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Bettinger H, Lenczner G, Guigui J, Rotenberg L, Zerbib E, Attia A, Vidal J, Beaumel P. Evaluation of the Performance of an Artificial Intelligence (AI) Algorithm in Detecting Thoracic Pathologies on Chest Radiographs. Diagnostics (Basel) 2024; 14:1183. [PMID: 38893709 PMCID: PMC11171491 DOI: 10.3390/diagnostics14111183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 05/21/2024] [Accepted: 05/30/2024] [Indexed: 06/21/2024] Open
Abstract
The purpose of the study was to assess the performance of readers in diagnosing thoracic anomalies on standard chest radiographs (CXRs) with and without a deep-learning-based AI tool (Rayvolve) and to evaluate the standalone performance of Rayvolve in detecting thoracic pathologies on CXRs. This retrospective multicentric study was conducted in two phases. In phase 1, nine readers independently reviewed 900 CXRs from imaging group A and identified thoracic abnormalities with and without AI assistance. A consensus from three radiologists served as the ground truth. In phase 2, the standalone performance of Rayvolve was evaluated on 1500 CXRs from imaging group B. The average values of AUC across the readers significantly increased by 15.94%, with AI-assisted reading compared to unaided reading (0.88 ± 0.01 vs. 0.759 ± 0.07, p < 0.001). The time taken to read the CXRs decreased significantly, by 35.81% with AI assistance. The average values of sensitivity and specificity across the readers increased significantly by 11.44% and 2.95% with AI-assisted reading compared to unaided reading (0.857 ± 0.02 vs. 0.769 ± 0.02 and 0.974 ± 0.01 vs. 0.946 ± 0.01, p < 0.001). From the standalone perspective, the AI model achieved an average sensitivity, specificity, PPV, and NPV of 0.964, 0.844, 0.757, and 0.9798. The speed and performance of the readers improved significantly with AI assistance.
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Affiliation(s)
| | | | - Jean Guigui
- Radiologie Paris Ouest, 47 Rue du Rocher, 75008 Paris, France
| | - Luc Rotenberg
- Radiologie Paris Ouest, 47 Rue du Rocher, 75008 Paris, France
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Kosar A, Asif M, Ahmad MB, Akram W, Mahmood K, Kumari S. Towards classification and comprehensive analysis of AI-based COVID-19 diagnostic techniques: A survey. Artif Intell Med 2024; 151:102858. [PMID: 38583369 DOI: 10.1016/j.artmed.2024.102858] [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: 04/22/2023] [Revised: 01/02/2024] [Accepted: 03/25/2024] [Indexed: 04/09/2024]
Abstract
The unpredictable pandemic came to light at the end of December 2019, known as the novel coronavirus, also termed COVID-19, identified by the World Health Organization (WHO). The virus first originated in Wuhan (China) and rapidly affected most of the world's population. This outbreak's impact is experienced worldwide because it causes high mortality risk, many cases, and economic falls. Around the globe, the total number of cases and deaths reported till November 12, 2022, were >600 million and 6.6 million, respectively. During the period of COVID-19, several diverse diagnostic techniques have been proposed. This work presents a systematic review of COVID-19 diagnostic techniques in response to such acts. Initially, these techniques are classified into different categories based on their working principle and detection modalities, i.e. chest X-ray imaging, cough sound or respiratory patterns, RT-PCR, antigen testing, and antibody testing. After that, a comparative analysis is performed to evaluate these techniques' efficacy which may help to determine an optimum solution for a particular scenario. The findings of the proposed work show that Artificial Intelligence plays a vital role in developing COVID-19 diagnostic techniques which support the healthcare system. The related work can be a footprint for all the researchers, available under a single umbrella. Additionally, all the techniques are long-lasting and can be used for future pandemics.
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Affiliation(s)
- Amna Kosar
- Department of Computer Science, Lahore Garrison University, Lahore, Pakistan
| | - Muhammad Asif
- Department of Computer Science, Lahore Garrison University, Lahore, Pakistan
| | - Maaz Bin Ahmad
- College of Computing and Information Sciences, Karachi Institute of Economics and Technology (KIET), Karachi, Pakistan
| | - Waseem Akram
- Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Douliu, Taiwan, ROC
| | - Khalid Mahmood
- Graduate School of Intelligent Data Science, National Yunlin University of Science and Technology, Douliu, Taiwan, ROC.
| | - Saru Kumari
- Departement of Mathematics, Chaudhary Charan Singh University, Meerut, India
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Ippolito D, Maino C, Gandola D, Franco PN, Miron R, Barbu V, Bologna M, Corso R, Breaban ME. Artificial Intelligence Applied to Chest X-ray: A Reliable Tool to Assess the Differential Diagnosis of Lung Pneumonia in the Emergency Department. Diseases 2023; 11:171. [PMID: 37987282 PMCID: PMC10660530 DOI: 10.3390/diseases11040171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/09/2023] [Accepted: 11/13/2023] [Indexed: 11/22/2023] Open
Abstract
BACKGROUND Considering the large number of patients with pulmonary symptoms admitted to the emergency department daily, it is essential to diagnose them correctly. It is necessary to quickly solve the differential diagnosis between COVID-19 and typical bacterial pneumonia to address them with the best management possible. In this setting, an artificial intelligence (AI) system can help radiologists detect pneumonia more quickly. METHODS We aimed to test the diagnostic performance of an AI system in detecting COVID-19 pneumonia and typical bacterial pneumonia in patients who underwent a chest X-ray (CXR) and were admitted to the emergency department. The final dataset was composed of three sub-datasets: the first included all patients positive for COVID-19 pneumonia (n = 1140, namely "COVID-19+"), the second one included all patients with typical bacterial pneumonia (n = 500, "pneumonia+"), and the third one was composed of healthy subjects (n = 1000). Two radiologists were blinded to demographic, clinical, and laboratory data. The developed AI system was used to evaluate all CXRs randomly and was asked to classify them into three classes. Cohen's κ was used for interrater reliability analysis. The AI system's diagnostic accuracy was evaluated using a confusion matrix, and 95%CIs were reported as appropriate. RESULTS The interrater reliability analysis between the most experienced radiologist and the AI system reported an almost perfect agreement for COVID-19+ (κ = 0.822) and pneumonia+ (κ = 0.913). We found 96% sensitivity (95% CIs = 94.9-96.9) and 79.8% specificity (76.4-82.9) for the radiologist and 94.7% sensitivity (93.4-95.8) and 80.2% specificity (76.9-83.2) for the AI system in the detection of COVID-19+. Moreover, we found 97.9% sensitivity (98-99.3) and 88% specificity (83.5-91.7) for the radiologist and 97.5% sensitivity (96.5-98.3) and 83.9% specificity (79-87.9) for the AI system in the detection of pneumonia+ patients. Finally, the AI system reached an accuracy of 93.8%, with a misclassification rate of 6.2% and weighted-F1 of 93.8% in detecting COVID+, pneumonia+, and healthy subjects. CONCLUSIONS The AI system demonstrated excellent diagnostic performance in identifying COVID-19 and typical bacterial pneumonia in CXRs acquired in the emergency setting.
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Affiliation(s)
- Davide Ippolito
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy; (D.I.); (D.G.); (P.N.F.); (R.C.)
- School of Medicine, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, Italy
| | - Cesare Maino
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy; (D.I.); (D.G.); (P.N.F.); (R.C.)
| | - Davide Gandola
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy; (D.I.); (D.G.); (P.N.F.); (R.C.)
| | - Paolo Niccolò Franco
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy; (D.I.); (D.G.); (P.N.F.); (R.C.)
| | - Radu Miron
- Sentic Lab, Strada Elena Doamna 20, 700398 Iași, Romania; (R.M.); (V.B.)
| | - Vlad Barbu
- Sentic Lab, Strada Elena Doamna 20, 700398 Iași, Romania; (R.M.); (V.B.)
| | | | - Rocco Corso
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy; (D.I.); (D.G.); (P.N.F.); (R.C.)
| | - Mihaela Elena Breaban
- Faculty of Computer Science, “Alexandru Ioan Cuza” University of Iasi, Strada General Henri Mathias Berthelot 16, 700483 Iași, Romania
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Wu Y, Dravid A, Wehbe RM, Katsaggelos AK. DeepCOVID-Fuse: A Multi-Modality Deep Learning Model Fusing Chest X-rays and Clinical Variables to Predict COVID-19 Risk Levels. Bioengineering (Basel) 2023; 10:bioengineering10050556. [PMID: 37237626 DOI: 10.3390/bioengineering10050556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 05/28/2023] Open
Abstract
The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, highlighting the need for accurate and timely risk prediction models that can prioritize patient care and allocate resources effectively. This study presents DeepCOVID-Fuse, a deep learning fusion model that predicts risk levels in patients with confirmed COVID-19 by combining chest radiographs (CXRs) and clinical variables. The study collected initial CXRs, clinical variables, and outcomes (i.e., mortality, intubation, hospital length of stay, Intensive care units (ICU) admission) from February to April 2020, with risk levels determined by the outcomes. The fusion model was trained on 1657 patients (Age: 58.30 ± 17.74; Female: 807) and validated on 428 patients (56.41 ± 17.03; 190) from the local healthcare system and tested on 439 patients (56.51 ± 17.78; 205) from a different holdout hospital. The performance of well-trained fusion models on full or partial modalities was compared using DeLong and McNemar tests. Results show that DeepCOVID-Fuse significantly (p < 0.05) outperformed models trained only on CXRs or clinical variables, with an accuracy of 0.658 and an area under the receiver operating characteristic curve (AUC) of 0.842. The fusion model achieves good outcome predictions even when only one of the modalities is used in testing, demonstrating its ability to learn better feature representations across different modalities during training.
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Affiliation(s)
- Yunan Wu
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL 60201, USA
| | - Amil Dravid
- Department of Computer Science, Northwestern University, Evanston, IL 60201, USA
| | - Ramsey Michael Wehbe
- The Division of Cardiology, Department of Medicine and Bluhm Cardiovascular Institute, Northwestern Memorial Hospital, Chicago, IL 60611, USA
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL 60201, USA
- Department of Computer Science, Northwestern University, Evanston, IL 60201, USA
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Suba S, Muthulakshmi M. A systematic review: Chest radiography images (X-ray images) analysis and COVID-19 categorization diagnosis using artificial intelligence techniques. NETWORK (BRISTOL, ENGLAND) 2023; 34:26-64. [PMID: 36420865 DOI: 10.1080/0954898x.2022.2147231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 10/27/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
COVID-19 pandemic created a turmoil across nations due to Severe Acute Respiratory Syndrome Corona virus-1(SARS - Co-V-2). The severity of COVID-19 symptoms is starting from cold, breathing problems, issues in respiratory system which may also lead to life threatening situations. This disease is widely contaminating and transmitted from man-to-man. The contamination is spreading when the human organs like eyes, nose, and mouth get in contact with contaminated fluids. This virus can be screened through performing a nasopharyngeal swab test which is time consuming. So the physicians are preferring the fast detection methods like chest radiography images and CT scans. At times some confusion in finding out the accurate disorder from chest radiography images can happen. To overcome this issue this study reviews several deep learning and machine learning procedures to be implemented in X-ray images of chest. This also helps the professionals to find out the other types of malfunctions happening in the chest other than COVID-19 also. This review can act as a guidance to the doctors and radiologists in identifying the COVID-19 and other types of viruses causing illness in the human anatomy and can provide aid soon.
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Affiliation(s)
- Saravanan Suba
- Department of Computer Science, Kamarajar Government Arts College, Tirunelveli, Surandai 627859, India
| | - M Muthulakshmi
- Department of Computer Science, Kamarajar Government Arts College, Tirunelveli, Surandai 627859, India
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Comparison of the Diagnostic Performance of Deep Learning Algorithms for Reducing the Time Required for COVID-19 RT-PCR Testing. Viruses 2023; 15:v15020304. [PMID: 36851519 PMCID: PMC9966023 DOI: 10.3390/v15020304] [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: 10/21/2022] [Revised: 01/13/2023] [Accepted: 01/19/2023] [Indexed: 01/24/2023] Open
Abstract
(1) Background: Rapid and accurate negative discrimination enables efficient management of scarce isolated bed resources and adequate patient accommodation in the majority of areas experiencing an explosion of confirmed cases due to Omicron mutations. Until now, methods for artificial intelligence or deep learning to replace time-consuming RT-PCR have relied on CXR, chest CT, blood test results, or clinical information. (2) Methods: We proposed and compared five different types of deep learning algorithms (RNN, LSTM, Bi-LSTM, GRU, and transformer) for reducing the time required for RT-PCR diagnosis by learning the change in fluorescence value derived over time during the RT-PCR process. (3) Results: Among the five deep learning algorithms capable of training time series data, Bi-LSTM and GRU were shown to be able to decrease the time required for RT-PCR diagnosis by half or by 25% without significantly impairing the diagnostic performance of the COVID-19 RT-PCR test. (4) Conclusions: The diagnostic performance of the model developed in this study when 40 cycles of RT-PCR are used for diagnosis shows the possibility of nearly halving the time required for RT-PCR diagnosis.
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Ibragimov B, Arzamasov K, Maksudov B, Kiselev S, Mongolin A, Mustafaev T, Ibragimova D, Evteeva K, Andreychenko A, Morozov S. A 178-clinical-center experiment of integrating AI solutions for lung pathology diagnosis. Sci Rep 2023; 13:1135. [PMID: 36670118 PMCID: PMC9859802 DOI: 10.1038/s41598-023-27397-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 01/02/2023] [Indexed: 01/22/2023] Open
Abstract
In 2020, an experiment testing AI solutions for lung X-ray analysis on a multi-hospital network was conducted. The multi-hospital network linked 178 Moscow state healthcare centers, where all chest X-rays from the network were redirected to a research facility, analyzed with AI, and returned to the centers. The experiment was formulated as a public competition with monetary awards for participating industrial and research teams. The task was to perform the binary detection of abnormalities from chest X-rays. For the objective real-life evaluation, no training X-rays were provided to the participants. This paper presents one of the top-performing AI frameworks from this experiment. First, the framework used two EfficientNets, histograms of gradients, Haar feature ensembles, and local binary patterns to recognize whether an input image represents an acceptable lung X-ray sample, meaning the X-ray is not grayscale inverted, is a frontal chest X-ray, and completely captures both lung fields. Second, the framework extracted the region with lung fields and then passed them to a multi-head DenseNet, where the heads recognized the patient's gender, age and the potential presence of abnormalities, and generated the heatmap with the abnormality regions highlighted. During one month of the experiment from 11.23.2020 to 12.25.2020, 17,888 cases have been analyzed by the framework with 11,902 cases having radiological reports with the reference diagnoses that were unequivocally parsed by the experiment organizers. The performance measured in terms of the area under receiving operator curve (AUC) was 0.77. The AUC for individual diseases ranged from 0.55 for herniation to 0.90 for pneumothorax.
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Affiliation(s)
- Bulat Ibragimov
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
| | - Kirill Arzamasov
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Bulat Maksudov
- School of Electronic Engineering, Dublin City University, Dublin, Ireland
| | | | - Alexander Mongolin
- Innopolis University, Innopolis, Russia
- Nova Information Management School, Universidade Nova de Lisboa, Lisbon, Portugal
| | - Tamerlan Mustafaev
- Innopolis University, Innopolis, Russia
- University Clinic Kazan State University, Kazan, Russia
| | | | - Ksenia Evteeva
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Anna Andreychenko
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Sergey Morozov
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
- Osimis SA, Liege, Belgium
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New patch-based strategy for COVID-19 automatic identification using chest x-ray images. HEALTH AND TECHNOLOGY 2022; 12:1117-1132. [PMCID: PMC9647770 DOI: 10.1007/s12553-022-00704-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 10/09/2022] [Indexed: 11/11/2022]
Abstract
Purpose The development of a robust model for automatic identification of COVID-19 based on chest x-rays has been a widely addressed topic over the last couple of years; however, the scarcity of good quality images sets, and their limited size, have proven to be an important obstacle to obtain reliable models. In fact, models proposed so far have suffered from over-fitting erroneous features instead of learning lung features, a phenomenon known as shortcut learning. In this research, a new image classification methodology is proposed that attempts to mitigate this problem. Methods To this end, annotation by expert radiologists of a set of images was performed. The lung region was then segmented and a new classification strategy based on a patch partitioning that improves the resolution of the convolution neural network is proposed. In addition, a set of native images, used as an external evaluation set, is released. Results The best results were obtained for the 6-patch splitting variant with 0.887 accuracy, 0.85 recall and 0.848 F1score on the external validation set. Conclusion The results show that the proposed new strategy maintains similar values between internal and external validation, which gives our model generalization power, making it available for use in hospital settings. Supplementary Information The online version contains supplementary material available at 10.1007/s12553-022-00704-4.
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Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future. Diagnostics (Basel) 2022; 12:diagnostics12112644. [PMID: 36359485 PMCID: PMC9689810 DOI: 10.3390/diagnostics12112644] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 10/26/2022] [Accepted: 10/29/2022] [Indexed: 11/30/2022] Open
Abstract
Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase of lung cancer management, from detection to assessment of response to treatment. The development of imaging-based artificial intelligence (AI) models has the potential to play a key role in early detection and customized treatment planning. Computer-aided detection of lung nodules in screening programs has revolutionized the early detection of the disease. Moreover, the possibility to use AI approaches to identify patients at risk of developing lung cancer during their life can help a more targeted screening program. The combination of imaging features and clinical and laboratory data through AI models is giving promising results in the prediction of patients’ outcomes, response to specific therapies, and risk for toxic reaction development. In this review, we provide an overview of the main imaging AI-based tools in lung cancer imaging, including automated lesion detection, characterization, segmentation, prediction of outcome, and treatment response to provide radiologists and clinicians with the foundation for these applications in a clinical scenario.
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Saheb SK, Narayanan B, Rao TVN. ADL-CDF: A Deep Learning Framework for COVID-19 Detection from CT Scans Towards an Automated Clinical Decision Support System. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022; 48:1-13. [PMID: 36212631 PMCID: PMC9531859 DOI: 10.1007/s13369-022-07271-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 09/06/2022] [Indexed: 11/29/2022]
Abstract
The emergence of deep learning has paved to solve many problems in the real world. COVID-19 pandemic, since the late 2019, has been affecting lives of people across the globe. Chest CT scan images are used to detect it and know its severity in patients. The problem with many existing solutions in COVID-19 detection using CT scan images is that inability to detect the infection when it is in initial stages. As the infection can exist on varied scales, there is need for more comprehensive approach that can ascertain the disease at all scales. Towards this end, we proposed a deep learning-based framework known as Automated Deep Learning-based COVID-19 Detection Framework (ADL-CDF). It does not need a human medical expert in diagnosis as it is capable of detecting automatically. The framework is assisted by two algorithms that involve image processing and deep learning. The first algorithm known as Region of Interest (ROI)-based Image Filtering (ROI-IF) which analyses given input CT scan images of a patient and discards the ones where ROI is missing. This algorithm minimizes time taken for processing besides reducing false positive rate. The second algorithm is known as Multi-Scale Feature Selection algorithm that fits into the deep learning framework's pipeline to leverage detection performance of the ADL-CDF. The proposed framework is evaluated against ResNet50V2 and Xception. Our empirical study revealed that our model outperforms the state of the art.
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Affiliation(s)
- Shaik Khasim Saheb
- Department of CSE, Annamalai University, Annamalainagar, Chidambaram, Tamil Nadu India
- Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana India
| | - B. Narayanan
- Department of CSE, Annamalai University, Annamalainagar, Chidambaram, Tamil Nadu India
| | - Thota Venkat Narayana Rao
- Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana India
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Albiol A, Albiol F, Paredes R, Plasencia-Martínez JM, Blanco Barrio A, Santos JMG, Tortajada S, González Montaño VM, Rodríguez Godoy CE, Fernández Gómez S, Oliver-Garcia E, de la Iglesia Vayá M, Márquez Pérez FL, Rayo Madrid JI. A comparison of Covid-19 early detection between convolutional neural networks and radiologists. Insights Imaging 2022; 13:122. [PMID: 35900673 PMCID: PMC9330942 DOI: 10.1186/s13244-022-01250-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 06/09/2022] [Indexed: 01/01/2023] Open
Abstract
Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience.
Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx.
Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01250-3.
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Affiliation(s)
- Alberto Albiol
- ETSI Telecomunicación, iTeam Institute, Universitat Politècnica València, Camino de Vera S/N, 46022, València, Spain.
| | - Francisco Albiol
- Instituto Física Corpuscular, National Research Council (CSIC)-Universitat València, València, Spain.,Instituto de Física Corpuscular IFIC (CSIC-UVEG), Madrid, Spain
| | - Roberto Paredes
- PRLHT Research Center, Universitat Politècnica de València, València, Spain
| | | | | | | | | | | | | | | | - Elena Oliver-Garcia
- Biomedical Imaging Mixed Unit, FISABIO-CIPF, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunidad Valenciana, València, Spain
| | - María de la Iglesia Vayá
- Biomedical Imaging Mixed Unit, FISABIO-CIPF, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunidad Valenciana, València, Spain.,Regional Ministry of Universal Health a Public Health in València, València, Spain
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COVIDetection: deep convolutional neural networks-based automatic detection of COVID-19 with chest x-ray images. RESEARCH ON BIOMEDICAL ENGINEERING 2022. [PMCID: PMC9244037 DOI: 10.1007/s42600-022-00230-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose Until recently, COVID-19 was considered a highly contagious air borne infection that leads to fatal pneumonia and other health hazardous infections. The new coronavirus, or type SARS-COV-2, is responsible for COVID-19 and has demonstrated the deadly nature of the respiratory disease that threatens many people worldwide. A clinical study found that a person infected with COVID-19 can experience dry cough, muscle pain, headache, fever, sore throat, and mild to moderate respiratory illness. At the same time, it has a negative effect on the lungs if there is a viral infection. Thus, the lungs can become visible internal organs for diagnosing the severity of COVID-19 infection using chest X-rays and CT scans. Despite the long testing time, RT-PCR is a proven testing method for detecting coronavirus infection. Sometimes there are more false positives and false negatives than the desired percentage. The concept of artificial neural network (ANN) is inspired by the biological neural networks which consists of inter-connected units called artificial neurons. Convolutional neural network (CNN) which is a variant of multilayer perceptron that belongs to a class of feedforward ANN is widely used for various applications due to its enhanced accuracy. Method Traditional RT-PCR methodology supports for accurate clinical diagnosis, screening for COVID-19 using an X-ray or CT scan of the human lung that can be considered. In this work, a new multi-image augmentation system is proposed based on CNN to detect COVID-19 in the chest using chest X-rays or CT images of people suspected of having the coronavirus. Results The optimal selection of slices/features has led to obtain best results for accuracy and loss. In addition to that the parameter selection reflected optimal true positive rate and false positive rates. The results look promising even with the small publicly available data set in a short period of time. Conclusion This work presented a model that found to detect positive cases of COVID-19 from chest X-rays using an in-depth training model. The system demonstrates a significant performance improvement over the publicly maintained COVID-19 positive X-ray classification kit, the same dataset of pneumonia chest X-rays. The results look promising even with the small publicly available data set in terms of accuracy and loss as well as with enhanced true positive results.
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Becker J, Decker JA, Römmele C, Kahn M, Messmann H, Wehler M, Schwarz F, Kroencke T, Scheurig-Muenkler C. Artificial Intelligence-Based Detection of Pneumonia in Chest Radiographs. Diagnostics (Basel) 2022; 12:diagnostics12061465. [PMID: 35741276 PMCID: PMC9221818 DOI: 10.3390/diagnostics12061465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 06/10/2022] [Accepted: 06/12/2022] [Indexed: 11/24/2022] Open
Abstract
Artificial intelligence is gaining increasing relevance in the field of radiology. This study retrospectively evaluates how a commercially available deep learning algorithm can detect pneumonia in chest radiographs (CR) in emergency departments. The chest radiographs of 948 patients with dyspnea between 3 February and 8 May 2020, as well as 15 October and 15 December 2020, were used. A deep learning algorithm was used to identify opacifications associated with pneumonia, and the performance was evaluated by using ROC analysis, sensitivity, specificity, PPV and NPV. Two radiologists assessed all enrolled images for pulmonal infection patterns as the reference standard. If consolidations or opacifications were present, the radiologists classified the pulmonal findings regarding a possible COVID-19 infection because of the ongoing pandemic. The AUROC value of the deep learning algorithm reached 0.923 when detecting pneumonia in chest radiographs with a sensitivity of 95.4%, specificity of 66.0%, PPV of 80.2% and NPV of 90.8%. The detection of COVID-19 pneumonia in CR by radiologists was achieved with a sensitivity of 50.6% and a specificity of 73%. The deep learning algorithm proved to be an excellent tool for detecting pneumonia in chest radiographs. Thus, the assessment of suspicious chest radiographs can be purposefully supported, shortening the turnaround time for reporting relevant findings and aiding early triage.
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Affiliation(s)
- Judith Becker
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (J.B.); (J.A.D.); (F.S.); (C.S.-M.)
| | - Josua A. Decker
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (J.B.); (J.A.D.); (F.S.); (C.S.-M.)
| | - Christoph Römmele
- Department of Gastroenterology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (C.R.); (M.K.); (H.M.)
| | - Maria Kahn
- Department of Gastroenterology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (C.R.); (M.K.); (H.M.)
| | - Helmut Messmann
- Department of Gastroenterology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (C.R.); (M.K.); (H.M.)
| | - Markus Wehler
- Department of Internal Medicine IV, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany;
- Emergency Department, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany
| | - Florian Schwarz
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (J.B.); (J.A.D.); (F.S.); (C.S.-M.)
| | - Thomas Kroencke
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (J.B.); (J.A.D.); (F.S.); (C.S.-M.)
- Correspondence: ; Tel.: +49-821-400-2441
| | - Christian Scheurig-Muenkler
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (J.B.); (J.A.D.); (F.S.); (C.S.-M.)
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Carvalho CRR, Chate RC, Sawamura MVY, Garcia ML, Lamas CA, Cardenas DAC, Lima DM, Scudeller PG, Salge JM, Nomura CH, Gutierrez MA. Chronic lung lesions in COVID-19 survivors: predictive clinical model. BMJ Open 2022; 12:e059110. [PMID: 35697456 PMCID: PMC9195157 DOI: 10.1136/bmjopen-2021-059110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 04/21/2022] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE This study aimed to propose a simple, accessible and low-cost predictive clinical model to detect lung lesions due to COVID-19 infection. DESIGN This prospective cohort study included COVID-19 survivors hospitalised between 30 March 2020 and 31 August 2020 followed-up 6 months after hospital discharge. The pulmonary function was assessed using the modified Medical Research Council (mMRC) dyspnoea scale, oximetry (SpO2), spirometry (forced vital capacity (FVC)) and chest X-ray (CXR) during an in-person consultation. Patients with abnormalities in at least one of these parameters underwent chest CT. mMRC scale, SpO2, FVC and CXR findings were used to build a machine learning model for lung lesion detection on CT. SETTING A tertiary hospital in Sao Paulo, Brazil. PARTICIPANTS 749 eligible RT-PCR-confirmed SARS-CoV-2-infected patients aged ≥18 years. PRIMARY OUTCOME MEASURE A predictive clinical model for lung lesion detection on chest CT. RESULTS There were 470 patients (63%) that had at least one sign of pulmonary involvement and were eligible for CT. Almost half of them (48%) had significant pulmonary abnormalities, including ground-glass opacities, parenchymal bands, reticulation, traction bronchiectasis and architectural distortion. The machine learning model, including the results of 257 patients with complete data on mMRC, SpO2, FVC, CXR and CT, accurately detected pulmonary lesions by the joint data of CXR, mMRC scale, SpO2 and FVC (sensitivity, 0.85±0.08; specificity, 0.70±0.06; F1-score, 0.79±0.06 and area under the curve, 0.80±0.07). CONCLUSION A predictive clinical model based on CXR, mMRC, oximetry and spirometry data can accurately screen patients with lung lesions after SARS-CoV-2 infection. Given that these examinations are highly accessible and low cost, this protocol can be automated and implemented in different countries for early detection of COVID-19 sequelae.
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Affiliation(s)
| | - Rodrigo Caruso Chate
- Instituto de Radiologia, Universidade de São Paulo Hospital das Clínicas, Sao Paulo, Brazil
| | | | - Michelle Louvaes Garcia
- Instituto do Coração-Divisão de Pneumologia, Universidade de São Paulo Hospital das Clínicas, Sao Paulo, Brazil
| | - Celina Almeida Lamas
- Instituto do Coração-Divisão de Pneumologia, Universidade de São Paulo Hospital das Clínicas, Sao Paulo, Brazil
| | | | - Daniel Mario Lima
- Instituto do Coração-Divisão de Informática, Universidade de São Paulo Hospital das Clínicas, Sao Paulo, Brazil
| | - Paula Gobi Scudeller
- Instituto do Coração-Divisão de Pneumologia, Universidade de São Paulo Hospital das Clínicas, Sao Paulo, Brazil
| | - João Marcos Salge
- Instituto do Coração-Divisão de Pneumologia, Universidade de São Paulo Hospital das Clínicas, Sao Paulo, Brazil
| | - Cesar Higa Nomura
- Instituto de Radiologia, Universidade de São Paulo Hospital das Clínicas, Sao Paulo, Brazil
| | - Marco Antonio Gutierrez
- Instituto do Coração-Divisão de Pneumologia, Universidade de São Paulo Hospital das Clínicas, Sao Paulo, Brazil
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15
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Kiziloluk S, Sert E. COVID-CCD-Net: COVID-19 and colon cancer diagnosis system with optimized CNN hyperparameters using gradient-based optimizer. Med Biol Eng Comput 2022; 60:1595-1612. [PMID: 35396625 PMCID: PMC8993211 DOI: 10.1007/s11517-022-02553-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/12/2022] [Indexed: 02/01/2023]
Abstract
Coronavirus disease-2019 (COVID-19) is a new types of coronavirus which have turned into a pandemic within a short time. Reverse transcription–polymerase chain reaction (RT-PCR) test is used for the diagnosis of COVID-19 in national healthcare centers. Because the number of PCR test kits is often limited, it is sometimes difficult to diagnose the disease at an early stage. However, X-ray technology is accessible nearly all over the world, and it succeeds in detecting symptoms of COVID-19 more successfully. Another disease which affects people’s lives to a great extent is colorectal cancer. Tissue microarray (TMA) is a technological method which is widely used for its high performance in the analysis of colorectal cancer. Computer-assisted approaches which can classify colorectal cancer in TMA images are also needed. In this respect, the present study proposes a convolutional neural network (CNN) classification approach with optimized parameters using gradient-based optimizer (GBO) algorithm. Thanks to the proposed approach, COVID-19, normal, and viral pneumonia in various chest X-ray images can be classified accurately. Additionally, other types such as epithelial and stromal regions in epidermal growth factor receptor (EFGR) colon in TMAs can also be classified. The proposed approach was called COVID-CCD-Net. AlexNet, DarkNet-19, Inception-v3, MobileNet, ResNet-18, and ShuffleNet architectures were used in COVID-CCD-Net, and the hyperparameters of this architecture was optimized for the proposed approach. Two different medical image classification datasets, namely, COVID-19 and Epistroma, were used in the present study. The experimental findings demonstrated that proposed approach increased the classification performance of the non-optimized CNN architectures significantly and displayed a very high classification performance even in very low value of epoch.
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Affiliation(s)
- Soner Kiziloluk
- Department of Computer Engineering, Malatya Turgut Özal University, Malatya, Turkey
| | - Eser Sert
- Department of Computer Engineering, Malatya Turgut Özal University, Malatya, Turkey
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16
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St Mart JP, Goh EL, Liew I, Shah Z, Sinha J. Artificial intelligence in orthopaedic surgery: transforming technological innovation in patient care and surgical training. Postgrad Med J 2022:postgradmedj-2022-141596. [PMID: 35379754 DOI: 10.1136/postgradmedj-2022-141596] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 03/19/2022] [Indexed: 12/16/2022]
Abstract
Artificial intelligence (AI) is an exciting field combining computer science with robust data sets to facilitate problem-solving. It has the potential to transform education, practice and delivery of healthcare especially in orthopaedics. This review article outlines some of the already used AI pathways as well as recent technological advances in orthopaedics. Additionally, this article further explains how potentially these two entities could be combined in the future to improve surgical education, training and ultimately patient care and outcomes.
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Affiliation(s)
- Jean-Pierre St Mart
- Trauma and Orthopaedics, North West Anglia NHS Foundation Trust, Peterborough, UK
| | - En Lin Goh
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Oxford Trauma, Kadoorie Centre, University of Oxford, Oxford, UK
| | - Ignatius Liew
- Trauma and Orthopaedics, North West Anglia NHS Foundation Trust, Peterborough, UK
| | - Zameer Shah
- Trauma and Orthopaedic Surgery, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Joydeep Sinha
- Trauma and Orthopaedic Surgery, King's College Hospital NHS Foundation Trust, London, UK
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17
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Mhlanga D. The Role of Artificial Intelligence and Machine Learning Amid the COVID-19 Pandemic: What Lessons Are We Learning on 4IR and the Sustainable Development Goals. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:1879. [PMID: 35162901 PMCID: PMC8835201 DOI: 10.3390/ijerph19031879] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 01/31/2022] [Accepted: 02/03/2022] [Indexed: 01/20/2023]
Abstract
The COVID-19 pandemic came with disruptions in every aspect of human existence, with all the sectors of the economies of the world affected greatly. In the health sector, the pandemic halted and reversed progress in health and subsequently shortened life expectancy, especially in developing and underdeveloped nations. On the other hand, machine learning and artificial intelligence contributed a great deal to the handling of the pandemic globally. Therefore, the current study aimed to assess the role played by artificial intelligence and machine learning in addressing the dangers posed by the COVID-19 pandemic, as well as extrapolate the lessons on the fourth industrial revolution and sustainable development goals. Using qualitative content analysis, the results indicated that artificial intelligence and machine learning played an important role in the response to the challenges posed by the COVID-19 pandemic. Artificial intelligence, machine learning, and various digital communication tools through telehealth performed meaningful roles in scaling customer communications, provided a platform for understanding how COVID-19 spreads, and sped up research and treatment of COVID-19, among other notable achievements. The lessons we draw from this is that, despite the disruptions and the rise in the number of unintended consequences of technology in the fourth industrial revolution, the role played by artificial intelligence and machine learning motivates us to conclude that governments must build trust in these technologies, to address health problems going forward, to ensure that the sustainable development goals related to good health and wellbeing are achieved.
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Affiliation(s)
- David Mhlanga
- Faculty of Business and Economics, University of Johannesburg, Johannesburg 2006, South Africa
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18
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The application of a deep learning system developed to reduce the time for RT-PCR in COVID-19 detection. Sci Rep 2022; 12:1234. [PMID: 35075153 PMCID: PMC8786863 DOI: 10.1038/s41598-022-05069-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 01/06/2022] [Indexed: 01/02/2023] Open
Abstract
Reducing the time to diagnose COVID-19 helps to manage insufficient isolation-bed resources and adequately accommodate critically ill patients. There is currently no alternative method to real-time reverse transcriptase polymerase chain reaction (RT-PCR), which requires 40 cycles to diagnose COVID-19. We propose a deep learning (DL) model to improve the speed of COVID-19 RT-PCR diagnosis. We developed and tested a DL model using the long short-term memory method with a dataset of fluorescence values measured in each cycle of 5810 RT-PCR tests. Among the DL models developed here, the diagnostic performance of the 21st model showed an area under the receiver operating characteristic (AUROC), sensitivity, and specificity of 84.55%, 93.33%, and 75.72%, respectively. The diagnostic performance of the 24th model showed an AUROC, sensitivity, and specificity of 91.27%, 90.00%, and 92.54%, respectively.
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19
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Shanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Using decision tree algorithms for estimating ICU admission of COVID-19 patients. INFORMATICS IN MEDICINE UNLOCKED 2022; 30:100919. [PMID: 35317245 PMCID: PMC8930186 DOI: 10.1016/j.imu.2022.100919] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 02/25/2022] [Accepted: 03/15/2022] [Indexed: 11/02/2022] Open
Abstract
Introduction Materials and methods Results Conclusions
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20
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Kufel J, Bargieł K, Koźlik M, Czogalik Ł, Dudek P, Jaworski A, Cebula M, Gruszczyńska K. Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review. Int J Med Sci 2022; 19:1743-1752. [PMID: 36313227 PMCID: PMC9608047 DOI: 10.7150/ijms.76515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 09/07/2022] [Indexed: 11/06/2022] Open
Abstract
This systematic review focuses on using artificial intelligence (AI) to detect COVID-19 infection with the help of X-ray images. Methodology: In January 2022, the authors searched PubMed, Embase and Scopus using specific medical subject headings terms and filters. All articles were independently reviewed by two reviewers. All conflicts resulting from a misunderstanding were resolved by a third independent researcher. After assessing abstracts and article usefulness, eliminating repetitions and applying inclusion and exclusion criteria, six studies were found to be qualified for this study. Results: The findings from individual studies differed due to the various approaches of the authors. Sensitivity was 72.59%-100%, specificity was 79%-99.9%, precision was 74.74%-98.7%, accuracy was 76.18%-99.81%, and the area under the curve was 95.24%-97.7%. Conclusion: AI computational models used to assess chest X-rays in the process of diagnosing COVID-19 should achieve sufficiently high sensitivity and specificity. Their results and performance should be repeatable to make them dependable for clinicians. Moreover, these additional diagnostic tools should be more affordable and faster than the currently available procedures. The performance and calculations of AI-based systems should take clinical data into account.
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Affiliation(s)
- Jakub Kufel
- Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland
| | - Katarzyna Bargieł
- Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-752 Katowice, Poland
| | - Maciej Koźlik
- Division of Cardiology and Structural Heart Disease, Medical University of Silesia, 40-635 Katowice, Poland
| | - Łukasz Czogalik
- Professor Zbigniew Religa Student Scientific Association at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland
| | - Piotr Dudek
- Professor Zbigniew Religa Student Scientific Association at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland
| | - Aleksander Jaworski
- Professor Zbigniew Religa Student Scientific Association at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland
| | - Maciej Cebula
- Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-754 Katowice, Poland
| | - Katarzyna Gruszczyńska
- Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-754 Katowice, Poland
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21
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The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions. Comput Biol Med 2021; 141:105141. [PMID: 34929464 PMCID: PMC8668784 DOI: 10.1016/j.compbiomed.2021.105141] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/06/2021] [Accepted: 12/11/2021] [Indexed: 12/21/2022]
Abstract
Since December 2019, the COVID-19 outbreak has resulted in countless deaths and has harmed all facets of human existence. COVID-19 has been designated an epidemic by the World Health Organization (WHO), which has placed a tremendous burden on nearly all countries, especially those with weak health systems. However, Deep Learning (DL) has been applied in several applications and many types of detection applications in the medical field, including thyroid diagnosis, lung nodule recognition, fetal localization, and detection of diabetic retinopathy. Furthermore, various clinical imaging sources, like Magnetic Resonance Imaging (MRI), X-ray, and Computed Tomography (CT), make DL a perfect technique to tackle the epidemic of COVID-19. Inspired by this fact, a considerable amount of research has been done. A Systematic Literature Review (SLR) has been used in this study to discover, assess, and integrate findings from relevant studies. DL techniques used in COVID-19 have also been categorized into seven main distinct categories as Long Short Term Memory Networks (LSTM), Self-Organizing Maps (SOMs), Conventional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Autoencoders, and hybrid approaches. Then, the state-of-the-art studies connected to DL techniques and applications for health problems with COVID-19 have been highlighted. Moreover, many issues and problems associated with DL implementation for COVID-19 have been addressed, which are anticipated to stimulate more investigations to control the prevalence and disaster control in the future. According to the findings, most papers are assessed using characteristics such as accuracy, delay, robustness, and scalability. Meanwhile, other features are underutilized, such as security and convergence time. Python is also the most commonly used language in papers, accounting for 75% of the time. According to the investigation, 37.83% of applications have identified chest CT/chest X-ray images for patients.
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22
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Garcia Santa Cruz B, Bossa MN, Sölter J, Husch AD. Public Covid-19 X-ray datasets and their impact on model bias - A systematic review of a significant problem. Med Image Anal 2021. [PMID: 34597937 DOI: 10.1101/2021.02.15.21251775] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Computer-aided-diagnosis and stratification of COVID-19 based on chest X-ray suffers from weak bias assessment and limited quality-control. Undetected bias induced by inappropriate use of datasets, and improper consideration of confounders prevents the translation of prediction models into clinical practice. By adopting established tools for model evaluation to the task of evaluating datasets, this study provides a systematic appraisal of publicly available COVID-19 chest X-ray datasets, determining their potential use and evaluating potential sources of bias. Only 9 out of more than a hundred identified datasets met at least the criteria for proper assessment of risk of bias and could be analysed in detail. Remarkably most of the datasets utilised in 201 papers published in peer-reviewed journals, are not among these 9 datasets, thus leading to models with high risk of bias. This raises concerns about the suitability of such models for clinical use. This systematic review highlights the limited description of datasets employed for modelling and aids researchers to select the most suitable datasets for their task.
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Affiliation(s)
- Beatriz Garcia Santa Cruz
- Centre Hospitalier de Luxembourg, 4, Rue Ernest Barble, Luxembourg L-1210, Luxembourg; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, Avenue des Hauts Fourneaux, Esch-sur-Alzette L-4362, Luxembourg.
| | - Matías Nicolás Bossa
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, Avenue des Hauts Fourneaux, Esch-sur-Alzette L-4362, Luxembourg; Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Pleinlaan 2, Brussels B-1050, Belgium
| | - Jan Sölter
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, Avenue des Hauts Fourneaux, Esch-sur-Alzette L-4362, Luxembourg
| | - Andreas Dominik Husch
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, Avenue des Hauts Fourneaux, Esch-sur-Alzette L-4362, Luxembourg
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23
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Gudigar A, Raghavendra U, Nayak S, Ooi CP, Chan WY, Gangavarapu MR, Dharmik C, Samanth J, Kadri NA, Hasikin K, Barua PD, Chakraborty S, Ciaccio EJ, Acharya UR. Role of Artificial Intelligence in COVID-19 Detection. SENSORS (BASEL, SWITZERLAND) 2021; 21:8045. [PMID: 34884045 PMCID: PMC8659534 DOI: 10.3390/s21238045] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 11/26/2021] [Accepted: 11/26/2021] [Indexed: 12/15/2022]
Abstract
The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.
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Affiliation(s)
- Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Sneha Nayak
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore;
| | - Wai Yee Chan
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia;
| | - Mokshagna Rohit Gangavarapu
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Chinmay Dharmik
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Jyothi Samanth
- Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Nahrizul Adib Kadri
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.A.K.); (K.H.)
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.A.K.); (K.H.)
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia;
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia;
| | - Subrata Chakraborty
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia;
- Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia
| | - Edward J. Ciaccio
- Department of Medicine, Columbia University Medical Center, New York, NY 10032, USA;
| | - U. Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore;
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan
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24
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Kriza C, Amenta V, Zenié A, Panidis D, Chassaigne H, Urbán P, Holzwarth U, Sauer AV, Reina V, Griesinger CB. Artificial intelligence for imaging-based COVID-19 detection: Systematic review comparing added value of AI versus human readers. Eur J Radiol 2021; 145:110028. [PMID: 34839214 PMCID: PMC8594127 DOI: 10.1016/j.ejrad.2021.110028] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/04/2021] [Accepted: 11/09/2021] [Indexed: 11/19/2022]
Abstract
PURPOSE A growing number of studies have examined whether Artificial Intelligence (AI) systems can support imaging-based diagnosis of COVID-19-caused pneumonia, including both gains in diagnostic performance and speed. However, what is currently missing is a combined appreciation of studies comparing human readers and AI. METHODS We followed PRISMA-DTA guidelines for our systematic review, searching EMBASE, PUBMED and Scopus databases. To gain insights into the potential value of AI methods, we focused on studies comparing the performance of human readers versus AI models or versus AI-supported human readings. RESULTS Our search identified 1270 studies, of which 12 fulfilled specific selection criteria. Concerning diagnostic performance, in testing datasets reported sensitivity was 42-100% (human readers, n = 9 studies), 60-95% (AI systems, n = 10) and 81-98% (AI-supported readers, n = 3), whilst reported specificity was 26-100% (human readers, n = 8), 61-96% (AI systems, n = 10) and 78-99% (AI-supported readings, n = 2). One study highlighted the potential of AI-supported readings for the assessment of lung lesion burden changes, whilst two studies indicated potential time savings for detection with AI. CONCLUSIONS Our review indicates that AI systems or AI-supported human readings show less performance variability (interquartile range) in general, and may support the differentiation of COVID-19 pneumonia from other forms of pneumonia when used in high-prevalence and symptomatic populations. However, inconsistencies related to study design, reporting of data, areas of risk of bias, as well as limitations of statistical analyses complicate clear conclusions. We therefore support efforts for developing critical elements of study design when assessing the value of AI for diagnostic imaging.
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Affiliation(s)
- Christine Kriza
- European Commission, Joint Research Centre (JRC), Via E. Fermi 2749 (TP 281) Ispra, Lombardy, Italy.
| | - Valeria Amenta
- European Commission, Joint Research Centre (JRC), Via E. Fermi 2749 (TP 281) Ispra, Lombardy, Italy
| | - Alexandre Zenié
- European Commission, Joint Research Centre (JRC), Via E. Fermi 2749 (TP 281) Ispra, Lombardy, Italy
| | - Dimitris Panidis
- European Commission, Joint Research Centre (JRC), Via E. Fermi 2749 (TP 281) Ispra, Lombardy, Italy
| | - Hubert Chassaigne
- European Commission, Joint Research Centre (JRC), Via E. Fermi 2749 (TP 281) Ispra, Lombardy, Italy
| | - Patricia Urbán
- European Commission, Joint Research Centre (JRC), Via E. Fermi 2749 (TP 281) Ispra, Lombardy, Italy
| | - Uwe Holzwarth
- European Commission, Joint Research Centre (JRC), Via E. Fermi 2749 (TP 281) Ispra, Lombardy, Italy
| | - Aisha Vanessa Sauer
- European Commission, Joint Research Centre (JRC), Via E. Fermi 2749 (TP 281) Ispra, Lombardy, Italy
| | - Vittorio Reina
- European Commission, Joint Research Centre (JRC), Via E. Fermi 2749 (TP 281) Ispra, Lombardy, Italy
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Abstract
Purpose of Review Sepsis is a leading cause of death worldwide. Groundbreaking international collaborative efforts have culminated in the widely accepted surviving sepsis guidelines, with iterative improvements in management strategies and definitions providing important advances in care for patients. Key to the diagnosis of sepsis is identification of infection, and whilst the diagnostic criteria for sepsis is now clear, the diagnosis of infection remains a challenge and there is often discordance between clinician assessments for infection. Recent Findings We review the utility of common biochemical, microbiological and radiological tools employed by clinicians to diagnose infection and explore the difficulty of making a diagnosis of infection in severe inflammatory states through illustrative case reports. Finally, we discuss some of the novel and emerging approaches in diagnosis of infection and sepsis. Summary While prompt diagnosis and treatment of sepsis is essential to improve outcomes in sepsis, there remains no single tool to reliably identify or exclude infection. This contributes to unnecessary antimicrobial use that is harmful to individuals and populations. There is therefore a pressing need for novel solutions. Machine learning approaches using multiple diagnostic and clinical inputs may offer a potential solution but as yet these approaches remain experimental.
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Development and prospective validation of COVID-19 chest X-ray screening model for patients attending emergency departments. Sci Rep 2021; 11:20384. [PMID: 34650190 PMCID: PMC8516957 DOI: 10.1038/s41598-021-99986-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 10/05/2021] [Indexed: 01/08/2023] Open
Abstract
Chest X-rays (CXRs) are the first-line investigation in patients presenting to emergency departments (EDs) with dyspnoea and are a valuable adjunct to clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to facilitate rapid triage of CXRs for further patient testing and/or isolation. In this work we develop an AI algorithm, CovIx, to differentiate normal, abnormal, non-COVID-19 pneumonia, and COVID-19 CXRs using a multicentre cohort of 293,143 CXRs. The algorithm is prospectively validated in 3289 CXRs acquired from patients presenting to ED with symptoms of COVID-19 across four sites in NHS Greater Glasgow and Clyde. CovIx achieves area under receiver operating characteristic curve for COVID-19 of 0.86, with sensitivity and F1-score up to 0.83 and 0.71 respectively, and performs on-par with four board-certified radiologists. AI-based algorithms can identify CXRs with COVID-19 associated pneumonia, as well as distinguish non-COVID pneumonias in symptomatic patients presenting to ED. Pre-trained models and inference scripts are freely available at https://github.com/beringresearch/bravecx-covid.
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López-Cabrera JD, Orozco-Morales R, Portal-Díaz JA, Lovelle-Enríquez O, Pérez-Díaz M. Current limitations to identify covid-19 using artificial intelligence with chest x-ray imaging (part ii). The shortcut learning problem. HEALTH AND TECHNOLOGY 2021; 11:1331-1345. [PMID: 34660166 PMCID: PMC8502237 DOI: 10.1007/s12553-021-00609-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 10/05/2021] [Indexed: 12/12/2022]
Abstract
Since the outbreak of the COVID-19 pandemic, computer vision researchers have been working on automatic identification of this disease using radiological images. The results achieved by automatic classification methods far exceed those of human specialists, with sensitivity as high as 100% being reported. However, prestigious radiology societies have stated that the use of this type of imaging alone is not recommended as a diagnostic method. According to some experts the patterns presented in these images are unspecific and subtle, overlapping with other viral pneumonias. This report seeks to evaluate the analysis the robustness and generalizability of different approaches using artificial intelligence, deep learning and computer vision to identify COVID-19 using chest X-rays images. We also seek to alert researchers and reviewers to the issue of "shortcut learning". Recommendations are presented to identify whether COVID-19 automatic classification models are being affected by shortcut learning. Firstly, papers using explainable artificial intelligence methods are reviewed. The results of applying external validation sets are evaluated to determine the generalizability of these methods. Finally, studies that apply traditional computer vision methods to perform the same task are considered. It is evident that using the whole chest X-Ray image or the bounding box of the lungs, the image regions that contribute most to the classification appear outside of the lung region, something that is not likely possible. In addition, although the investigations that evaluated their models on data sets external to the training set, the effectiveness of these models decreased significantly, it may provide a more realistic representation as how the model will perform in the clinic. The results indicate that, so far, the existing models often involve shortcut learning, which makes their use less appropriate in the clinical setting.
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Affiliation(s)
- José Daniel López-Cabrera
- Centro de Investigaciones de La Informática, Facultad de Matemática, Física y Computación, Universidad Central “Marta Abreu” de Las Villas, Villa Clara, Santa Clara, Cuba
| | - Rubén Orozco-Morales
- Departamento de Control Automático, Facultad de Ingeniería Eléctrica, Universidad Central “Marta Abreu” de Las Villas, Villa Clara, Santa Clara, Cuba
| | - Jorge Armando Portal-Díaz
- Departamento de Control Automático, Facultad de Ingeniería Eléctrica, Universidad Central “Marta Abreu” de Las Villas, Villa Clara, Santa Clara, Cuba
| | - Orlando Lovelle-Enríquez
- Departamento de Imagenología, Hospital Comandante Manuel Fajardo Rivero, Villa Clara, Santa Clara, Cuba
| | - Marlén Pérez-Díaz
- Departamento de Control Automático, Facultad de Ingeniería Eléctrica, Universidad Central “Marta Abreu” de Las Villas, Villa Clara, Santa Clara, Cuba
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Public Covid-19 X-ray datasets and their impact on model bias - A systematic review of a significant problem. Med Image Anal 2021; 74:102225. [PMID: 34597937 PMCID: PMC8479314 DOI: 10.1016/j.media.2021.102225] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 08/29/2021] [Accepted: 09/02/2021] [Indexed: 12/23/2022]
Abstract
Computer-aided-diagnosis and stratification of COVID-19 based on chest X-ray suffers from weak bias assessment and limited quality-control. Undetected bias induced by inappropriate use of datasets, and improper consideration of confounders prevents the translation of prediction models into clinical practice. By adopting established tools for model evaluation to the task of evaluating datasets, this study provides a systematic appraisal of publicly available COVID-19 chest X-ray datasets, determining their potential use and evaluating potential sources of bias. Only 9 out of more than a hundred identified datasets met at least the criteria for proper assessment of risk of bias and could be analysed in detail. Remarkably most of the datasets utilised in 201 papers published in peer-reviewed journals, are not among these 9 datasets, thus leading to models with high risk of bias. This raises concerns about the suitability of such models for clinical use. This systematic review highlights the limited description of datasets employed for modelling and aids researchers to select the most suitable datasets for their task.
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Murugan R, Goel T, Mirjalili S, Chakrabartty DK. WOANet: Whale optimized deep neural network for the classification of COVID-19 from radiography images. Biocybern Biomed Eng 2021; 41:1702-1718. [PMID: 34720309 PMCID: PMC8536521 DOI: 10.1016/j.bbe.2021.10.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 10/02/2021] [Accepted: 10/08/2021] [Indexed: 12/23/2022]
Abstract
Coronavirus Diseases (COVID-19) is a new disease that will be declared a global pandemic in 2020. It is characterized by a constellation of traits like fever, dry cough, dyspnea, fatigue, chest pain, etc. Clinical findings have shown that the human chest Computed Tomography(CT) images can diagnose lung infection in most COVID-19 patients. Visual changes in CT scan due to COVID-19 is subjective and evaluated by radiologists for diagnosis purpose. Deep Learning (DL) can provide an automatic diagnosis tool to relieve radiologists' burden for quantitative analysis of CT scan images in patients. However, DL techniques face different training problems like mode collapse and instability. Deciding on training hyper-parameters to adjust the weight and biases of DL by a given CT image dataset is crucial for achieving the best accuracy. This paper combines the backpropagation algorithm and Whale Optimization Algorithm (WOA) to optimize such DL networks. Experimental results for the diagnosis of COVID-19 patients from a comprehensive COVID-CT scan dataset show the best performance compared to other recent methods. The proposed network architecture results were validated with the existing pre-trained network to prove the efficiency of the network.
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Affiliation(s)
- R Murugan
- Bio-Medical Imaging Laboratory(BIOMIL), Department of Electronics and communication Engineering, National Institute Of Technology Silchar, Assam 788010, India
| | - Tripti Goel
- Bio-Medical Imaging Laboratory(BIOMIL), Department of Electronics and communication Engineering, National Institute Of Technology Silchar, Assam 788010, India
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006, Australia
- Yonsei Frontier Lab, Yonsei University, Seoul, South Korea
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30
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Saha R, Aich S, Tripathy S, Kim HC. Artificial Intelligence Is Reshaping Healthcare amid COVID-19: A Review in the Context of Diagnosis & Prognosis. Diagnostics (Basel) 2021; 11:1604. [PMID: 34573946 PMCID: PMC8471992 DOI: 10.3390/diagnostics11091604] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 08/28/2021] [Accepted: 09/01/2021] [Indexed: 12/24/2022] Open
Abstract
Preventing respiratory failure is crucial in a large proportion of COVID-19 patients infected with SARS-CoV-2 virus pneumonia termed as Novel Coronavirus Pneumonia (NCP). Rapid diagnosis and detection of high-risk patients for effective interventions have been shown to be troublesome. Using a large, computed tomography (CT) database, we developed an artificial intelligence (AI) parameter to diagnose NCP and distinguish it from other kinds of pneumonia and traditional controls. The literature was studied and analyzed from diverse assets which include Scopus, Nature medicine, IEEE, Google scholar, Wiley Library, and PubMed. The search terms used were 'COVID-19', 'AI', 'diagnosis', and 'prognosis'. To strengthen the overall performance of AI in COVID-19 diagnosis and prognosis, we segregated several components to perceive threats and opportunities, as well as their inter-dependencies that affect the healthcare sector. This paper seeks to pick out the crucial fulfillment of factors for AI with inside the healthcare sector in the Indian context. Using critical literature review and experts' opinion, a total of 11 factors affecting COVID-19 diagnosis and prognosis were detected, and we eventually used an interpretive structural model (ISM) to build a framework of interrelationships among the identified factors. Finally, the matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis resulted the driving and dependence powers of these identified factors. Our analysis will help healthcare stakeholders to realize the requirements for successful implementation of AI.
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Affiliation(s)
- Rajnandini Saha
- School of Biotechnology, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India;
| | | | - Sushanta Tripathy
- School of Mechanical Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India
| | - Hee-Cheol Kim
- Institute of Digital Anti-Aging Healthcare, College of AI Convergence, u-AHRC, Inje University, Gimhae 50834, Korea
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31
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Erdaw Y, Tachbele E. Machine Learning Model Applied on Chest X-Ray Images Enables Automatic Detection of COVID-19 Cases with High Accuracy. Int J Gen Med 2021; 14:4923-4931. [PMID: 34483682 PMCID: PMC8409602 DOI: 10.2147/ijgm.s325609] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 08/05/2021] [Indexed: 12/21/2022] Open
Abstract
PURPOSE This research was designed to investigate the application of artificial intelligence (AI) in the rapid and accurate diagnosis of coronavirus disease 2019 (COVID-19) using digital chest X-ray images, and to develop a robust computer-aided application for the automatic classification of COVID-19 pneumonia from other pneumonia and normal images. MATERIALS AND METHODS A total of 1100 chest X-ray images were randomly selected from three different open sources, containing 300 X-ray images of confirmed COVID-19 patients, 400 images of other pneumonia patients, and 400 normal X-ray images. In this study, a classical machine learning approach was employed. The model was built using the support vector machine (SVM) classifier algorithm. The SVM was trained by 630 features obtained from the HOG descriptor, which was quantized into 30 orientation bins in the range between 0 and 360. The model was validated using a 10-fold cross-validation method. The performance of the model was evaluated using appropriate classification metrics, including sensitivity, specificity, area under the curve, positive predictive value, negative predictive value, kappa, and accuracy. RESULTS The multi-level classification model was able to distinguish COVID-19 patients with a sensitivity of 97.92% and specificity of 98.91%, for the internal testing or cross-validation. For the independent external testing, the model showed sensitivity of 95% and specificity of 98.13%, for distinguishing COVID-19 from other pneumonia and no-findings. The binary classification model was able to distinguish COVID-19 patients with a sensitivity of 99.58% and specificity of 99.69%, for the internal testing. For the independent external testing, the model showed a sensitivity of 98.33% and specificity of 100%, for distinguishing COVID-19 from normal images. CONCLUSION The model can achieve the rapid and accurate identification of COVID-19 patients from chest X-rays with more than 97% accuracy. This high accuracy and very rapid computer-aided diagnostic approach would be very helpful to control the pandemic.
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Affiliation(s)
- Yabsera Erdaw
- Electrical and Mechanical Engineering, Addis Ababa Science & Technology University, Addis Ababa, Ethiopia
| | - Erdaw Tachbele
- Nursing & Midwifery, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
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32
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Chen L, Rezaei T. A New Optimal Diagnosis System for Coronavirus (COVID-19) Diagnosis Based on Archimedes Optimization Algorithm on Chest X-Ray Images. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:7788491. [PMID: 34447431 PMCID: PMC8384522 DOI: 10.1155/2021/7788491] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 07/29/2021] [Accepted: 08/11/2021] [Indexed: 11/18/2022]
Abstract
The new coronavirus, COVID-19, has affected people all over the world. Coronaviruses are a large group of viruses that can infect animals and humans and cause respiratory distress; these discomforts may be as mild as a cold or as severe as pneumonia. Correct detection of this disease can help to avoid its spreading increasingly. In this paper, a new CAD-based approach is suggested for the optimal diagnosis of this disease from chest X-ray images. The proposed method starts with a min-max normalization to scale all data into a normal scale, and then, histogram equalization is performed to improve the quality of the image before main processing. Afterward, 18 different features are extracted from the image. To decrease the method difficulty, the minimum features are selected based on a metaheuristic called Archimedes optimization algorithm (AOA). The model is then implemented on three datasets, and its results are compared with four other state-of-the-art methods. The final results indicated that the proposed method with 86% accuracy and 96% precision has the highest balance between accuracy and reliability with the compared methods as a diagnostic system for COVID-19.
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Affiliation(s)
- Liping Chen
- College of Computer, Weinan Normal University, Weinan, Shaanxi, China
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33
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Akyol K, Şen B. Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images. Interdiscip Sci 2021; 14:89-100. [PMID: 34313974 PMCID: PMC8313418 DOI: 10.1007/s12539-021-00463-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 07/08/2021] [Accepted: 07/12/2021] [Indexed: 12/23/2022]
Abstract
Coronavirus disease, which comes up in China at the end of 2019 and showed different symptoms in people infected, affected millions of people. Computer-aided expert systems are needed due to the inadequacy of the reverse transcription-polymerase chain reaction kit, which is widely used in the diagnosis of this disease. Undoubtedly, expert systems that provide effective solutions to many problems will be very useful in the detection of Covid-19 disease, especially when unskilled personnel and financial deficiencies in underdeveloped countries are taken into consideration. In the literature, there are numerous machine learning approaches built with different classifiers in the detection of this disease. This paper proposes an approach based on deep learning which detects Covid-19 and no-finding cases using chest X-ray images. Here, the classification performance of the Bi-LSTM network on the deep features was compared with the Deep Neural Network within the frame of the fivefold cross-validation technique. Accuracy, sensitivity, specificity and precision metrics were used to evaluate the classification performance of the trained models. Bi-LSTM network presented better performance compare to DNN with 97.6% value of high accuracy despite the few numbers of Covid-19 images in the dataset. In addition, it is understood that concatenated deep features more meaningful than deep features obtained with pre-trained networks by one by, as well. Consequently, it is thought that the proposed study based on the Bi-LSTM network and concatenated deep features will be noteworthy in the design of highly sensitive automated Covid-19 monitoring systems.
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Affiliation(s)
- Kemal Akyol
- Department of Computer Engineering, Faculty of Engineering and Architecture, Kastamonu University, Kastamonu, Turkey.
| | - Baha Şen
- Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Ankara Yıldırım Beyazıt University, Ankara, Turkey
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34
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Dogan O, Tiwari S, Jabbar MA, Guggari S. A systematic review on AI/ML approaches against COVID-19 outbreak. COMPLEX INTELL SYST 2021; 7:2655-2678. [PMID: 34777970 PMCID: PMC8256231 DOI: 10.1007/s40747-021-00424-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 06/05/2021] [Indexed: 12/24/2022]
Abstract
A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.
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Affiliation(s)
- Onur Dogan
- Department of Industrial Engineering, Izmir Bakircay University, 35665 Izmir, Turkey.,Research Center for Data Analytics and Spatial Data Modeling (RC-DAS), Izmir Bakircay University, 35665 Izmir, Turkey
| | - Sanju Tiwari
- Department of Computer Science, Universidad Autonoma de Tamaulipas, Ciudad Victoria, Mexico
| | - M A Jabbar
- Vardhaman College of Engineering, Kacharam, India
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35
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Hwang EJ, Kim KB, Kim JY, Lim JK, Nam JG, Choi H, Kim H, Yoon SH, Goo JM, Park CM. COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system. PLoS One 2021; 16:e0252440. [PMID: 34097708 PMCID: PMC8184006 DOI: 10.1371/journal.pone.0252440] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 05/17/2021] [Indexed: 01/08/2023] Open
Abstract
Chest X-rays (CXRs) can help triage for Coronavirus disease (COVID-19) patients in resource-constrained environments, and a computer-aided detection system (CAD) that can identify pneumonia on CXR may help the triage of patients in those environment where expert radiologists are not available. However, the performance of existing CAD for identifying COVID-19 and associated pneumonia on CXRs has been scarcely investigated. In this study, CXRs of patients with and without COVID-19 confirmed by reverse transcriptase polymerase chain reaction (RT-PCR) were retrospectively collected from four and one institution, respectively, and a commercialized, regulatory-approved CAD that can identify various abnormalities including pneumonia was used to analyze each CXR. Performance of the CAD was evaluated using area under the receiver operating characteristic curves (AUCs), with reference standards of the RT-PCR results and the presence of findings of pneumonia on chest CTs obtained within 24 hours from the CXR. For comparison, 5 thoracic radiologists and 5 non-radiologist physicians independently interpreted the CXRs. Afterward, they re-interpreted the CXRs with corresponding CAD results. The performance of CAD (AUCs, 0.714 and 0.790 against RT-PCR and chest CT, respectively hereinafter) were similar with those of thoracic radiologists (AUCs, 0.701 and 0.784), and higher than those of non-radiologist physicians (AUCs, 0.584 and 0.650). Non-radiologist physicians showed significantly improved performance when assisted with the CAD (AUCs, 0.584 to 0.664 and 0.650 to 0.738). In addition, inter-reader agreement among physicians was also improved in the CAD-assisted interpretation (Fleiss' kappa coefficient, 0.209 to 0.322). In conclusion, radiologist-level performance of the CAD in identifying COVID-19 and associated pneumonia on CXR and enhanced performance of non-radiologist physicians with the CAD assistance suggest that the CAD can support physicians in interpreting CXRs and helping image-based triage of COVID-19 patients in resource-constrained environment.
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Affiliation(s)
- Eui Jin Hwang
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Ki Beom Kim
- Department of Radiology, Daegu Fatima Hospital, Daegu, Korea
| | - Jin Young Kim
- Department of Radiology, Keimyung University School of Medicine, Daegu, Korea
| | - Jae-Kwang Lim
- Department of Radiology, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Ju Gang Nam
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Hyewon Choi
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Hyungjin Kim
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Soon Ho Yoon
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Jin Mo Goo
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
| | - Chang Min Park
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
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36
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Çallı E, Sogancioglu E, van Ginneken B, van Leeuwen KG, Murphy K. Deep learning for chest X-ray analysis: A survey. Med Image Anal 2021; 72:102125. [PMID: 34171622 DOI: 10.1016/j.media.2021.102125] [Citation(s) in RCA: 103] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/17/2021] [Accepted: 05/27/2021] [Indexed: 12/14/2022]
Abstract
Recent advances in deep learning have led to a promising performance in many medical image analysis tasks. As the most commonly performed radiological exam, chest radiographs are a particularly important modality for which a variety of applications have been researched. The release of multiple, large, publicly available chest X-ray datasets in recent years has encouraged research interest and boosted the number of publications. In this paper, we review all studies using deep learning on chest radiographs published before March 2021, categorizing works by task: image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation. Detailed descriptions of all publicly available datasets are included and commercial systems in the field are described. A comprehensive discussion of the current state of the art is provided, including caveats on the use of public datasets, the requirements of clinically useful systems and gaps in the current literature.
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Affiliation(s)
- Erdi Çallı
- Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands.
| | - Ecem Sogancioglu
- Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands
| | - Bram van Ginneken
- Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands
| | - Kicky G van Leeuwen
- Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands
| | - Keelin Murphy
- Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands
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Adamidi ES, Mitsis K, Nikita KS. Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review. Comput Struct Biotechnol J 2021; 19:2833-2850. [PMID: 34025952 PMCID: PMC8123783 DOI: 10.1016/j.csbj.2021.05.010] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/01/2021] [Accepted: 05/02/2021] [Indexed: 12/23/2022] Open
Abstract
The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.
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Key Words
- ABG, Arterial Blood Gas
- ADA, Adenosine Deaminase
- AI, Artificial Intelligence
- ANN, Artificial Neural Networks
- APTT, Activated Partial Thromboplastin Time
- ARMED, Attribute Reduction with Multi-objective Decomposition Ensemble optimizer
- AUC, Area Under the Curve
- Acc, Accuracy
- Adaboost, Adaptive Boosting
- Apol AI, Apolipoprotein AI
- Apol B, Apolipoprotein B
- Artificial intelligence
- BNB, Bernoulli Naïve Bayes
- BUN, Blood Urea Nitrogen
- CI, Confidence Interval
- CK-MB, Creatine Kinase isoenzyme
- CNN, Convolutional Neural Networks
- COVID-19
- CPP, COVID-19 Positive Patients
- CRP, C-Reactive Protein
- CRT, Classification and Regression Decision Tree
- CoxPH, Cox Proportional Hazards
- DCNN, Deep Convolutional Neural Networks
- DL, Deep Learning
- DLC, Density Lipoprotein Cholesterol
- DNN, Deep Neural Networks
- DT, Decision Tree
- Diagnosis
- ED, Emergency Department
- ESR, Erythrocyte Sedimentation Rate
- ET, Extra Trees
- FCV, Fold Cross Validation
- FL, Federated Learning
- FiO2, Fraction of Inspiration O2
- GBDT, Gradient Boost Decision Tree
- GBM light, Gradient Boosting Machine light
- GDCNN, Genetic Deep Learning Convolutional Neural Network
- GFR, Glomerular Filtration Rate
- GFS, Gradient boosted feature selection
- GGT, Glutamyl Transpeptidase
- GNB, Gaussian Naïve Bayes
- HDLC, High Density Lipoprotein Cholesterol
- INR, International Normalized Ratio
- Inception Resnet, Inception Residual Neural Network
- L1LR, L1 Regularized Logistic Regression
- LASSO, Least Absolute Shrinkage and Selection Operator
- LDA, Linear Discriminant Analysis
- LDH, Lactate Dehydrogenase
- LDLC, Low Density Lipoprotein Cholesterol
- LR, Logistic Regression
- LSTM, Long-Short Term Memory
- MCHC, Mean Corpuscular Hemoglobin Concentration
- MCV, Mean corpuscular volume
- ML, Machine Learning
- MLP, MultiLayer Perceptron
- MPV, Mean Platelet Volume
- MRMR, Maximum Relevance Minimum Redundancy
- Multimodal data
- NB, Naïve Bayes
- NLP, Natural Language Processing
- NPV, Negative Predictive Values
- Nadam optimizer, Nesterov Accelerated Adaptive Moment optimizer
- OB, Occult Blood test
- PCT, Thrombocytocrit
- PPV, Positive Predictive Values
- PWD, Platelet Distribution Width
- PaO2, Arterial Oxygen Tension
- Paco2, Arterial Carbondioxide Tension
- Prognosis
- RBC, Red Blood Cell
- RBF, Radial Basis Function
- RBP, Retinol Binding Protein
- RDW, Red blood cell Distribution Width
- RF, Random Forest
- RFE, Recursive Feature Elimination
- RSV, Respiratory Syncytial Virus
- SEN, Sensitivity
- SG, Specific Gravity
- SMOTE, Synthetic Minority Oversampling Technique
- SPE, Specificity
- SRLSR, Sparse Rescaled Linear Square Regression
- SVM, Support Vector Machine
- SaO2, Arterial Oxygen saturation
- Screening
- TBA, Total Bile Acid
- TTS, Training Test Split
- WBC, White Blood Cell count
- XGB, eXtreme Gradient Boost
- k-NN, K-Nearest Neighbor
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Affiliation(s)
- Eleni S. Adamidi
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Konstantinos Mitsis
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Konstantina S. Nikita
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
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Wehbe RM, Sheng J, Dutta S, Chai S, Dravid A, Barutcu S, Wu Y, Cantrell DR, Xiao N, Allen BD, MacNealy GA, Savas H, Agrawal R, Parekh N, Katsaggelos AK. DeepCOVID-XR: An Artificial Intelligence Algorithm to Detect COVID-19 on Chest Radiographs Trained and Tested on a Large U.S. Clinical Data Set. Radiology 2021; 299:E167-E176. [PMID: 33231531 PMCID: PMC7993244 DOI: 10.1148/radiol.2020203511] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 10/26/2020] [Accepted: 10/30/2020] [Indexed: 12/23/2022]
Abstract
Background There are characteristic findings of coronavirus disease 2019 (COVID-19) on chest images. An artificial intelligence (AI) algorithm to detect COVID-19 on chest radiographs might be useful for triage or infection control within a hospital setting, but prior reports have been limited by small data sets, poor data quality, or both. Purpose To present DeepCOVID-XR, a deep learning AI algorithm to detect COVID-19 on chest radiographs, that was trained and tested on a large clinical data set. Materials and Methods DeepCOVID-XR is an ensemble of convolutional neural networks developed to detect COVID-19 on frontal chest radiographs, with reverse-transcription polymerase chain reaction test results as the reference standard. The algorithm was trained and validated on 14 788 images (4253 positive for COVID-19) from sites across the Northwestern Memorial Health Care System from February 2020 to April 2020 and was then tested on 2214 images (1192 positive for COVID-19) from a single hold-out institution. Performance of the algorithm was compared with interpretations from five experienced thoracic radiologists on 300 random test images using the McNemar test for sensitivity and specificity and the DeLong test for the area under the receiver operating characteristic curve (AUC). Results A total of 5853 patients (mean age, 58 years ± 19 [standard deviation]; 3101 women) were evaluated across data sets. For the entire test set, accuracy of DeepCOVID-XR was 83%, with an AUC of 0.90. For 300 random test images (134 positive for COVID-19), accuracy of DeepCOVID-XR was 82%, compared with that of individual radiologists (range, 76%-81%) and the consensus of all five radiologists (81%). DeepCOVID-XR had a significantly higher sensitivity (71%) than one radiologist (60%, P < .001) and significantly higher specificity (92%) than two radiologists (75%, P < .001; 84%, P = .009). AUC of DeepCOVID-XR was 0.88 compared with the consensus AUC of 0.85 (P = .13 for comparison). With consensus interpretation as the reference standard, the AUC of DeepCOVID-XR was 0.95 (95% CI: 0.92, 0.98). Conclusion DeepCOVID-XR, an artificial intelligence algorithm, detected coronavirus disease 2019 on chest radiographs with a performance similar to that of experienced thoracic radiologists in consensus. © RSNA, 2020 Supplemental material is available for this article. See also the editorial by van Ginneken in this issue.
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Affiliation(s)
- Ramsey M. Wehbe
- From the Division of Cardiology, Department of Medicine and Bluhm Cardiovascular Institute (R.M.W.), Division of Neurointerventional Radiology (D.R.C.), Division of Interventional Radiology (N.X.), and Division of Thoracic Imaging (B.D.A., G.A.M., H.S., R.A., N.P.), Department of Radiology, Northwestern Memorial Hospital, 676 N St Clair St, Chicago, IL 60611; and Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Evanston, Ill (J.S., S.D., S.C., A.D., S.B., Y.W., A.K.K.)
| | - Jiayue Sheng
- From the Division of Cardiology, Department of Medicine and Bluhm Cardiovascular Institute (R.M.W.), Division of Neurointerventional Radiology (D.R.C.), Division of Interventional Radiology (N.X.), and Division of Thoracic Imaging (B.D.A., G.A.M., H.S., R.A., N.P.), Department of Radiology, Northwestern Memorial Hospital, 676 N St Clair St, Chicago, IL 60611; and Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Evanston, Ill (J.S., S.D., S.C., A.D., S.B., Y.W., A.K.K.)
| | - Shinjan Dutta
- From the Division of Cardiology, Department of Medicine and Bluhm Cardiovascular Institute (R.M.W.), Division of Neurointerventional Radiology (D.R.C.), Division of Interventional Radiology (N.X.), and Division of Thoracic Imaging (B.D.A., G.A.M., H.S., R.A., N.P.), Department of Radiology, Northwestern Memorial Hospital, 676 N St Clair St, Chicago, IL 60611; and Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Evanston, Ill (J.S., S.D., S.C., A.D., S.B., Y.W., A.K.K.)
| | - Siyuan Chai
- From the Division of Cardiology, Department of Medicine and Bluhm Cardiovascular Institute (R.M.W.), Division of Neurointerventional Radiology (D.R.C.), Division of Interventional Radiology (N.X.), and Division of Thoracic Imaging (B.D.A., G.A.M., H.S., R.A., N.P.), Department of Radiology, Northwestern Memorial Hospital, 676 N St Clair St, Chicago, IL 60611; and Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Evanston, Ill (J.S., S.D., S.C., A.D., S.B., Y.W., A.K.K.)
| | - Amil Dravid
- From the Division of Cardiology, Department of Medicine and Bluhm Cardiovascular Institute (R.M.W.), Division of Neurointerventional Radiology (D.R.C.), Division of Interventional Radiology (N.X.), and Division of Thoracic Imaging (B.D.A., G.A.M., H.S., R.A., N.P.), Department of Radiology, Northwestern Memorial Hospital, 676 N St Clair St, Chicago, IL 60611; and Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Evanston, Ill (J.S., S.D., S.C., A.D., S.B., Y.W., A.K.K.)
| | - Semih Barutcu
- From the Division of Cardiology, Department of Medicine and Bluhm Cardiovascular Institute (R.M.W.), Division of Neurointerventional Radiology (D.R.C.), Division of Interventional Radiology (N.X.), and Division of Thoracic Imaging (B.D.A., G.A.M., H.S., R.A., N.P.), Department of Radiology, Northwestern Memorial Hospital, 676 N St Clair St, Chicago, IL 60611; and Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Evanston, Ill (J.S., S.D., S.C., A.D., S.B., Y.W., A.K.K.)
| | - Yunan Wu
- From the Division of Cardiology, Department of Medicine and Bluhm Cardiovascular Institute (R.M.W.), Division of Neurointerventional Radiology (D.R.C.), Division of Interventional Radiology (N.X.), and Division of Thoracic Imaging (B.D.A., G.A.M., H.S., R.A., N.P.), Department of Radiology, Northwestern Memorial Hospital, 676 N St Clair St, Chicago, IL 60611; and Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Evanston, Ill (J.S., S.D., S.C., A.D., S.B., Y.W., A.K.K.)
| | - Donald R. Cantrell
- From the Division of Cardiology, Department of Medicine and Bluhm Cardiovascular Institute (R.M.W.), Division of Neurointerventional Radiology (D.R.C.), Division of Interventional Radiology (N.X.), and Division of Thoracic Imaging (B.D.A., G.A.M., H.S., R.A., N.P.), Department of Radiology, Northwestern Memorial Hospital, 676 N St Clair St, Chicago, IL 60611; and Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Evanston, Ill (J.S., S.D., S.C., A.D., S.B., Y.W., A.K.K.)
| | - Nicholas Xiao
- From the Division of Cardiology, Department of Medicine and Bluhm Cardiovascular Institute (R.M.W.), Division of Neurointerventional Radiology (D.R.C.), Division of Interventional Radiology (N.X.), and Division of Thoracic Imaging (B.D.A., G.A.M., H.S., R.A., N.P.), Department of Radiology, Northwestern Memorial Hospital, 676 N St Clair St, Chicago, IL 60611; and Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Evanston, Ill (J.S., S.D., S.C., A.D., S.B., Y.W., A.K.K.)
| | - Bradley D. Allen
- From the Division of Cardiology, Department of Medicine and Bluhm Cardiovascular Institute (R.M.W.), Division of Neurointerventional Radiology (D.R.C.), Division of Interventional Radiology (N.X.), and Division of Thoracic Imaging (B.D.A., G.A.M., H.S., R.A., N.P.), Department of Radiology, Northwestern Memorial Hospital, 676 N St Clair St, Chicago, IL 60611; and Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Evanston, Ill (J.S., S.D., S.C., A.D., S.B., Y.W., A.K.K.)
| | - Gregory A. MacNealy
- From the Division of Cardiology, Department of Medicine and Bluhm Cardiovascular Institute (R.M.W.), Division of Neurointerventional Radiology (D.R.C.), Division of Interventional Radiology (N.X.), and Division of Thoracic Imaging (B.D.A., G.A.M., H.S., R.A., N.P.), Department of Radiology, Northwestern Memorial Hospital, 676 N St Clair St, Chicago, IL 60611; and Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Evanston, Ill (J.S., S.D., S.C., A.D., S.B., Y.W., A.K.K.)
| | - Hatice Savas
- From the Division of Cardiology, Department of Medicine and Bluhm Cardiovascular Institute (R.M.W.), Division of Neurointerventional Radiology (D.R.C.), Division of Interventional Radiology (N.X.), and Division of Thoracic Imaging (B.D.A., G.A.M., H.S., R.A., N.P.), Department of Radiology, Northwestern Memorial Hospital, 676 N St Clair St, Chicago, IL 60611; and Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Evanston, Ill (J.S., S.D., S.C., A.D., S.B., Y.W., A.K.K.)
| | - Rishi Agrawal
- From the Division of Cardiology, Department of Medicine and Bluhm Cardiovascular Institute (R.M.W.), Division of Neurointerventional Radiology (D.R.C.), Division of Interventional Radiology (N.X.), and Division of Thoracic Imaging (B.D.A., G.A.M., H.S., R.A., N.P.), Department of Radiology, Northwestern Memorial Hospital, 676 N St Clair St, Chicago, IL 60611; and Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Evanston, Ill (J.S., S.D., S.C., A.D., S.B., Y.W., A.K.K.)
| | - Nishant Parekh
- From the Division of Cardiology, Department of Medicine and Bluhm Cardiovascular Institute (R.M.W.), Division of Neurointerventional Radiology (D.R.C.), Division of Interventional Radiology (N.X.), and Division of Thoracic Imaging (B.D.A., G.A.M., H.S., R.A., N.P.), Department of Radiology, Northwestern Memorial Hospital, 676 N St Clair St, Chicago, IL 60611; and Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Evanston, Ill (J.S., S.D., S.C., A.D., S.B., Y.W., A.K.K.)
| | - Aggelos K. Katsaggelos
- From the Division of Cardiology, Department of Medicine and Bluhm Cardiovascular Institute (R.M.W.), Division of Neurointerventional Radiology (D.R.C.), Division of Interventional Radiology (N.X.), and Division of Thoracic Imaging (B.D.A., G.A.M., H.S., R.A., N.P.), Department of Radiology, Northwestern Memorial Hospital, 676 N St Clair St, Chicago, IL 60611; and Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Evanston, Ill (J.S., S.D., S.C., A.D., S.B., Y.W., A.K.K.)
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Signoroni A, Savardi M, Benini S, Adami N, Leonardi R, Gibellini P, Vaccher F, Ravanelli M, Borghesi A, Maroldi R, Farina D. BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset. Med Image Anal 2021; 71:102046. [PMID: 33862337 PMCID: PMC8010334 DOI: 10.1016/j.media.2021.102046] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 02/04/2021] [Accepted: 03/17/2021] [Indexed: 12/22/2022]
Abstract
In this work we design an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients. Such semi-quantitative scoring system, namely Brixia score, is applied in serial monitoring of such patients, showing significant prognostic value, in one of the hospitals that experienced one of the highest pandemic peaks in Italy. To solve such a challenging visual task, we adopt a weakly supervised learning strategy structured to handle different tasks (segmentation, spatial alignment, and score estimation) trained with a “from-the-part-to-the-whole” procedure involving different datasets. In particular, we exploit a clinical dataset of almost 5,000 CXR annotated images collected in the same hospital. Our BS-Net demonstrates self-attentive behavior and a high degree of accuracy in all processing stages. Through inter-rater agreement tests and a gold standard comparison, we show that our solution outperforms single human annotators in rating accuracy and consistency, thus supporting the possibility of using this tool in contexts of computer-assisted monitoring. Highly resolved (super-pixel level) explainability maps are also generated, with an original technique, to visually help the understanding of the network activity on the lung areas. We also consider other scores proposed in literature and provide a comparison with a recently proposed non-specific approach. We eventually test the performance robustness of our model on an assorted public COVID-19 dataset, for which we also provide Brixia score annotations, observing good direct generalization and fine-tuning capabilities that highlight the portability of BS-Net in other clinical settings. The CXR dataset along with the source code and the trained model are publicly released for research purposes.
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Affiliation(s)
- Alberto Signoroni
- Department of Information Engineering, University of Brescia, Brescia, Italy.
| | - Mattia Savardi
- Department of Information Engineering, University of Brescia, Brescia, Italy
| | - Sergio Benini
- Department of Information Engineering, University of Brescia, Brescia, Italy
| | - Nicola Adami
- Department of Information Engineering, University of Brescia, Brescia, Italy
| | - Riccardo Leonardi
- Department of Information Engineering, University of Brescia, Brescia, Italy
| | - Paolo Gibellini
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Filippo Vaccher
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Marco Ravanelli
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Andrea Borghesi
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Roberto Maroldi
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Davide Farina
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
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Salvatore C, Interlenghi M, Monti CB, Ippolito D, Capra D, Cozzi A, Schiaffino S, Polidori A, Gandola D, Alì M, Castiglioni I, Messa C, Sardanelli F. Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia. Diagnostics (Basel) 2021; 11:530. [PMID: 33809625 PMCID: PMC8000736 DOI: 10.3390/diagnostics11030530] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/09/2021] [Accepted: 03/11/2021] [Indexed: 02/05/2023] Open
Abstract
We assessed the role of artificial intelligence applied to chest X-rays (CXRs) in supporting the diagnosis of COVID-19. We trained and cross-validated a model with an ensemble of 10 convolutional neural networks with CXRs of 98 COVID-19 patients, 88 community-acquired pneumonia (CAP) patients, and 98 subjects without either COVID-19 or CAP, collected in two Italian hospitals. The system was tested on two independent cohorts, namely, 148 patients (COVID-19, CAP, or negative) collected by one of the two hospitals (independent testing I) and 820 COVID-19 patients collected by a multicenter study (independent testing II). On the training and cross-validation dataset, sensitivity, specificity, and area under the curve (AUC) were 0.91, 0.87, and 0.93 for COVID-19 versus negative subjects, 0.85, 0.82, and 0.94 for COVID-19 versus CAP. On the independent testing I, sensitivity, specificity, and AUC were 0.98, 0.88, and 0.98 for COVID-19 versus negative subjects, 0.97, 0.96, and 0.98 for COVID-19 versus CAP. On the independent testing II, the system correctly diagnosed 652 COVID-19 patients versus negative subjects (0.80 sensitivity) and correctly differentiated 674 COVID-19 versus CAP patients (0.82 sensitivity). This system appears promising for the diagnosis and differential diagnosis of COVID-19, showing its potential as a second opinion tool in conditions of the variable prevalence of different types of infectious pneumonia.
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Affiliation(s)
- Christian Salvatore
- Department of Science, Technology, and Society, Scuola Universitaria IUSS, Istituto Universitario di Studi Superiori, Piazza della Vittoria 15, 27100 Pavia, Italy;
- DeepTrace Technologies S.R.L., via Conservatorio 17, 20122 Milano, Italy; (M.I.); (A.P.)
| | - Matteo Interlenghi
- DeepTrace Technologies S.R.L., via Conservatorio 17, 20122 Milano, Italy; (M.I.); (A.P.)
| | - Caterina B. Monti
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milano, Italy; (C.B.M.); (D.C.); (A.C.); (F.S.)
| | - Davide Ippolito
- Department of Radiology, ASST Monza—Ospedale San Gerardo, Via Pergolesi 33, 20900 Monza, Italy; (D.I.); (D.G.)
| | - Davide Capra
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milano, Italy; (C.B.M.); (D.C.); (A.C.); (F.S.)
| | - Andrea Cozzi
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milano, Italy; (C.B.M.); (D.C.); (A.C.); (F.S.)
| | - Simone Schiaffino
- Unit of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097 San Donato Milanese, Italy;
| | - Annalisa Polidori
- DeepTrace Technologies S.R.L., via Conservatorio 17, 20122 Milano, Italy; (M.I.); (A.P.)
| | - Davide Gandola
- Department of Radiology, ASST Monza—Ospedale San Gerardo, Via Pergolesi 33, 20900 Monza, Italy; (D.I.); (D.G.)
| | - Marco Alì
- Department of Diagnostic Imaging and Stereotactic Radiosurgery, C.D.I. Centro Diagnostico Italiano S.p.A., Via Saint Bon 20, 20147 Milano, Italy;
| | - Isabella Castiglioni
- Department of Physics, Università degli Studi di Milano-Bicocca, Piazza della Scienza 3, 20126 Milano, Italy
- Institute of Biomedical Imaging and Physiology, Consiglio Nazionale delle Ricerche, Via Fratelli Cervi 93, 20090 Segrate, Italy
| | - Cristina Messa
- School of Medicine and Surgery, Università degli Studi di Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milano, Italy;
- Fondazione Tecnomed, Università degli Studi di Milano-Bicocca, Palazzina Ciclotrone—Via Pergolesi 33, 20900 Monza, Italy
| | - Francesco Sardanelli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milano, Italy; (C.B.M.); (D.C.); (A.C.); (F.S.)
- Unit of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097 San Donato Milanese, Italy;
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Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging. HEALTH AND TECHNOLOGY 2021; 11:411-424. [PMID: 33585153 PMCID: PMC7864619 DOI: 10.1007/s12553-021-00520-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 01/11/2021] [Indexed: 12/17/2022]
Abstract
The scientific community has joined forces to mitigate the scope of the current COVID-19 pandemic. The early identification of the disease, as well as the evaluation of its evolution is a primary task for the timely application of medical protocols. The use of medical images of the chest provides valuable information to specialists. Specifically, chest X-ray images have been the focus of many investigations that apply artificial intelligence techniques for the automatic classification of this disease. The results achieved to date on the subject are promising. However, some results of these investigations contain errors that must be corrected to obtain appropriate models for clinical use. This research discusses some of the problems found in the current scientific literature on the application of artificial intelligence techniques in the automatic classification of COVID-19. It is evident that in most of the reviewed works an incorrect evaluation protocol is applied, which leads to overestimating the results.
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Alghamdi HS, Amoudi G, Elhag S, Saeedi K, Nasser J. Deep Learning Approaches for Detecting COVID-19 From Chest X-Ray Images: A Survey. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:20235-20254. [PMID: 34786304 PMCID: PMC8545235 DOI: 10.1109/access.2021.3054484] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 01/21/2021] [Indexed: 05/18/2023]
Abstract
Chest X-ray (CXR) imaging is a standard and crucial examination method used for suspected cases of coronavirus disease (COVID-19). In profoundly affected or limited resource areas, CXR imaging is preferable owing to its availability, low cost, and rapid results. However, given the rapidly spreading nature of COVID-19, such tests could limit the efficiency of pandemic control and prevention. In response to this issue, artificial intelligence methods such as deep learning are promising options for automatic diagnosis because they have achieved state-of-the-art performance in the analysis of visual information and a wide range of medical images. This paper reviews and critically assesses the preprint and published reports between March and May 2020 for the diagnosis of COVID-19 via CXR images using convolutional neural networks and other deep learning architectures. Despite the encouraging results, there is an urgent need for public, comprehensive, and diverse datasets. Further investigations in terms of explainable and justifiable decisions are also required for more robust, transparent, and accurate predictions.
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Affiliation(s)
- Hanan S. Alghamdi
- Information Systems DepartmentFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia
| | - Ghada Amoudi
- Information Systems DepartmentFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia
| | - Salma Elhag
- Information Systems DepartmentFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia
| | - Kawther Saeedi
- Information Systems DepartmentFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia
| | - Jomanah Nasser
- Faculty of MedicineKing Abdulaziz UniversityJeddah80215Saudi Arabia
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KC K, Yin Z, Wu M, Wu Z. Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images. SIGNAL, IMAGE AND VIDEO PROCESSING 2021; 15:959-966. [PMID: 33432267 PMCID: PMC7788389 DOI: 10.1007/s11760-020-01820-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 09/01/2020] [Accepted: 11/12/2020] [Indexed: 05/02/2023]
Abstract
The COVID-19, novel coronavirus or SARS-Cov-2, has claimed hundreds of thousands of lives and affected millions of people all around the world with the number of deaths and infections growing exponentially. Deep convolutional neural network (DCNN) has been a huge milestone for image classification task including medical images. Transfer learning of state-of-the-art models have proven to be an efficient method of overcoming deficient data problem. In this paper, a thorough evaluation of eight pre-trained models is presented. Training, validating, and testing of these models were performed on chest X-ray (CXR) images belonging to five distinct classes, containing a total of 760 images. Fine-tuned models, pre-trained in ImageNet dataset, were computationally efficient and accurate. Fine-tuned DenseNet121 achieved a test accuracy of 98.69% and macro f1-score of 0.99 for four classes classification containing healthy, bacterial pneumonia, COVID-19, and viral pneumonia, and fine-tuned models achieved higher test accuracy for three-class classification containing healthy, COVID-19, and SARS images. The experimental results show that only 62% of total parameters were retrained to achieve such accuracy.
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Affiliation(s)
- Kamal KC
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, 150001 China
| | - Zhendong Yin
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, 150001 China
| | - Mingyang Wu
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, 150001 China
| | - Zhilu Wu
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, 150001 China
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