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Zhu K, Shen Z, Wang M, Jiang L, Zhang Y, Yang T, Zhang H, Zhang M. Visual Knowledge Domain of Artificial Intelligence in Computed Tomography: A Review Based on Bibliometric Analysis. J Comput Assist Tomogr 2024; 48:652-662. [PMID: 38271538 DOI: 10.1097/rct.0000000000001585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
ABSTRACT Artificial intelligence (AI)-assisted medical imaging technology is a new research area of great interest that has developed rapidly over the last decade. However, there has been no bibliometric analysis of published studies in this field. The present review focuses on AI-related studies on computed tomography imaging in the Web of Science database and uses CiteSpace and VOSviewer to generate a knowledge map and conduct the basic information analysis, co-word analysis, and co-citation analysis. A total of 7265 documents were included and the number of documents published had an overall upward trend. Scholars from the United States and China have made outstanding achievements, and there is a general lack of extensive cooperation in this field. In recent years, the research areas of great interest and difficulty have been the optimization and upgrading of algorithms, and the application of theoretical models to practical clinical applications. This review will help researchers understand the developments, research areas of great interest, and research frontiers in this field and provide reference and guidance for future studies.
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Kumari J, Kumar E, Kumar D. A Structured Analysis to study the Role of Machine Learning and Deep Learning in The Healthcare Sector with Big Data Analytics. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:1-29. [PMID: 37359744 PMCID: PMC10064607 DOI: 10.1007/s11831-023-09915-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 03/13/2023] [Indexed: 06/28/2023]
Abstract
Machine and deep learning are used worldwide. Machine Learning (ML) and Deep Learning (DL) are playing an increasingly important role in the healthcare sector, particularly when combined with big data analytics. Some of the ways that ML and DL are being used in healthcare include Predictive Analytics, Medical Image Analysis, Drug Discovery, Personalized Medicine, and Electronic Health Records (EHR) Analysis. It has become one of the advanced and popular tool for computer science domain.' The advancement of ML and DL for various fields has opened new avenues for research and development. It could revolutionize prediction and decision-making capabilities. Due to increased awareness about the ML and DL in the healthcare, it has become one of the vital approaches for the sector. High-volume of unstructured, and complex medical imaging data from health monitoring devices, gadgets, sensors, etc. Is the biggest trouble for healthcare sector. The current study uses analysis to examine research trends in adoption of machine learning and deep learning approaches in the healthcare sector. The WoS database for SCI/SCI-E/ESCI journals are used as the datasets for the comprehensive analysis. Apart from these various search strategy are utilised for the requisite scientific analysis of the extracted research documents. Bibliometrics R statistical analysis is performed for year-wise, nation-wise, affiliation-wise, research area, sources, documents, and author based analysis. VOS viewer software is used to create author, source, country, institution, global cooperation, citation, co-citation, and trending term co-occurrence networks. ML and DL, combined with big data analytics, have the potential to revolutionize healthcare by improving patient outcomes, reducing costs, and accelerating the development of new treatments, so the current study will help academics, researchers, decision-makers, and healthcare professionals understand and direct research.
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Affiliation(s)
- Juli Kumari
- Indira Gandhi Delhi Technical University for Women (IGDTUW), New Church Rd, Kashmere Gate, Delhi, James Church, New Delhi, 110006 India
| | - Ela Kumar
- Indira Gandhi Delhi Technical University for Women (IGDTUW), New Church Rd, Kashmere Gate, Delhi, James Church, New Delhi, 110006 India
| | - Deepak Kumar
- Center of Excellence in Weather & Climate Analytics, Atmospheric Sciences Research Center (ASRC), University at Albany (UAlbany), State University of New York (SUNY), Albany, New York 12226 USA
- Amity Institute of Geoinformatics & Remote Sensing (AIGIRS), Amity University Uttar Pradesh (AUUP), Sector-125, Gautam Buddha Nagar, Noida, Uttar Pradesh 201313 India
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Bhakar S, Sinwar D, Pradhan N, Dhaka VS, Cherrez-Ojeda I, Parveen A, Hassan MU. Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains. Diagnostics (Basel) 2023; 13:diagnostics13071212. [PMID: 37046431 PMCID: PMC10093052 DOI: 10.3390/diagnostics13071212] [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: 01/29/2023] [Revised: 03/06/2023] [Accepted: 03/08/2023] [Indexed: 04/14/2023] Open
Abstract
Disease severity identification using computational intelligence-based approaches is gaining popularity nowadays. Artificial intelligence and deep-learning-assisted approaches are proving to be significant in the rapid and accurate diagnosis of several diseases. In addition to disease identification, these approaches have the potential to identify the severity of a disease. The problem of disease severity identification can be considered multi-class classification, where the class labels are the severity levels of the disease. Plenty of computational intelligence-based solutions have been presented by researchers for severity identification. This paper presents a comprehensive review of recent approaches for identifying disease severity levels using computational intelligence-based approaches. We followed the PRISMA guidelines and compiled several works related to the severity identification of multidisciplinary diseases of the last decade from well-known publishers, such as MDPI, Springer, IEEE, Elsevier, etc. This article is devoted toward the severity identification of two main diseases, viz. Parkinson's Disease and Diabetic Retinopathy. However, severity identification of a few other diseases, such as COVID-19, autonomic nervous system dysfunction, tuberculosis, sepsis, sleep apnea, psychosis, traumatic brain injury, breast cancer, knee osteoarthritis, and Alzheimer's disease, was also briefly covered. Each work has been carefully examined against its methodology, dataset used, and the type of disease on several performance metrics, accuracy, specificity, etc. In addition to this, we also presented a few public repositories that can be utilized to conduct research on disease severity identification. We hope that this review not only acts as a compendium but also provides insights to the researchers working on disease severity identification using computational intelligence-based approaches.
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Affiliation(s)
- Suman Bhakar
- Department of Computer and Communication Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur 303007, Rajasthan, India
| | - Deepak Sinwar
- Department of Computer and Communication Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur 303007, Rajasthan, India
| | - Nitesh Pradhan
- Department of Computer Science and Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur 303007, Rajasthan, India
| | - Vijaypal Singh Dhaka
- Department of Computer and Communication Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur 303007, Rajasthan, India
| | - Ivan Cherrez-Ojeda
- Allergy and Pulmonology, Espíritu Santo University, Samborondón 0901-952, Ecuador
| | - Amna Parveen
- College of Pharmacy, Gachon University, Medical Campus, No. 191, Hambakmoero, Yeonsu-gu, Incheon 21936, Republic of Korea
| | - Muhammad Umair Hassan
- Department of ICT and Natural Sciences, Norwegian University of Science and Technology (NTNU), 6009 Ålesund, Norway
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Dwivedy V, Shukla HD, Roy PK. LMNet: Lightweight multi-scale convolutional neural network architecture for COVID-19 detection in IoMT environment. COMPUTERS & ELECTRICAL ENGINEERING : AN INTERNATIONAL JOURNAL 2022; 103:108325. [PMID: 35990557 PMCID: PMC9376345 DOI: 10.1016/j.compeleceng.2022.108325] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 08/07/2022] [Accepted: 08/10/2022] [Indexed: 06/15/2023]
Abstract
The COVID-19 disease, initially known as SARS-CoV-2, was first reported in early December 2019 and has caused immense damage to humans globally. The most widely used clinical screening method for COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR). RT-PCR uses respiratory samples for testing, because of which, this manual technique becomes complicated, laborious and time-consuming. Even though it has a low sensitivity, it carries a considerable risk for the testing medical staff. Hence, there is a need for an automated diagnosis system that can provide quick and efficient diagnosis results. This research proposed a multi-scale lightweight CNN (LMNet) architecture for COVID-19 detection. The proposed model is computationally less expensive than previously available models and requires less memory space. The performance of the proposed LMNet model ensemble with DenseNet169 and MobileNetV2 is higher than the other state-of-the-art models. The ensemble model can be integrated at the backend of the smart devices; hence it is useful for the Internet of Medical Things (IoMT) environment.
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Affiliation(s)
- Vishwajeet Dwivedy
- Department of Computer Science and Engineering, Indian Institute of Information Technology (IIIT) Surat, Gujarat, India
| | - Harsh Deep Shukla
- Department of Computer Science and Engineering, Indian Institute of Information Technology (IIIT) Surat, Gujarat, India
| | - Pradeep Kumar Roy
- Department of Computer Science and Engineering, Indian Institute of Information Technology (IIIT) Surat, Gujarat, India
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Muzoğlu N, Halefoğlu AM, Avci MO, Kaya Karaaslan M, Yarman BSB. Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods. EXPERT SYSTEMS 2022; 40:e13141. [PMID: 36245832 PMCID: PMC9537791 DOI: 10.1111/exsy.13141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 07/25/2022] [Accepted: 09/05/2022] [Indexed: 06/16/2023]
Abstract
Since the first case of COVID-19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID-19 and COVID-19 findings from computed tomography images using pre-trained models using two different datasets. COVID-19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy-paving pattern, ground-glass opacity, ground-glass opacity and consolidation, ground-glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre-trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine-Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient-weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID-19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID-19 in the lungs was an innovative and successful approach.
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Affiliation(s)
- Nedim Muzoğlu
- Department of Biomedical Engineering, Faculty of EngineeringIstanbul University‐CerrahpasaIstanbulTurkey
| | - Ahmet Mesrur Halefoğlu
- Department of RadiologySisli Hamidiye Etfal Training and Research Hospital, Health Sciences UniversityIstanbulTurkey
| | - Muhammed Onur Avci
- Department of Biomedical Engineering, Faculty of EngineeringIstanbul University‐CerrahpasaIstanbulTurkey
| | - Melike Kaya Karaaslan
- Department of Biomedical SciencesFaculty of Engineering, Kocaeli UniversityKocaeliTurkey
| | - Bekir Sıddık Binboğa Yarman
- Department of Electrical‐Electronics Engineering, Faculty of EngineeringIstanbul University‐CerrahpasaIstanbulTurkey
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Rasheed J, Shubair RM. Screening Lung Diseases Using Cascaded Feature Generation and Selection Strategies. Healthcare (Basel) 2022; 10:healthcare10071313. [PMID: 35885839 PMCID: PMC9317294 DOI: 10.3390/healthcare10071313] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/13/2022] [Accepted: 07/13/2022] [Indexed: 12/15/2022] Open
Abstract
The global pandemic COVID-19 is still a cause of a health emergency in several parts of the world. Apart from standard testing techniques to identify positive cases, auxiliary tools based on artificial intelligence can help with the identification and containment of the disease. The need for the development of alternative smart diagnostic tools to combat the COVID-19 pandemic has become more urgent. In this study, a smart auxiliary framework based on machine learning (ML) is proposed; it can help medical practitioners in the identification of COVID-19-affected patients, among others with pneumonia and healthy individuals, and can help in monitoring the status of COVID-19 cases using X-ray images. We investigated the application of transfer-learning (TL) networks and various feature-selection techniques for improving the classification accuracy of ML classifiers. Three different TL networks were tested to generate relevant features from images; these TL networks include AlexNet, ResNet101, and SqueezeNet. The generated relevant features were further refined by applying feature-selection methods that include iterative neighborhood component analysis (iNCA), iterative chi-square (iChi2), and iterative maximum relevance–minimum redundancy (iMRMR). Finally, classification was performed using convolutional neural network (CNN), linear discriminant analysis (LDA), and support vector machine (SVM) classifiers. Moreover, the study exploited stationary wavelet (SW) transform to handle the overfitting problem by decomposing each image in the training set up to three levels. Furthermore, it enhanced the dataset, using various operations as data-augmentation techniques, including random rotation, translation, and shear operations. The analysis revealed that the combination of AlexNet, ResNet101, SqueezeNet, iChi2, and SVM was very effective in the classification of X-ray images, producing a classification accuracy of 99.2%. Similarly, AlexNet, ResNet101, and SqueezeNet, along with iChi2 and the proposed CNN network, yielded 99.0% accuracy. The results showed that the cascaded feature generator and selection strategies significantly affected the performance accuracy of the classifier.
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Affiliation(s)
- Jawad Rasheed
- Department of Software Engineering, Nisantasi University, Istanbul 34398, Turkey
- Correspondence:
| | - Raed M. Shubair
- Department of Electrical and Computer Engineering, New York University (NYU), Abu Dhabi 129188, United Arab Emirates;
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Roy PK, Kumar A. Early prediction of COVID-19 using ensemble of transfer learning. COMPUTERS & ELECTRICAL ENGINEERING : AN INTERNATIONAL JOURNAL 2022; 101:108018. [PMID: 35502295 PMCID: PMC9046104 DOI: 10.1016/j.compeleceng.2022.108018] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 04/12/2022] [Accepted: 04/15/2022] [Indexed: 06/14/2023]
Abstract
In the wake of the COVID-19 outbreak, automated disease detection has become a crucial part of medical science given the infectious nature of the coronavirus. This research aims to introduce a deep ensemble framework of transfer learning models for early prediction of COVID-19 from the respective chest X-ray images of the patients. The dataset used in this research was taken from the Kaggle repository having two classes-COVID-19 Positive and COVID-19 Negative. The proposed model achieved high accuracy on the test sample with minimum false positive prediction. It can assist doctors and technicians with early detection of COVID-19 infection. The patient's health can further be monitored remotely with the help of connected devices with the Internet, which may be termed as the Internet of Medical Things (IoMT). The proposed IoMT-based solution for the automatic detection of COVID-19 can be a significant step toward fighting the pandemic.
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Affiliation(s)
- Pradeep Kumar Roy
- Department of Computer Science and Engineering, Indian Institute of Information Technology, Surat, Gujarat, India
| | - Abhinav Kumar
- Department of Computer Science and Engineering, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
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San-Cristobal R, Martín-Hernández R, Ramos-Lopez O, Martinez-Urbistondo D, Micó V, Colmenarejo G, Villares Fernandez P, Daimiel L, Martínez JA. Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort. J Clin Med 2022; 11:jcm11123327. [PMID: 35743398 PMCID: PMC9224935 DOI: 10.3390/jcm11123327] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 06/02/2022] [Accepted: 06/07/2022] [Indexed: 01/27/2023] Open
Abstract
The use of routine laboratory biomarkers plays a key role in decision making in the clinical practice of COVID-19, allowing the development of clinical screening tools for personalized treatments. This study performed a short-term longitudinal cluster from patients with COVID-19 based on biochemical measurements for the first 72 h after hospitalization. Clinical and biochemical variables from 1039 confirmed COVID-19 patients framed on the “COVID Data Save Lives” were grouped in 24-h blocks to perform a longitudinal k-means clustering algorithm to the trajectories. The final solution of the three clusters showed a strong association with different clinical severity outcomes (OR for death: Cluster A reference, Cluster B 12.83 CI: 6.11−30.54, and Cluster C 14.29 CI: 6.66−34.43; OR for ventilation: Cluster-B 2.22 CI: 1.64−3.01, and Cluster-C 1.71 CI: 1.08−2.76), improving the AUC of the models in terms of age, sex, oxygen concentration, and the Charlson Comorbidities Index (0.810 vs. 0.871 with p < 0.001 and 0.749 vs. 0.807 with p < 0.001, respectively). Patient diagnoses and prognoses remarkably diverged between the three clusters obtained, evidencing that data-driven technologies devised for the screening, analysis, prediction, and tracking of patients play a key role in the application of individualized management of the COVID-19 pandemics.
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Affiliation(s)
- Rodrigo San-Cristobal
- Precision Nutrition and Cardiometabolic Health Researh Program, Institute on Food and Health Sciences (Institute IMDEA Food), 28049 Madrid, Spain; (V.M.); (J.A.M.)
- Correspondence:
| | - Roberto Martín-Hernández
- Biostatistics & Bioinformatics Unit, Madrid Institute for Advanced Studies (IMDEA) Food, CEI UAM + CSIS, 28049 Madrid, Spain; (R.M.-H.); (G.C.)
| | - Omar Ramos-Lopez
- Medicine and Psychology School, Autonomous University of Baja California, Tijuana 22390, Baja California, Mexico;
| | - Diego Martinez-Urbistondo
- Internal Medicine Department, Hospital Universitario HM Sanchinarro, 28050 Madrid, Spain; (D.M.-U.); (P.V.F.)
| | - Víctor Micó
- Precision Nutrition and Cardiometabolic Health Researh Program, Institute on Food and Health Sciences (Institute IMDEA Food), 28049 Madrid, Spain; (V.M.); (J.A.M.)
| | - Gonzalo Colmenarejo
- Biostatistics & Bioinformatics Unit, Madrid Institute for Advanced Studies (IMDEA) Food, CEI UAM + CSIS, 28049 Madrid, Spain; (R.M.-H.); (G.C.)
| | - Paula Villares Fernandez
- Internal Medicine Department, Hospital Universitario HM Sanchinarro, 28050 Madrid, Spain; (D.M.-U.); (P.V.F.)
| | - Lidia Daimiel
- Nutritional Control of the Epigenome Group, IMDEA Food Institute, CEI UAM + CSIC, 28049 Madrid, Spain;
| | - Jose Alfredo Martínez
- Precision Nutrition and Cardiometabolic Health Researh Program, Institute on Food and Health Sciences (Institute IMDEA Food), 28049 Madrid, Spain; (V.M.); (J.A.M.)
- CIBERobn Physiopathology of Obesity and Nutrition, Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
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Chandrasekar KS. Exploring the Deep-Learning Techniques in Detecting the Presence of Coronavirus in the Chest X-Ray Images: A Comprehensive Review. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 29:5381-5395. [PMID: 35645554 PMCID: PMC9126247 DOI: 10.1007/s11831-022-09768-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 05/07/2022] [Indexed: 06/15/2023]
Abstract
The deadly coronavirus (COVID-19) is one of the dangerous diseases affecting the entire world and is fastly spreading disease. This spread can be reduced by detecting and quarantining the patients at an earlier stage. The most common diagnostic tool for detecting the coronavirus is the Reverse transcription-polymerase chain reaction (RT-PCR) test which is time-consuming and also needs more equipment and manpower. Furthermore, many countries had a deficit of RTPCR kits. This is why it is exceptionally very crucial to develop artificial intelligence (AI) techniques to detect the outbreak of coronavirus. This motivated many researchers to involve deep-learning methods using X-ray images for more decisive analysis. Thus, this paper outlines many papers that used traditional and pre-trained deep learning methods that are newly developed to reduce the spread of COVID-19 disease. Specifically, advanced deep learning methods play a critical role in extracting the features from the chest X-ray images. These features are then used to classify whether the patient is affected with coronavirus or not. Besides, this paper shows that deep learning techniques have probable applications in the medical field.
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Negrini D, Danese E, Henry BM, Lippi G, Montagnana M. Artificial intelligence at the time of COVID-19: who does the lion's share? Clin Chem Lab Med 2022; 60:1881-1886. [PMID: 35470639 DOI: 10.1515/cclm-2022-0306] [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: 03/29/2022] [Accepted: 04/13/2022] [Indexed: 01/06/2023]
Abstract
OBJECTIVES The development and use of artificial intelligence (AI) methodologies, especially machine learning (ML) and deep learning (DL), have been considerably fostered during the ongoing coronavirus disease 2019 (COVID-19) pandemic. Several models and algorithms have been developed and applied for both identifying COVID-19 cases and for assessing and predicting the risk of developing unfavourable outcomes. Our aim was to summarize how AI is being currently applied to COVID-19. METHODS We conducted a PubMed search using as query MeSH major terms "Artificial Intelligence" AND "COVID-19", searching for articles published until December 31, 2021, which explored the possible role of AI in COVID-19. The dataset origin (internal dataset or public datasets available online) and data used for training and testing the proposed ML/DL model(s) were retrieved. RESULTS Our analysis finally identified 292 articles in PubMed. These studies displayed large heterogeneity in terms of imaging test, laboratory parameters and clinical-demographic data included. Most models were based on imaging data, in particular CT scans or chest X-rays images. C-Reactive protein, leukocyte count, creatinine, lactate dehydrogenase, lymphocytes and platelets counts were found to be the laboratory biomarkers most frequently included in COVID-19 related AI models. CONCLUSIONS The lion's share of AI applied to COVID-19 seems to be played by diagnostic imaging. However, AI in laboratory medicine is also gaining momentum, especially with digital tools characterized by low cost and widespread applicability.
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Affiliation(s)
- Davide Negrini
- Section of Clinical Biochemistry and School of Medicine, University Hospital of Verona, Verona, Italy
| | - Elisa Danese
- Section of Clinical Biochemistry and School of Medicine, University Hospital of Verona, Verona, Italy
| | - Brandon M Henry
- Clinical Laboratory, Division of Nephrology and Hypertension, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Giuseppe Lippi
- Section of Clinical Biochemistry and School of Medicine, University Hospital of Verona, Verona, Italy
| | - Martina Montagnana
- Section of Clinical Biochemistry and School of Medicine, University Hospital of Verona, Verona, Italy
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