Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases , 2020, Journal of Medical and Biological Engineering.

[19]  A. Tahamtan,et al.  Real-time RT-PCR in COVID-19 detection: issues affecting the results , 2020, Expert review of molecular diagnostics.

[20]  Lihua Li,et al.  A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis , 2020, IEEE Transactions on Medical Imaging.

[21]  Geoffrey E. Hinton,et al.  A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.

[22]  Tayyip Ozcan.  A Deep Learning Framework for Coronavirus Disease (COVID-19) Detection in X-Ray Images , 2020 .

[23]  U. Rajendra Acharya,et al.  Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks , 2020, Computers in Biology and Medicine.

[24]  Dinggang Shen,et al.  Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19 , 2020, IEEE Reviews in Biomedical Engineering.

[25]  U. Rajendra Acharya,et al.  Automated detection of COVID-19 cases using deep neural networks with X-ray images , 2020, Computers in Biology and Medicine.

[26]  Deepak Gupta,et al.  CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection , 2020, IEEE Access.

[27]  Ling Shao,et al.  Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images , 2020, IEEE Transactions on Medical Imaging.

[28]  W. Liang,et al.  Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography , 2020, Cell.

[29]  Antonella Santone,et al.  Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays , 2020, Computer Methods and Programs in Biomedicine.

[30]  Ming-Ming Cheng,et al.  JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation , 2020, IEEE Transactions on Image Processing.

[31]  Muhammet Fatih Aslan,et al.  CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection , 2020, Applied Soft Computing.

[32]  Sonali Agarwal,et al.  Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks , 2020, Applied Intelligence.

[33]  Youness Chawki,et al.  Using X-ray images and deep learning for automated detection of coronavirus disease , 2020, Journal of biomolecular structure & dynamics.

[34]  Mohamed Medhat Gaber,et al.  4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection , 2021, IEEE Transactions on Neural Networks and Learning Systems.

[35]  I. Hussain,et al.  Automatic Detection of COVID-19 Infection from Chest X-ray using Deep Learning , 2020, medRxiv.

[36]  N. Adami,et al.  End-to-end learning for semiquantitative rating of COVID-19 severity on Chest X-rays , 2020, ArXiv.

[37]  Akshay Pai,et al.  Lung Segmentation from Chest X-rays using Variational Data Imputation , 2020, ArXiv.

[38]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  Mohamed Medhat Gaber,et al.  Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network , 2020, Appl. Intell..

[40]  X. He,et al.  Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans , 2020, medRxiv.

[41]  Yuanjie Zheng,et al.  Robust Screening of COVID-19 from Chest X-ray via Discriminative Cost-Sensitive Learning , 2020, ArXiv.

[42]  Christian Etmann,et al.  Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans , 2020 .

[43]  Won-Joo Hwang,et al.  Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts , 2020, IEEE Access.

[44]  M. Kuo,et al.  Frequency and Distribution of Chest Radiographic Findings in COVID-19 Positive Patients , 2019, Radiology.

[45]  Haibo Xu,et al.  AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks , 2020, medRxiv.

[46]  Saleh Albahli,et al.  Efficient GAN-based Chest Radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia , 2020, International journal of medical sciences.

[47]  D. Giambelluca,et al.  Radiographic and chest CT imaging presentation and follow-up of COVID-19 pneumonia: a multicenter experience from an endemic area , 2020, Emergency Radiology.

[48]  Joseph Paul Cohen,et al.  COVID-19 Image Data Collection: Prospective Predictions Are the Future , 2020, ArXiv.

[49]  Yoshua Bengio,et al.  Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning , 2020, Cureus.

[50]  A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19) , 2021, European Radiology.

[51]  Sheng Zhang,et al.  Profile of RT-PCR for SARS-CoV-2: A Preliminary Study From 56 COVID-19 Patients , 2020, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[52]  Manjit Kaur,et al.  Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays , 2020, IRBM.

[53]  Hadi Karimi Mobin,et al.  Integrative analysis for COVID-19 patient outcome prediction. , 2020, Medical Image Analysis.

[54]  X. He,et al.  Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans , 2020, medRxiv.

[55]  Yinghuan Shi,et al.  A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning , 2021, Medical Image Analysis.

[56]  S. Balakrishnan,et al.  Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients. , 2020, AJR. American journal of roentgenology.

[57]  Wei Zhang,et al.  Accurate Screening of COVID-19 Using Attention-Based Deep 3D Multiple Instance Learning , 2020, IEEE Transactions on Medical Imaging.

[58]  P. Xie,et al.  COVID-CT-Dataset: A CT Scan Dataset about COVID-19 , 2020, ArXiv.

[59]  Junaid Qadir,et al.  Leveraging Data Science to Combat COVID-19: A Comprehensive Review , 2020, IEEE Transactions on Artificial Intelligence.

[60]  Miguel Cazorla,et al.  BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients , 2020, ArXiv.

[61]  Meng Niu,et al.  A Deep Learning Prognosis Model Help Alert for COVID-19 Patients at High-Risk of Death: A Multi-Center Study , 2020, IEEE Journal of Biomedical and Health Informatics.

[62]  Samrat Kumar Dey,et al.  COVID faster R–CNN: A novel framework to Diagnose Novel Coronavirus Disease (COVID-19) in X-Ray images , 2020, Informatics in Medicine Unlocked.

[63]  Petia Radeva,et al.  Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images , 2020, ArXiv.

[64]  ProgNet: COVID-19 Prognosis Using Recurrent and Convolutional Neural Networks , 2020 .

[65]  Su Ruan,et al.  Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation , 2020, Computers in Biology and Medicine.

[66]  Zhibin Liao,et al.  Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection , 2020, IEEE Transactions on Medical Imaging.

[67]  Yi Tao,et al.  Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Network , 2020, 2020 IEEE 6th International Conference on Computer and Communications (ICCC).

[68]  Ioannis D. Apostolopoulos,et al.  Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks , 2020, Physical and Engineering Sciences in Medicine.

[69]  Yan Bai,et al.  A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis , 2020, European Respiratory Journal.

[70]  L. Xia,et al.  CT Features of Coronavirus Disease 2019 (COVID-19) Pneumonia in 62 Patients in Wuhan, China. , 2020, AJR. American journal of roentgenology.

[71]  T. Majeed,et al.  Covid-19 Detection using CNN Transfer Learning from X-ray Images , 2020, medRxiv.

[72]  S. Rajaraman,et al.  Weakly Labeled Data Augmentation for Deep Learning: A Study on COVID-19 Detection in Chest X-Rays , 2020, Diagnostics.

[73]  Serkan Kiranyaz,et al.  A Comparative Study on Early Detection of COVID-19 from Chest X-Ray Images , 2020, ArXiv.

[74]  Kesari Verma,et al.  Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble , 2020, Expert Systems with Applications.

[75]  Yaozong Gao,et al.  Hypergraph learning for identification of COVID-19 with CT imaging , 2020, Medical Image Analysis.

[76]  Fatemeh Homayounieh,et al.  CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images , 2021, npj Digital Medicine.

[77]  S. Kumar,et al.  Deep Transfer Learning-Based COVID-19 Prediction Using Chest X-Rays , 2020, medRxiv.

[78]  Yan Bai,et al.  Presumed Asymptomatic Carrier Transmission of COVID-19. , 2020, JAMA.

[79]  Bingliang Zeng,et al.  Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT? , 2020, European Journal of Radiology.

[80]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[81]  Florentin Smarandache,et al.  A Study of the Neutrosophic Set Significance on Deep Transfer Learning Models: an Experimental Case on a Limited COVID-19 Chest X-ray Dataset , 2020, Cognitive computation.

[82]  Md. Milon Islam,et al.  A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images , 2020, Informatics in Medicine Unlocked.

[83]  Edward H. Lee,et al.  Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT , 2021, npj Digital Medicine.

[84]  Shuangjia Zheng,et al.  Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images , 2021, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[85]  M. Kalra,et al.  Association of AI quantified COVID-19 chest CT and patient outcome , 2021, International Journal of Computer Assisted Radiology and Surgery.

[86]  Zhiyong Xu,et al.  A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images , 2020, IEEE Transactions on Medical Imaging.

[87]  Anvar Kurmukov,et al.  CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification , 2020, ArXiv.

[88]  Brian D Goodwin,et al.  Intra-model Variability in COVID-19 Classification Using Chest X-ray Images , 2020, ArXiv.

[89]  Jieli Zhou,et al.  SODA: Detecting Covid-19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation , 2020, ArXiv.

[90]  A. Rahmim,et al.  Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients , 2021, Computers in Biology and Medicine.

[91]  Milan Sonka,et al.  Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning , 2020, Medical Image Analysis.

[92]  Yi Li,et al.  Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection , 2020 .

[93]  T. Anwar,et al.  Deep learning based diagnosis of COVID-19 using chest CT-scan images , 2020 .

[94]  Youngjin Yoo,et al.  3D Tomographic Pattern Synthesis for Enhancing the Quantification of COVID-19 , 2020, ArXiv.

[95]  William Parker,et al.  A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images , 2020, ArXiv.

[96]  Mohammad Rahimzadeh,et al.  A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset , 2021, Biomedical Signal Processing and Control.

[97]  Manxiang Li,et al.  Clinical diagnostic value of CT imaging in COVID-19 with multiple negative RT-PCR testing , 2020, Travel Medicine and Infectious Disease.