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Singh K, Kaur N, Prabhu A. Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review. Curr Top Med Chem 2024; 24:737-753. [PMID: 38318824 DOI: 10.2174/0115680266282179240124072121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 12/19/2023] [Accepted: 12/27/2023] [Indexed: 02/07/2024]
Abstract
BACKGROUND SARS-CoV-2, the unique coronavirus that causes COVID-19, has wreaked damage around the globe, with victims displaying a wide range of difficulties that have encouraged medical professionals to look for innovative technical solutions and therapeutic approaches. Artificial intelligence-based methods have contributed a significant part in tackling complicated issues, and some institutions have been quick to embrace and tailor these solutions in response to the COVID-19 pandemic's obstacles. Here, in this review article, we have covered a few DL techniques for COVID-19 detection and diagnosis, as well as ML techniques for COVID-19 identification, severity classification, vaccine and drug development, mortality rate prediction, contact tracing, risk assessment, and public distancing. This review illustrates the overall impact of AI/ML tools on tackling and managing the outbreak. PURPOSE The focus of this research was to undertake a thorough evaluation of the literature on the part of Artificial Intelligence (AI) as a complete and efficient solution in the battle against the COVID-19 epidemic in the domains of detection and diagnostics of disease, mortality prediction and vaccine as well as drug development. METHODS A comprehensive exploration of PubMed, Web of Science, and Science Direct was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) regulations to find all possibly suitable papers conducted and made publicly available between December 1, 2019, and August 2023. COVID-19, along with AI-specific words, was used to create the query syntax. RESULTS During the period covered by the search strategy, 961 articles were published and released online. Out of these, a total of 135 papers were chosen for additional investigation. Mortality rate prediction, early detection and diagnosis, vaccine as well as drug development, and lastly, incorporation of AI for supervising and controlling the COVID-19 pandemic were the four main topics focused entirely on AI applications used to tackle the COVID-19 crisis. Out of 135, 60 research papers focused on the detection and diagnosis of the COVID-19 pandemic. Next, 19 of the 135 studies applied a machine-learning approach for mortality rate prediction. Another 22 research publications emphasized the vaccine as well as drug development. Finally, the remaining studies were concentrated on controlling the COVID-19 pandemic by applying AI AI-based approach to it. CONCLUSION We compiled papers from the available COVID-19 literature that used AI-based methodologies to impart insights into various COVID-19 topics in this comprehensive study. Our results suggest crucial characteristics, data types, and COVID-19 tools that can aid in medical and translational research facilitation.
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Affiliation(s)
- Kavya Singh
- Department of Biotechnology, Banasthali University, Banasthali Vidyapith, Banasthali, 304022, Rajasthan, India
| | - Navjeet Kaur
- Department of Chemistry & Division of Research and Development, Lovely Professional University, Phagwara, 144411, Punjab, India
| | - Ashish Prabhu
- Biotechnology Department, NIT Warangal, Warangal, 506004, Telangana, India
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Lou L, Liang H, Wang Z. Deep-Learning-Based COVID-19 Diagnosis and Implementation in Embedded Edge-Computing Device. Diagnostics (Basel) 2023; 13:diagnostics13071329. [PMID: 37046553 PMCID: PMC10093656 DOI: 10.3390/diagnostics13071329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/24/2023] [Accepted: 03/28/2023] [Indexed: 04/07/2023] Open
Abstract
The rapid spread of coronavirus disease 2019 (COVID-19) has posed enormous challenges to the global public health system. To deal with the COVID-19 pandemic crisis, the more accurate and convenient diagnosis of patients needs to be developed. This paper proposes a deep-learning-based COVID-19 detection method and evaluates its performance on embedded edge-computing devices. By adding an attention module and mixed loss into the original VGG19 model, the method can effectively reduce the parameters of the model and increase the classification accuracy. The improved model was first trained and tested on the PC X86 GPU platform using a large dataset (COVIDx CT-2A) and a medium dataset (integrated CT scan); the weight parameters of the model were reduced by around six times compared to the original model, but it still approximately achieved 98.80%and 97.84% accuracy, outperforming most existing methods. The trained model was subsequently transferred to embedded NVIDIA Jetson devices (TX2, Nano), where it achieved 97% accuracy at a 0.6−1 FPS inference speed using the NVIDIA TensorRT engine. The experimental results demonstrate that the proposed method is practicable and convenient; it can be used on a low-cost medical edge-computing terminal. The source code is available on GitHub for researchers.
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Affiliation(s)
- Lu Lou
- School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China
| | - Hong Liang
- School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China
| | - Zhengxia Wang
- School of Computer Science and Technology, Hainan University, Haikou 570100, China
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Rajaram-Gilkes M, Shariff H, Adamski N, Costan S, Taglieri M, Loukas M, Tubbs RS. A Review of Crucial Radiological Investigations in the Management of COVID-19 Cases. Cureus 2023; 15:e36825. [PMID: 37123693 PMCID: PMC10139823 DOI: 10.7759/cureus.36825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/28/2023] [Indexed: 03/30/2023] Open
Abstract
Chest X-ray, chest CT, and lung ultrasound are the most common radiological interventions used in the diagnosis and management of coronavirus disease 2019 (COVID-19) patients. The purpose of this literature review, which was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, is to determine which radiological investigation is crucial for that purpose. PubMed, Medline, American Journal of Radiology (AJR), Public Library of Science (PLOS), Elsevier, National Center for Biotechnology Information (NCBI), and ScienceDirect were explored. Seventy-two articles were reviewed for potential inclusion, including 50 discussing chest CT, 15 discussing chest X-ray, five discussing lung ultrasound, and two discussing COVID-19 epidemiology. The reported sensitivities and specificities for chest CT ranged from 64 to 98% and 25 to 88%, respectively. The reported sensitivities and specificities for chest X-rays ranged from 33 to 89% and 11.1 to 88.9%, respectively. The reported sensitivities and specificities for lung ultrasound ranged from 93 to 96.8% and 21.3 to 95%, respectively. The most common findings on chest CT include ground glass opacities and consolidation. The most common findings on chest X-rays include opacities, consolidation, and pleural effusion. The data indicate that chest CT is the most effective radiological tool for the diagnosis and management of COVID-19 patients. The authors support the continued use of reverse transcription polymerase chain reaction (RT-PCR), along with physical examination and contact history, for such diagnosis. Chest CT could be more appropriate in emergency situations when quick triage of patients is necessary before RT-PCR results are available. CT can also be used to visualize the progression of COVID-19 pneumonia and to identify potential false positive RT-PCR results. Chest X-ray and lung ultrasound are acceptable in situations where chest CT is unavailable or contraindicated.
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Vinod DN, Prabaharan SRS. COVID-19-The Role of Artificial Intelligence, Machine Learning, and Deep Learning: A Newfangled. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:2667-2682. [PMID: 36685135 PMCID: PMC9843670 DOI: 10.1007/s11831-023-09882-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 01/05/2023] [Indexed: 05/29/2023]
Abstract
The absolute previously infected novel coronavirus (COVID-19) was found in Wuhan, China, in December 2019. The COVID-19 epidemic has spread to more than 220 nations and territories globally and has altogether influenced each part of our day-to-day lives. As of 9th March 2022, a total aggregate of 44,78,82,185 (60,07,317) contaminated (dead) COVID-19 cases were accounted for all over the world. The quantities of contaminated cases passing despite everything increment essentially and do not indicate a controlled circumstance. The scope of this paper is to address this issue by presenting a comprehensive and comparative analysis of the existing Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) based approaches used in significance in reacting to the COVID-19 epidemic and diagnosing the severe impacts. The paper provides, firstly, an overview of COVID-19 infection and highlights of this article; Secondly, an overview of exploring various executive innovations by utilizing different resources to stop the spread of COVID-19; Thirdly, a comparison of existing predicting methods of COVID-19 in the literature, with focus on ML, DL and AI-driven techniques with performance metrics; and finally, a discussion on the results of the work as well as future scope.
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Affiliation(s)
- Dasari Naga Vinod
- Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu 600062 India
| | - S. R. S. Prabaharan
- Sathyabama Centre for Advanced Studies, Sathyabama Institute of Science and Technology, Rajiv Gandhi Salai, Chennai, Tamil Nadu 600119 India
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5
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Topff L, Sánchez-García J, López-González R, Pastor AJ, Visser JJ, Huisman M, Guiot J, Beets-Tan RGH, Alberich-Bayarri A, Fuster-Matanzo A, Ranschaert ER. A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative. PLoS One 2023; 18:e0285121. [PMID: 37130128 PMCID: PMC10153726 DOI: 10.1371/journal.pone.0285121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 04/15/2023] [Indexed: 05/03/2023] Open
Abstract
BACKGROUND Recently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19). OBJECTIVES To develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity. METHODS The Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected. RESULTS A total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user. CONCLUSION We developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans.
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Affiliation(s)
- Laurens Topff
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | | | | | | | - Jacob J Visser
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Merel Huisman
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Julien Guiot
- Department of Pneumology, University Hospital of Liège (CHU Liège), Liège, Belgium
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | | | | | - Erik R Ranschaert
- Department of Radiology, St. Nikolaus Hospital, Eupen, Belgium
- Ghent University, Ghent, Belgium
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6
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Ashton RE, Philips BE, Faghy M. The acute and chronic implications of the COVID-19 virus on the cardiovascular system in adults: A systematic review. Prog Cardiovasc Dis 2023; 76:31-37. [PMID: 36690284 PMCID: PMC9854143 DOI: 10.1016/j.pcad.2023.01.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 01/17/2023] [Indexed: 01/21/2023]
Abstract
Despite coronavirus disease 2019 (COVID-19) primarily being identified as a respiratory illness, some patients who seemingly recovered from initial infection, developed chronic multi-system complications such as cardiovascular (CV), pulmonary and neurological issues leading to multiple organ injuries. However, to date, there is a dearth of understanding of the acute and chronic implications of a COVID-19 infection on the CV system in adults. A systematic review of the literature was conducted according to PRISMA guidelines and prospectively registered via Prospero (ID: CRD42022360444). The MEDLINE Ovid, Cochrane Library and PubMed databases were searched from inception to August 2022. The search strategy keywords and MeSH terms used included: 1) COVID; 2) coronavirus; 3) long COVID; 4) cardiovascular; and 5) cardiovascular disease. Reference lists of all relevant systematic reviews identified were searched for additional studies. A total of 11,332 records were retrieved from database searches, of which 310 records were duplicates. A further 9887 were eliminated following screening of titles and abstracts. After full-text screening of 1135 articles, 9 manuscripts were included for review. The evidence of CV implications post-COVID-19 infection is clear, and this must be addressed with appropriate management strategies that recognise the acute and chronic nature of cardiac injury in COVID-19 patients. Efficacious management strategies will be needed to address long standing issues and morbidity.
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Affiliation(s)
- Ruth E Ashton
- Biomedical Research Theme, School of Human Sciences, University of Derby, Derby, UK; Healthy Living for Pandemic Event Protection (HL - PIVOT) Network, Chicago, IL, United States of America.
| | - Bethan E Philips
- MRC-Versus Arthritis Centre for Musculoskeletal Ageing Research and National Institute for Health Research Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, Derby, UK
| | - Mark Faghy
- Biomedical Research Theme, School of Human Sciences, University of Derby, Derby, UK; Healthy Living for Pandemic Event Protection (HL - PIVOT) Network, Chicago, IL, United States of America; Department of Physical Therapy, College of Applied Sciences, University of Illinois at Chicago, Chicago, IL, United States of America
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7
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de Margerie-Mellon C, Chassagnon G. Artificial intelligence: A critical review of applications for lung nodule and lung cancer. Diagn Interv Imaging 2023; 104:11-17. [PMID: 36513593 DOI: 10.1016/j.diii.2022.11.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) is a broad concept that usually refers to computer programs that can learn from data and perform certain specific tasks. In the recent years, the growth of deep learning, a successful technique for computer vision tasks that does not require explicit programming, coupled with the availability of large imaging databases fostered the development of multiple applications in the medical imaging field, especially for lung nodules and lung cancer, mostly through convolutional neural networks (CNN). Some of the first applications of AI is this field were dedicated to automated detection of lung nodules on X-ray and computed tomography (CT) examinations, with performances now reaching or exceeding those of radiologists. For lung nodule segmentation, CNN-based algorithms applied to CT images show excellent spatial overlap index with manual segmentation, even for irregular and ground glass nodules. A third application of AI is the classification of lung nodules between malignant and benign, which could limit the number of follow-up CT examinations for less suspicious lesions. Several algorithms have demonstrated excellent capabilities for the prediction of the malignancy risk when a nodule is discovered. These different applications of AI for lung nodules are particularly appealing in the context of lung cancer screening. In the field of lung cancer, AI tools applied to lung imaging have been investigated for distinct aims. First, they could play a role for the non-invasive characterization of tumors, especially for histological subtype and somatic mutation predictions, with a potential therapeutic impact. Additionally, they could help predict the patient prognosis, in combination to clinical data. Despite these encouraging perspectives, clinical implementation of AI tools is only beginning because of the lack of generalizability of published studies, of an inner obscure working and because of limited data about the impact of such tools on the radiologists' decision and on the patient outcome. Radiologists must be active participants in the process of evaluating AI tools, as such tools could support their daily work and offer them more time for high added value tasks.
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Affiliation(s)
- Constance de Margerie-Mellon
- Université Paris Cité, Laboratory of Imaging Biomarkers, Center for Research on Inflammation, UMR 1149, INSERM, 75018 Paris, France; Department of Radiology, Hôpital Saint-Louis APHP, 75010 Paris, France
| | - Guillaume Chassagnon
- Université Paris Cité, Faculté de Médecine, 75006 Paris, France; Department of Radiology, Hôpital Cochin APHP, 75014 Paris, France
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Al-Shourbaji I, Kachare PH, Abualigah L, Abdelhag ME, Elnaim B, Anter AM, Gandomi AH. A Deep Batch Normalized Convolution Approach for Improving COVID-19 Detection from Chest X-ray Images. Pathogens 2022; 12:pathogens12010017. [PMID: 36678365 PMCID: PMC9860560 DOI: 10.3390/pathogens12010017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/12/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022] Open
Abstract
Pre-trained machine learning models have recently been widely used to detect COVID-19 automatically from X-ray images. Although these models can selectively retrain their layers for the desired task, the output remains biased due to the massive number of pre-trained weights and parameters. This paper proposes a novel batch normalized convolutional neural network (BNCNN) model to identify COVID-19 cases from chest X-ray images in binary and multi-class frameworks with a dual aim to extract salient features that improve model performance over pre-trained image analysis networks while reducing computational complexity. The BNCNN model has three phases: Data pre-processing to normalize and resize X-ray images, Feature extraction to generate feature maps, and Classification to predict labels based on the feature maps. Feature extraction uses four repetitions of a block comprising a convolution layer to learn suitable kernel weights for the features map, a batch normalization layer to solve the internal covariance shift of feature maps, and a max-pooling layer to find the highest-level patterns by increasing the convolution span. The classifier section uses two repetitions of a block comprising a dense layer to learn complex feature maps, a batch normalization layer to standardize internal feature maps, and a dropout layer to avoid overfitting while aiding the model generalization. Comparative analysis shows that when applied to an open-access dataset, the proposed BNCNN model performs better than four other comparative pre-trained models for three-way and two-way class datasets. Moreover, the BNCNN requires fewer parameters than the pre-trained models, suggesting better deployment suitability on low-resource devices.
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Affiliation(s)
- Ibrahim Al-Shourbaji
- Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
| | - Pramod H. Kachare
- Department of Electronics & Telecommunication Engineering, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai 400706, Maharashtra, India
| | - Laith Abualigah
- Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
- Faculty of Information Technology, Middle East University, Amman 11831, Jordan
- Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan
- School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
- Correspondence: (L.A.); (A.H.G.)
| | - Mohammed E. Abdelhag
- Department of Information Technology and Security, Jazan University, Jazan 45142, Saudi Arabia
| | - Bushra Elnaim
- Department of Computer Science, College of Science and Humanities in Al-Sulail, Prince Sattam bin Abdulaziz University, Riyadh 11671, Saudi Arabia
| | - Ahmed M. Anter
- Egypt-Japan University of Science and Technology (E-JUST), Alexandria 21934, Egypt
- Faculty of Computers and Artificial Intelligence, Beni-Suef University, Benisuef 62511, Egypt
| | - Amir H. Gandomi
- Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia
- University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary
- Correspondence: (L.A.); (A.H.G.)
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Batra S, Sharma H, Boulila W, Arya V, Srivastava P, Khan MZ, Krichen M. An Intelligent Sensor Based Decision Support System for Diagnosing Pulmonary Ailment through Standardized Chest X-ray Scans. SENSORS (BASEL, SWITZERLAND) 2022; 22:7474. [PMID: 36236573 PMCID: PMC9571822 DOI: 10.3390/s22197474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/28/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
Academics and the health community are paying much attention to developing smart remote patient monitoring, sensors, and healthcare technology. For the analysis of medical scans, various studies integrate sophisticated deep learning strategies. A smart monitoring system is needed as a proactive diagnostic solution that may be employed in an epidemiological scenario such as COVID-19. Consequently, this work offers an intelligent medicare system that is an IoT-empowered, deep learning-based decision support system (DSS) for the automated detection and categorization of infectious diseases (COVID-19 and pneumothorax). The proposed DSS system was evaluated using three independent standard-based chest X-ray scans. The suggested DSS predictor has been used to identify and classify areas on whole X-ray scans with abnormalities thought to be attributable to COVID-19, reaching an identification and classification accuracy rate of 89.58% for normal images and 89.13% for COVID-19 and pneumothorax. With the suggested DSS system, a judgment depending on individual chest X-ray scans may be made in approximately 0.01 s. As a result, the DSS system described in this study can forecast at a pace of 95 frames per second (FPS) for both models, which is near to real-time.
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Affiliation(s)
- Shivani Batra
- Department of Computer Science and Engineering, KIET Group of Institutions, Ghaziabad 201206, India
| | - Harsh Sharma
- Department of Computer Science and Engineering, KIET Group of Institutions, Ghaziabad 201206, India
| | - Wadii Boulila
- Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 12435, Saudi Arabia
- RIADI Laboratory, National School of Computer Sciences, University of Manouba, Manouba 2010, Tunisia
| | - Vaishali Arya
- School of Engineering, GD Goenka University, Gurugram 122103, India
| | - Prakash Srivastava
- Department of Computer Science and Engineering, Graphic Era (Deemed to Be University), Dehradun 248002, India
| | - Mohammad Zubair Khan
- Department of Computer Science and Information, Taibah University, Medina 42353, Saudi Arabia
| | - Moez Krichen
- Faculty of Computer Science & IT, Al Baha University, Al Baha 65779, Saudi Arabia
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Pellegrino F, Carnevale A, Bisi R, Cavedagna D, Reverberi R, Uccelli L, Leprotti S, Giganti M. Best Practices on Radiology Department Workflow: Tips from the Impact of the COVID-19 Lockdown on an Italian University Hospital. Healthcare (Basel) 2022; 10:healthcare10091771. [PMID: 36141383 PMCID: PMC9498676 DOI: 10.3390/healthcare10091771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/11/2022] [Accepted: 09/12/2022] [Indexed: 12/02/2022] Open
Abstract
Purpose: The workload of the radiology department (RD) of a university hospital in northern Italy dramatically changed during the COVID-19 outbreak. The restrictive measures of the COVID-19 pandemic lockdown influenced the use of radiological services and particularly in the emergency department (ED). Methods: Data on diagnostic services from March 2020 to May 2020 were retrospectively collected and analysed in aggregate form and compared with those of the same timeframe in the previous year. Data were sorted by patient type in the following categories: inpatients, outpatients, and ED patients; the latter divided in “traumatic” and “not traumatic” cases. Results: Compared to 2019, 6449 fewer patients (−32.6%) were assisted in the RD. This decrease was more pronounced for the emergency radiology unit (ERU) (−41%) compared to the general radiology unit (−25.7%). The proportion of investigations performed for trauma appeared to decrease significantly from 14.8% to 12.5% during the COVID-19 emergency (p < 0.001). Similarly, the proportion of assisted traumatic patients decreased from 16.6% to 12.5% (p < 0.001). The number of emergency patients assisted by the RD was significantly reduced from 45% during routine activity to 39.4% in the COVID-19 outbreak (p < 0.001). Conclusion: The COVID-19 outbreak had a tremendous impact on all radiology activities. We documented a drastic reduction in total imaging volume compared to 2019 because of both the pandemic and the lockdown. In this context, investigations performed for trauma showed a substantial decrease.
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Affiliation(s)
- Fabio Pellegrino
- Department of Translational Medicine, University of Ferrara, 44124 Ferrara, Italy
- Correspondence:
| | - Aldo Carnevale
- Department of Translational Medicine, University of Ferrara, 44124 Ferrara, Italy
| | - Riccardo Bisi
- Department of Translational Medicine, University of Ferrara, 44124 Ferrara, Italy
| | - Davide Cavedagna
- Department of Radiology, Sant’Anna University Hospital Ferrara, 44124 Ferrara, Italy
| | - Roberto Reverberi
- Blood Transfusion Service, Azienda Ospedaliera Universitaria di Ferrara, 44124 Ferrara, Italy
| | - Licia Uccelli
- Department of Translational Medicine, University of Ferrara, 44124 Ferrara, Italy
| | - Stefano Leprotti
- Department of Radiology, Sant’Anna University Hospital Ferrara, 44124 Ferrara, Italy
| | - Melchiore Giganti
- Department of Translational Medicine, University of Ferrara, 44124 Ferrara, Italy
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11
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Alghamdi F, Owen R, Ashton REM, Obotiba AD, Meertens RM, Hyde E, Faghy MA, Knapp KM, Rogers P, Strain WD. Post-acute COVID syndrome (long COVID): What should radiographers know and the potential impact for imaging services. Radiography (Lond) 2022; 28 Suppl 1:S93-S99. [PMID: 36109264 PMCID: PMC9468096 DOI: 10.1016/j.radi.2022.08.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 07/30/2022] [Accepted: 08/22/2022] [Indexed: 11/30/2022]
Abstract
Objectives The COVID-19 pandemic caused an unprecedented health crisis resulting in over 6 million deaths worldwide, a figure, which continues to grow. In addition to the excess mortality, there are individuals who recovered from the acute stages, but suffered long-term changes in their health post COVID-19, commonly referred to as long COVID. It is estimated there are currently 1.8 million long COVID sufferers by May 2022 in the UK alone. The aim of this narrative literature review is to explore the signs, symptoms and diagnosis of long COVID and the potential impact on imaging services. Key findings Long COVID is estimated to occur in 9.5% of those with two doses of vaccination and 14.6% if those with a single dose or no vaccination. Long COVID is defined by ongoing symptoms lasting for 12 or more weeks post acute infection. Symptoms are associated with reductions in the quality of daily life and may involve multisystem manifestations or present as a single symptom. Conclusion The full impact of long COVID on imaging services is yet to be realised, but there is likely to be significant increased demand for imaging, particularly in CT for the assessment of lung disease. Educators will need to include aspects related to long COVID pathophysiology and imaging presentations in curricula, underpinned by the rapidly evolving evidence base. Implications for practice Symptoms relating to long COVID are likely to become a common reason for imaging, with a particular burden on Computed Tomography services. Planning, education and updating protocols in line with a rapidly emerging evidence base is going to be essential.
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Affiliation(s)
- F Alghamdi
- College of Medicine and Health, University of Exeter, Exeter, UK.
| | - R Owen
- Human Sciences Research Centre, University of Derby, Derby, UK
| | - R E M Ashton
- Human Sciences Research Centre, University of Derby, Derby, UK
| | - A D Obotiba
- College of Medicine and Health, University of Exeter, Exeter, UK
| | - R M Meertens
- College of Medicine and Health, University of Exeter, Exeter, UK
| | - E Hyde
- College of Health, Psychology and Social Care, University of Derby, Derby, UK
| | - M A Faghy
- Human Sciences Research Centre, University of Derby, Derby, UK
| | - K M Knapp
- College of Medicine and Health, University of Exeter, Exeter, UK
| | - P Rogers
- Medical Imaging, Royal Devon and Exeter NHS Foundation Trust, UK
| | - W D Strain
- College of Medicine and Health, University of Exeter, Exeter, UK
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12
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An efficient recurrent neural network with ensemble classifier-based weighted model for disease prediction. JOURNAL OF INTELLIGENT SYSTEMS 2022. [DOI: 10.1515/jisys-2022-0068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Abstract
Day-to-day lives are affected globally by the epidemic coronavirus 2019. With an increasing number of positive cases, India has now become a highly affected country. Chronic diseases affect individuals with no time identification and impose a huge disease burden on society. In this article, an Efficient Recurrent Neural Network with Ensemble Classifier (ERNN-EC) is built using VGG-16 and Alexnet with weighted model to predict disease and its level. The dataset is partitioned randomly into small subsets by utilizing mean-based splitting method. Various models of classifier create a homogeneous ensemble by utilizing an accuracy-based weighted aging classifier ensemble, which is a weighted model’s modification. Two state of art methods such as Graph Sequence Recurrent Neural Network and Hybrid Rough-Block-Based Neural Network are used for comparison with respect to some parameters such as accuracy, precision, recall, f1-score, and relative absolute error (RAE). As a result, it is found that the proposed ERNN-EC method accomplishes accuracy of 95.2%, precision of 91%, recall of 85%, F1-score of 83.4%, and RAE of 41.6%.
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13
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Canivet P, Desir C, Thys M, Henket M, Frix AN, Ernst B, Walsh S, Occhipinti M, Vos W, Maes N, Canivet JL, Louis R, Meunier P, Guiot J. The Role of Imaging in the Detection of Non-COVID-19 Pathologies during the Massive Screening of the First Pandemic Wave. Diagnostics (Basel) 2022; 12:diagnostics12071567. [PMID: 35885473 PMCID: PMC9324631 DOI: 10.3390/diagnostics12071567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 06/25/2022] [Accepted: 06/26/2022] [Indexed: 11/24/2022] Open
Abstract
During the COVID-19 pandemic induced by the SARS-CoV-2, numerous chest scans were carried out in order to establish the diagnosis, quantify the extension of lesions but also identify the occurrence of potential pulmonary embolisms. In this perspective, the performed chest scans provided a varied database for a retrospective analysis of non-COVID-19 chest pathologies discovered de novo. The fortuitous discovery of de novo non-COVID-19 lesions was generally not detected by the automated systems for COVID-19 pneumonia developed in parallel during the pandemic and was thus identified on chest CT by the radiologist. The objective is to use the study of the occurrence of non-COVID-19-related chest abnormalities (known and unknown) in a large cohort of patients having suffered from confirmed COVID-19 infection and statistically correlate the clinical data and the occurrence of these abnormalities in order to assess the potential of increased early detection of lesions/alterations. This study was performed on a group of 362 COVID-19-positive patients who were prescribed a CT scan in order to diagnose and predict COVID-19-associated lung disease. Statistical analysis using mean, standard deviation (SD) or median and interquartile range (IQR), logistic regression models and linear regression models were used for data analysis. Results were considered significant at the 5% critical level (p < 0.05). These de novo non-COVID-19 thoracic lesions detected on chest CT showed a significant prevalence in cardiovascular pathologies, with calcifying atheromatous anomalies approaching nearly 35.4% in patients over 65 years of age. The detection of non-COVID-19 pathologies was mostly already known, except for suspicious nodule, thyroid goiter and the ascending thoracic aortic aneurysm. The presence of vertebral compression or signs of pulmonary fibrosis has shown a significant impact on inpatient length of stay. The characteristics of the patients in this sample, both from a demographic and a tomodensitometric point of view on non-COVID-19 pathologies, influenced the length of hospital stay as well as the risk of intra-hospital death. This retrospective study showed that the potential importance of the detection of these non-COVID-19 lesions by the radiologist was essential in the management and the intra-hospital course of the patients.
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Affiliation(s)
- Perrine Canivet
- Department of Radiology, University Hospital of Liège, 4000 Liège, Belgium; (C.D.); (P.M.)
- Correspondence:
| | - Colin Desir
- Department of Radiology, University Hospital of Liège, 4000 Liège, Belgium; (C.D.); (P.M.)
| | - Marie Thys
- Department of Medico-Economic Information, University Hospital of Liège, 4000 Liège, Belgium;
| | - Monique Henket
- Department of Pneumology, University Hospital of Liège, 4000 Liège, Belgium; (M.H.); (A.-N.F.); (B.E.); (R.L.); (J.G.)
| | - Anne-Noëlle Frix
- Department of Pneumology, University Hospital of Liège, 4000 Liège, Belgium; (M.H.); (A.-N.F.); (B.E.); (R.L.); (J.G.)
| | - Benoit Ernst
- Department of Pneumology, University Hospital of Liège, 4000 Liège, Belgium; (M.H.); (A.-N.F.); (B.E.); (R.L.); (J.G.)
| | - Sean Walsh
- Radiomics (Oncoradiomics SA), 4000 Liège, Belgium; (S.W.); (M.O.); (W.V.)
| | | | - Wim Vos
- Radiomics (Oncoradiomics SA), 4000 Liège, Belgium; (S.W.); (M.O.); (W.V.)
| | - Nathalie Maes
- Biostatistics and Medico-Economic Information Department, University Hospital of Liège, 4000 Liège, Belgium;
| | - Jean Luc Canivet
- Department of Intensive Unit Care, University Hospital of Liège, 4000 Liège, Belgium;
| | - Renaud Louis
- Department of Pneumology, University Hospital of Liège, 4000 Liège, Belgium; (M.H.); (A.-N.F.); (B.E.); (R.L.); (J.G.)
| | - Paul Meunier
- Department of Radiology, University Hospital of Liège, 4000 Liège, Belgium; (C.D.); (P.M.)
| | - Julien Guiot
- Department of Pneumology, University Hospital of Liège, 4000 Liège, Belgium; (M.H.); (A.-N.F.); (B.E.); (R.L.); (J.G.)
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14
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Boussel L, Bartoli JM, Adnane S, Meder JF, Malléa P, Clech J, Zins M, Bérégi JP. French Imaging Database Against Coronavirus (FIDAC): A large COVID-19 multi-center chest CT database. Diagn Interv Imaging 2022; 103:460-463. [PMID: 35715328 PMCID: PMC9194873 DOI: 10.1016/j.diii.2022.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/18/2022] [Accepted: 05/18/2022] [Indexed: 11/27/2022]
Abstract
Purpose During the first wave of the COVID-19 pandemic, the French Society of Radiology and the French College of Radiology, in partnership with NEHS Digital, have set up a system to collect chest computed tomography (CT) examinations with clinical, virological and radiological metadata, from patients clinically suspected of COVID-19 pneumonia. This allowed the constitution of an anonymized multicenter database, named FIDAC (French Imaging Database Against Coronavirus). The aim of this report was to describe the content of this public database. Materials and methods Twenty-two French radiology centers participated to the data collection. The data collected were chest CT examinations in DICOM format associated with the following metadata: patient age and sex, originating facility identifier, originating facility region, time from symptom onset to CT examination, indication for CT examination, reverse transcription-polymerase chain reaction (RT-PCR) results and normalized CT report performed by a senior radiologist. All the data were anonymized and sent through a NEHS Digital system to a centralized data center. Results A total of 5944 patients were included from the 22 centers aggregated into 8 regions with a mean number of patients of 743 ± 603.3 [SD] per region (range: 102–1577 patients). Reasons for CT examination and normalized CT reports were provided for all patients. RT-PCR results were provided in 5574 patients (93.77%) with a positive result of RT-PCR in 44.6% of patients. Conclusion The FIDAC project allowed the creation of a large database of chest CT images and metadata available, under conditions, in open access through the CERF-SFR website.
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15
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Hoang-Thi TN, Vakalopoulou M, Christodoulidis S, Paragios N, Revel MP, Chassagnon G. Deep learning for lung disease segmentation on CT: Which reconstruction kernel should be used? Diagn Interv Imaging 2021; 102:691-695. [PMID: 34686464 DOI: 10.1016/j.diii.2021.10.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 09/30/2021] [Accepted: 10/01/2021] [Indexed: 12/30/2022]
Abstract
PURPOSE The purpose of this study was to determine whether a single reconstruction kernel or both high and low frequency kernels should be used for training deep learning models for the segmentation of diffuse lung disease on chest computed tomography (CT). MATERIALS AND METHODS Two annotated datasets of COVID-19 pneumonia (323,960 slices) and interstitial lung disease (ILD) (4,284 slices) were used. Annotated CT images were used to train a U-Net architecture to segment disease. All CT slices were reconstructed using both a lung kernel (LK) and a mediastinal kernel (MK). Three different trainings, resulting in three different models were compared for each disease: training on LK only, MK only or LK+MK images. Dice similarity scores (DSC) were compared using the Wilcoxon signed-rank test. RESULTS Models only trained on LK images performed better on LK images than on MK images (median DSC = 0.62 [interquartile range (IQR): 0.54, 0.69] vs. 0.60 [IQR: 0.50, 0.70], P < 0.001 for COVID-19 and median DSC = 0.62 [IQR: 0.56, 0.69] vs. 0.50 [IQR 0.43, 0.57], P < 0.001 for ILD). Similarly, models only trained on MK images performed better on MK images (median DSC = 0.62 [IQR: 0.53, 0.68] vs. 0.54 [IQR: 0.47, 0.63], P < 0.001 for COVID-19 and 0.69 [IQR: 0.61, 0.73] vs. 0.63 [IQR: 0.53, 0.70], P < 0.001 for ILD). Models trained on both kernels performed better or similarly than those trained on only one kernel. For COVID-19, median DSC was 0.67 (IQR: =0.59, 0.73) when applied on LK images and 0.67 (IQR: 0.60, 0.74) when applied on MK images (P < 0.001 for both). For ILD, median DSC was 0.69 (IQR: 0.63, 0.73) when applied on LK images (P = 0.006) and 0.68 (IQR: 0.62, 0.72) when applied on MK images (P > 0.99). CONCLUSION Reconstruction kernels impact the performance of deep learning-based models for lung disease segmentation. Training on both LK and MK images improves the performance.
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Affiliation(s)
- Trieu-Nghi Hoang-Thi
- Université de Paris, Faculté de Médecine, 75006 Paris, France; Department of Radiology, Hôpital Cochin, AP-HP.centre, 75014 Paris, France
| | - Maria Vakalopoulou
- Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, 3 91190 Gif-sur-Yvette, France
| | - Stergios Christodoulidis
- Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, 3 91190 Gif-sur-Yvette, France
| | - Nikos Paragios
- Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, 3 91190 Gif-sur-Yvette, France; TheraPanacea, 75014 Paris, France
| | - Marie-Pierre Revel
- Université de Paris, Faculté de Médecine, 75006 Paris, France; Department of Radiology, Hôpital Cochin, AP-HP.centre, 75014 Paris, France
| | - Guillaume Chassagnon
- Université de Paris, Faculté de Médecine, 75006 Paris, France; Department of Radiology, Hôpital Cochin, AP-HP.centre, 75014 Paris, France.
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16
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COVID-19 diagnosis from chest x-rays: developing a simple, fast, and accurate neural network. Health Inf Sci Syst 2021; 9:36. [PMID: 34659742 PMCID: PMC8509906 DOI: 10.1007/s13755-021-00166-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 09/20/2021] [Indexed: 12/19/2022] Open
Abstract
Purpose Chest x-rays are a fast and inexpensive test that may potentially diagnose COVID-19, the disease caused by the novel coronavirus. However, chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonias. Recent research using deep learning may help overcome this issue as convolutional neural networks (CNNs) have demonstrated high accuracy of COVID-19 diagnosis at an early stage. Methods We used the COVID-19 Radiography database [36], which contains x-ray images of COVID-19, other viral pneumonia, and normal lungs. We developed a CNN in which we added a dense layer on top of a pre-trained baseline CNN (EfficientNetB0), and we trained, validated, and tested the model on 15,153 X-ray images. We used data augmentation to avoid overfitting and address class imbalance; we used fine-tuning to improve the model’s performance. From the external test dataset, we calculated the model’s accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. Results Our model differentiated COVID-19 from normal lungs with 95% accuracy, 90% sensitivity, and 97% specificity; it differentiated COVID-19 from other viral pneumonia and normal lungs with 93% accuracy, 94% sensitivity, and 95% specificity. Conclusions Our parsimonious CNN shows that it is possible to differentiate COVID-19 from other viral pneumonia and normal lungs on x-ray images with high accuracy. Our method may assist clinicians with making more accurate diagnostic decisions and support chest X-rays as a valuable screening tool for the early, rapid diagnosis of COVID-19. Supplementary Information The online version contains supplementary material available at 10.1007/s13755-021-00166-4.
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Wang T, Chen Z, Shang Q, Ma C, Chen X, Xiao E. A Promising and Challenging Approach: Radiologists' Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19. Diagnostics (Basel) 2021; 11:diagnostics11101924. [PMID: 34679622 PMCID: PMC8534829 DOI: 10.3390/diagnostics11101924] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/10/2021] [Accepted: 10/14/2021] [Indexed: 12/23/2022] Open
Abstract
Chest X-rays (CXR) and computed tomography (CT) are the main medical imaging modalities used against the increased worldwide spread of the 2019 coronavirus disease (COVID-19) epidemic. Machine learning (ML) and artificial intelligence (AI) technology, based on medical imaging fully extracting and utilizing the hidden information in massive medical imaging data, have been used in COVID-19 research of disease diagnosis and classification, treatment decision-making, efficacy evaluation, and prognosis prediction. This review article describes the extensive research of medical image-based ML and AI methods in preventing and controlling COVID-19, and summarizes their characteristics, differences, and significance in terms of application direction, image collection, and algorithm improvement, from the perspective of radiologists. The limitations and challenges faced by these systems and technologies, such as generalization and robustness, are discussed to indicate future research directions.
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Affiliation(s)
- Tianming Wang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Zhu Chen
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Quanliang Shang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Cong Ma
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Xiangyu Chen
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Enhua Xiao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
- Molecular Imaging Research Center, Central South University, Changsha 410008, China
- Correspondence:
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18
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Greffier J, Hoballah A, Sadate A, de Oliveira F, Claret PG, de Forges H, Loubet P, Mauboussin JM, Hamard A, Beregi JP, Frandon J. Ultra-low-dose chest CT performance for the detection of viral pneumonia patterns during the COVID-19 outbreak period: a monocentric experience. Quant Imaging Med Surg 2021; 11:3190-3199. [PMID: 34249645 DOI: 10.21037/qims-20-1176] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 02/18/2021] [Indexed: 12/16/2022]
Abstract
Background Ultra low dose chest computed tomography (CT) acquisitions have been used for selected emergency room patients with acute dyspnea or minor thoracic trauma. The purpose of this study was to evaluate the diagnostic performance of ultra-low-dose (ULD) chest CT for detecting viral pneumonia patterns compared to standard (STD) dose chest CT. Methods All consecutive adult patients with two non-enhanced chest CT acquisitions, one STD and one ULD, for suspicion of viral pneumonia between March 5th and April 2nd 2020 were included. CT results were divided into two groups: non-viral pneumonia CT or compatible with viral pneumonia CT based on viral pneumonia CT patterns: ground-glass opacity (GGO), consolidation, crazy paving, air bronchogram signs and fibrous stripes. The diagnostic performance of ULD CT for suspicion of viral pneumonia was evaluated. For CTs compatible with viral pneumonia, CT pattern detection on ULD CT was assessed and STD CT was used as a reference. Results The study included 380 patients with 97 CTs (25.5%) compatible with viral pneumonia. The mean effective doses (EDs) were 1.66 (1.29; 2.18) mSv for STD and 0.20 (0.18; 0.22) mSv for ULD CT (P<0.001). The sensitivity and specificity of ULD CT for viral pneumonia detection were 98.9% and 99.0%, respectively. GGO, consolidation and fibrous stripes were equally visible in STD and ULD in 100% (n=97), 36% (n=35) and 23% (n=22) of compatible viral pneumonia-CT patients, respectively. Air bronchogram sign detection was equivalent, concerning 23% (n=22) of patients in STD and 22% (n=21) in ULD. Crazy paving was visible in 24% (n=23) of patients in STD and only 8% (n=8) in ULD (P=0.003). Conclusions In comparison to STD dose chest CT, ULD chest CT, with a mean reduction dose of 88.0%, has comparable diagnostic performance for detecting viral pneumonia on CT.
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Affiliation(s)
- Joël Greffier
- Department of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, Nîmes, France
| | - Adel Hoballah
- Department of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, Nîmes, France
| | - Alexandre Sadate
- Department of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, Nîmes, France
| | - Fabien de Oliveira
- Department of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, Nîmes, France
| | - Pierre-Geraud Claret
- Department of Anesthesia Resuscitation Pain Emergencies, Nîmes University Hospital, University of Montpellier, Nîmes, France
| | - Hélène de Forges
- Department of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, Nîmes, France
| | - Paul Loubet
- VBMI, INSERM U1047, Department of Infectious and Tropical Disease, CHU Nîmes, Univ Montpellier, Nîmes, France
| | - Jean-Marc Mauboussin
- VBMI, INSERM U1047, Department of Infectious and Tropical Disease, CHU Nîmes, Univ Montpellier, Nîmes, France
| | - Aymeric Hamard
- Department of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, Nîmes, France
| | - Jean-Paul Beregi
- Department of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, Nîmes, France
| | - Julien Frandon
- Department of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, Nîmes, France
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Sideris GA, Nikolakea M, Karanikola AE, Konstantinopoulou S, Giannis D, Modahl L. Imaging in the COVID-19 era: Lessons learned during a pandemic. World J Radiol 2021. [DOI: 10.4329/wjr.v13.i6.193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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20
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Sideris GA, Nikolakea M, Karanikola AE, Konstantinopoulou S, Giannis D, Modahl L. Imaging in the COVID-19 era: Lessons learned during a pandemic. World J Radiol 2021; 13:192-222. [PMID: 34249239 PMCID: PMC8245753 DOI: 10.4329/wjr.v13.i6.192] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 04/02/2021] [Accepted: 06/15/2021] [Indexed: 02/07/2023] Open
Abstract
The first year of the coronavirus disease 2019 (COVID-19) pandemic has been a year of unprecedented changes, scientific breakthroughs, and controversies. The radiology community has not been spared from the challenges imposed on global healthcare systems. Radiology has played a crucial part in tackling this pandemic, either by demonstrating the manifestations of the virus and guiding patient management, or by safely handling the patients and mitigating transmission within the hospital. Major modifications involving all aspects of daily radiology practice have occurred as a result of the pandemic, including workflow alterations, volume reductions, and strict infection control strategies. Despite the ongoing challenges, considerable knowledge has been gained that will guide future innovations. The aim of this review is to provide the latest evidence on the role of imaging in the diagnosis of the multifaceted manifestations of COVID-19, and to discuss the implications of the pandemic on radiology departments globally, including infection control strategies and delays in cancer screening. Lastly, the promising contribution of artificial intelligence in the COVID-19 pandemic is explored.
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Affiliation(s)
- Georgios Antonios Sideris
- Department of Radiology, University of Massachusetts Medical School, Baystate Medical Center, Springfield, MA 01199, United States
- Radiology Working Group, Society of Junior Doctors, Athens 11527, Greece
| | - Melina Nikolakea
- Radiology Working Group, Society of Junior Doctors, Athens 11527, Greece
| | | | - Sofia Konstantinopoulou
- Division of Pulmonary Medicine, Department of Pediatrics, Sheikh Khalifa Medical City, Abu Dhabi W13-01, United Arab Emirates
| | - Dimitrios Giannis
- Institute of Health Innovations and Outcomes Research, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030, United States
| | - Lucy Modahl
- Department of Radiology, University of Massachusetts Medical School, Baystate Medical Center, Springfield, MA 01199, United States
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Kato S, Ishiwata Y, Aoki R, Iwasawa T, Hagiwara E, Ogura T, Utsunomiya D. Imaging of COVID-19: An update of current evidences. Diagn Interv Imaging 2021; 102:493-500. [PMID: 34088635 PMCID: PMC8148573 DOI: 10.1016/j.diii.2021.05.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 05/16/2021] [Accepted: 05/17/2021] [Indexed: 12/15/2022]
Abstract
Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been reported as a global emergency. As respiratory dysfunction is a major clinical presentation of COVID-19, chest computed tomography (CT) plays a central role in the diagnosis and management of patients with COVID-19. Recent advances in imaging approaches using artificial intelligence have been essential as a quantification and diagnostic tool to differentiate COVID-19 from other respiratory infectious diseases. Furthermore, cardiovascular involvement in patients with COVID-19 is not negligible and may result in rapid worsening of the disease and sudden death. Cardiac magnetic resonance imaging can accurately depict myocardial involvement in SARS-CoV-2 infection. This review summarizes the role of the radiology department in the management and the diagnosis of COVID-19, with a special emphasis on ultra-high-resolution CT findings, cardiovascular complications and the potential of artificial intelligence.
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Affiliation(s)
- Shingo Kato
- Department of Diagnostic Radiology, Yokohama City University Graduate School of Medicine, 236-0004 Yokohama, Kanagawa, Japan.
| | - Yoshinobu Ishiwata
- Department of Diagnostic Radiology, Yokohama City University Graduate School of Medicine, 236-0004 Yokohama, Kanagawa, Japan
| | - Ryo Aoki
- Department of Diagnostic Radiology, Yokohama City University Medical Center, 232-0024 Yokohama, Kanagawa, Japan
| | - Tae Iwasawa
- Department of Diagnostic Radiology, Kanagawa Cardiovascular and Respiratory Center, 236-0051 Yokohama, Kanagawa, Japan
| | - Eri Hagiwara
- Department of Respiratory Medicine, Kanagawa Cardiovascular and Respiratory Center, 236-0051 Yokohama, Kanagawa, Japan
| | - Takashi Ogura
- Department of Respiratory Medicine, Kanagawa Cardiovascular and Respiratory Center, 236-0051 Yokohama, Kanagawa, Japan
| | - Daisuke Utsunomiya
- Department of Diagnostic Radiology, Yokohama City University Graduate School of Medicine, 236-0004 Yokohama, Kanagawa, Japan
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22
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Ghaderzadeh M, Asadi F. Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:6677314. [PMID: 33747419 PMCID: PMC7958142 DOI: 10.1155/2021/6677314] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 01/08/2021] [Accepted: 02/11/2021] [Indexed: 12/17/2022]
Abstract
Introduction The early detection and diagnosis of COVID-19 and the accurate separation of non-COVID-19 cases at the lowest cost and in the early stages of the disease are among the main challenges in the current COVID-19 pandemic. Concerning the novelty of the disease, diagnostic methods based on radiological images suffer from shortcomings despite their many applications in diagnostic centers. Accordingly, medical and computer researchers tend to use machine-learning models to analyze radiology images. Material and Methods. The present systematic review was conducted by searching the three databases of PubMed, Scopus, and Web of Science from November 1, 2019, to July 20, 2020, based on a search strategy. A total of 168 articles were extracted and, by applying the inclusion and exclusion criteria, 37 articles were selected as the research population. Result This review study provides an overview of the current state of all models for the detection and diagnosis of COVID-19 through radiology modalities and their processing based on deep learning. According to the findings, deep learning-based models have an extraordinary capacity to offer an accurate and efficient system for the detection and diagnosis of COVID-19, the use of which in the processing of modalities would lead to a significant increase in sensitivity and specificity values. Conclusion The application of deep learning in the field of COVID-19 radiologic image processing reduces false-positive and negative errors in the detection and diagnosis of this disease and offers a unique opportunity to provide fast, cheap, and safe diagnostic services to patients.
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Affiliation(s)
- Mustafa Ghaderzadeh
- Student Research Committee, Department and Faculty of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farkhondeh Asadi
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Affiliation(s)
- Marie-Pierre Revel
- Department of Radiology, Hôpital Cochin, AP-HP, 75014 Paris, France; Université de Paris, Descartes-Paris 5, 75006 Paris, France.
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