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Alathari MJA, Mashhadany YA, Bakar AAA, Mokhtar MHH, Bin Zan MSD, Arsad N. COVID-19 IgG antibodies detection based on CNN-BiLSTM algorithm combined with fiber-optic dataset. J Virol Methods 2024; 330:115011. [PMID: 39154936 DOI: 10.1016/j.jviromet.2024.115011] [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: 05/17/2024] [Revised: 07/14/2024] [Accepted: 08/15/2024] [Indexed: 08/20/2024]
Abstract
The urgent need for efficient and accurate automated screening tools for COVID-19 detection has led to research efforts exploring various approaches. In this study, we present pioneering research on COVID-19 detection using a hybrid model that combines convolutional neural networks (CNN) with a bi-directional long short-term memory (Bi-LSTM) network, in conjunction with fiber optic data for SARS-CoV-2 Immunoglobulin G (IgG) antibodies. Our research introduces a comprehensive data preprocessing pipeline and evaluates the performance of four different deep learning (DL) algorithms: CNN, CNN-RNN, BiLSTM, and CNN-BiLSTM, in classifying samples as positive or negative for the COVID-19 virus. Among these, the CNN-BiLSTM classifier demonstrated superior performance on the training datasets, achieving an accuracy of 89 %, a recall of 88 %, a precision of 90 %, an F1-score of 89 %, a specificity of 90 %, a geometric mean (G-mean) of 89 %, and a receiver operating characteristic (ROC) of 96 %. In addition, the achieved classification results were compared with those reported in the literature. The findings indicate that the proposed model has promising potential for classifying COVID-19 and could serve as a valuable tool for healthcare professionals. The use of IgG antibodies to detect the virus enhances the specificity and accuracy of the diagnostic tool.
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Affiliation(s)
- Mohammed Jawad Ahmed Alathari
- UKM - Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.
| | - Yousif Al Mashhadany
- Department of Electrical Engineering, College of Engineering, Anbar University, Anbar 00964, Iraq.
| | - Ahmad Ashrif A Bakar
- UKM - Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.
| | - Mohd Hadri Hafiz Mokhtar
- UKM - Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.
| | - Mohd Saiful Dzulkefly Bin Zan
- UKM - Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.
| | - Norhana Arsad
- UKM - Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.
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2
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Wang Q, Lu X, Jia R, Yan X, Wang J, Zhao L, Zhong R, Sun G. Recent advances in chemometric modelling of inhibitors against SARS-CoV-2. Heliyon 2024; 10:e24209. [PMID: 38293468 PMCID: PMC10826659 DOI: 10.1016/j.heliyon.2024.e24209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 01/02/2024] [Accepted: 01/04/2024] [Indexed: 02/01/2024] Open
Abstract
The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused great harm to all countries worldwide. This disease can be prevented by vaccination and managed using various treatment methods, including injections, oral medications, or aerosol therapies. However, the selection of suitable compounds for the research and development of anti-SARS-CoV-2 drugs is a daunting task because of the vast databases of available compounds. The traditional process of drug research and development is time-consuming, labour-intensive, and costly. The application of chemometrics can significantly expedite drug R&D. This is particularly necessary and important for drug development against pandemic public emergency diseases, such as COVID-19. Through various chemometric techniques, such as quantitative structure-activity relationship (QSAR) modelling, molecular docking, and molecular dynamics (MD) simulations, compounds with inhibitory activity against SARS-CoV-2 can be quickly screened, allowing researchers to focus on the few prioritised candidates. In addition, the ADMET properties of the screened candidate compounds should be further explored to promote the successful discovery of anti-SARS-CoV-2 drugs. In this case, considerable time and economic costs can be saved while minimising the need for extensive animal experiments, in line with the 3R principles. This paper focuses on recent advances in chemometric modelling studies of COVID-19-related inhibitors, highlights current limitations, and outlines potential future directions for development.
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Affiliation(s)
- Qianqian Wang
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
| | - Xinyi Lu
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
| | - Runqing Jia
- Department of Biology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
| | - Xinlong Yan
- Department of Biology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
| | - Jianhua Wang
- Beijing Municipal Key Laboratory of Child Development and Nutriomics, Translational Medicine Laboratory, Capital Institute of Pediatrics, Beijing 100124, PR China
| | - Lijiao Zhao
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
| | - Rugang Zhong
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
| | - Guohui Sun
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
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Bekbolatova M, Mayer J, Ong CW, Toma M. Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives. Healthcare (Basel) 2024; 12:125. [PMID: 38255014 PMCID: PMC10815906 DOI: 10.3390/healthcare12020125] [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: 10/11/2023] [Revised: 12/27/2023] [Accepted: 01/02/2024] [Indexed: 01/24/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance.
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Affiliation(s)
- Molly Bekbolatova
- Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA; (M.B.); (J.M.)
| | - Jonathan Mayer
- Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA; (M.B.); (J.M.)
| | - Chi Wei Ong
- School of Chemistry, Chemical Engineering, and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459, Singapore
| | - Milan Toma
- Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA; (M.B.); (J.M.)
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4
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Dellacasa C, Ortali M, Rossi E, Abu Attieh H, Osmo T, Puskaric M, Rinaldi E, Prasser F, Stellmach C, Cataudella S, Agarwal B, Mata Naranjo J, Scipione G. An innovative technological infrastructure for managing SARS-CoV-2 data across different cohorts in compliance with General Data Protection Regulation. Digit Health 2024; 10:20552076241248922. [PMID: 38766364 PMCID: PMC11100396 DOI: 10.1177/20552076241248922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 04/04/2024] [Indexed: 05/22/2024] Open
Abstract
Background The ORCHESTRA project, funded by the European Commission, aims to create a pan-European cohort built on existing and new large-scale population cohorts to help rapidly advance the knowledge related to the prevention of the SARS-CoV-2 infection and the management of COVID-19 and its long-term sequelae. The integration and analysis of the very heterogeneous health data pose the challenge of building an innovative technological infrastructure as the foundation of a dedicated framework for data management that should address the regulatory requirements such as the General Data Protection Regulation (GDPR). Methods The three participating Supercomputing European Centres (CINECA - Italy, CINES - France and HLRS - Germany) designed and deployed a dedicated infrastructure to fulfil the functional requirements for data management to ensure sensitive biomedical data confidentiality/privacy, integrity, and security. Besides the technological issues, many methodological aspects have been considered: Berlin Institute of Health (BIH), Charité provided its expertise both for data protection, information security, and data harmonisation/standardisation. Results The resulting infrastructure is based on a multi-layer approach that integrates several security measures to ensure data protection. A centralised Data Collection Platform has been established in the Italian National Hub while, for the use cases in which data sharing is not possible due to privacy restrictions, a distributed approach for Federated Analysis has been considered. A Data Portal is available as a centralised point of access for non-sensitive data and results, according to findability, accessibility, interoperability, and reusability (FAIR) data principles. This technological infrastructure has been used to support significative data exchange between population cohorts and to publish important scientific results related to SARS-CoV-2. Conclusions Considering the increasing demand for data usage in accordance with the requirements of the GDPR regulations, the experience gained in the project and the infrastructure released for the ORCHESTRA project can act as a model to manage future public health threats. Other projects could benefit from the results achieved by ORCHESTRA by building upon the available standardisation of variables, design of the architecture, and process used for GDPR compliance.
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Affiliation(s)
- Chiara Dellacasa
- HPC Department, CINECA Consorzio Interuniversitario,
Bologna, Italy
| | - Maurizio Ortali
- HPC Department, CINECA Consorzio Interuniversitario,
Bologna, Italy
| | - Elisa Rossi
- HPC Department, CINECA Consorzio Interuniversitario,
Bologna, Italy
| | - Hammam Abu Attieh
- Berlin Institute of Health (BIH), Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Thomas Osmo
- Département Archivage et Services aux Données (DASD), Centre Informatique National de l'Enseignement Supérieur (CINES), Montpellier, France
| | - Miroslav Puskaric
- High Performance Computing Center Stuttgart (HLRS), University of Stuttgart, Stuttgart, Germany
| | - Eugenia Rinaldi
- Berlin Institute of Health (BIH), Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Fabian Prasser
- Berlin Institute of Health (BIH), Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Caroline Stellmach
- Berlin Institute of Health (BIH), Charité – Universitätsmedizin Berlin, Berlin, Germany
| | | | - Bhaskar Agarwal
- HPC Department, CINECA Consorzio Interuniversitario,
Bologna, Italy
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5
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Bai G, Sun C, Guo Z, Wang Y, Zeng X, Su Y, Zhao Q, Ma B. Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects. Semin Cancer Biol 2023; 95:13-24. [PMID: 37355214 DOI: 10.1016/j.semcancer.2023.06.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 06/09/2023] [Accepted: 06/18/2023] [Indexed: 06/26/2023]
Abstract
Therapeutic antibodies are the largest class of biotherapeutics and have been successful in treating human diseases. However, the design and discovery of antibody drugs remains challenging and time-consuming. Recently, artificial intelligence technology has had an incredible impact on antibody design and discovery, resulting in significant advances in antibody discovery, optimization, and developability. This review summarizes major machine learning (ML) methods and their applications for computational predictors of antibody structure and antigen interface/interaction, as well as the evaluation of antibody developability. Additionally, this review addresses the current status of ML-based therapeutic antibodies under preclinical and clinical phases. While many challenges remain, ML may offer a new therapeutic option for the future direction of fully computational antibody design.
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Affiliation(s)
- Ganggang Bai
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Chuance Sun
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ziang Guo
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao Special Administrative Region of China
| | - Yangjing Wang
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xincheng Zeng
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yuhong Su
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qi Zhao
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao Special Administrative Region of China; MoE Frontiers Science Center for Precision Oncology, University of Macau, Taipa, Macao Special Administrative Region of China.
| | - Buyong Ma
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; Shanghai Digiwiser BioTechnolgy, Limited, Shanghai 201203, China.
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Sfayyih AH, Sulaiman N, Sabry AH. A review on lung disease recognition by acoustic signal analysis with deep learning networks. JOURNAL OF BIG DATA 2023; 10:101. [PMID: 37333945 PMCID: PMC10259357 DOI: 10.1186/s40537-023-00762-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 05/08/2023] [Indexed: 06/20/2023]
Abstract
Recently, assistive explanations for difficulties in the health check area have been made viable thanks in considerable portion to technologies like deep learning and machine learning. Using auditory analysis and medical imaging, they also increase the predictive accuracy for prompt and early disease detection. Medical professionals are thankful for such technological support since it helps them manage further patients because of the shortage of skilled human resources. In addition to serious illnesses like lung cancer and respiratory diseases, the plurality of breathing difficulties is gradually rising and endangering society. Because early prediction and immediate treatment are crucial for respiratory disorders, chest X-rays and respiratory sound audio are proving to be quite helpful together. Compared to related review studies on lung disease classification/detection using deep learning algorithms, only two review studies based on signal analysis for lung disease diagnosis have been conducted in 2011 and 2018. This work provides a review of lung disease recognition with acoustic signal analysis with deep learning networks. We anticipate that physicians and researchers working with sound-signal-based machine learning will find this material beneficial.
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Affiliation(s)
- Alyaa Hamel Sfayyih
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Malaysia
| | - Nasri Sulaiman
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Malaysia
| | - Ahmad H. Sabry
- Department of Computer Engineering, Al-Nahrain University, Al Jadriyah Bridge, 64074 Baghdad, Iraq
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7
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AlMohimeed A, Saleh H, El-Rashidy N, Saad RMA, El-Sappagh S, Mostafa S. Diagnosis of COVID-19 Using Chest X-ray Images and Disease Symptoms Based on Stacking Ensemble Deep Learning. Diagnostics (Basel) 2023; 13:diagnostics13111968. [PMID: 37296820 DOI: 10.3390/diagnostics13111968] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 05/17/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
The COVID-19 virus is one of the most devastating illnesses humanity has ever faced. COVID-19 is an infection that is hard to diagnose until it has caused lung damage or blood clots. As a result, it is one of the most insidious diseases due to the lack of knowledge of its symptoms. Artificial intelligence (AI) technologies are being investigated for the early detection of COVID-19 using symptoms and chest X-ray images. Therefore, this work proposes stacking ensemble models using two types of COVID-19 datasets, symptoms and chest X-ray scans, to identify COVID-19. The first proposed model is a stacking ensemble model that is merged from the outputs of pre-trained models in the stacking: multi-layer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). Stacking trains and evaluates the meta-learner as a support vector machine (SVM) to predict the final decision. Two datasets of COVID-19 symptoms are used to compare the first proposed model with MLP, RNN, LSTM, and GRU models. The second proposed model is a stacking ensemble model that is merged from the outputs of pre-trained DL models in the stacking: VGG16, InceptionV3, Resnet50, and DenseNet121; it uses stacking to train and evaluate the meta-learner (SVM) to identify the final prediction. Two datasets of COVID-19 chest X-ray images are used to compare the second proposed model with other DL models. The result has shown that the proposed models achieve the highest performance compared to other models for each dataset.
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Affiliation(s)
- Abdulaziz AlMohimeed
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia
| | - Hager Saleh
- Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, Egypt
| | - Nora El-Rashidy
- Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh 13518, Egypt
| | - Redhwan M A Saad
- College of Informatics, Midocean University, Moroni 8722, Comoros
| | - Shaker El-Sappagh
- Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
| | - Sherif Mostafa
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia
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Alam MM, Alam MM, Mirza H, Sultana N, Sultana N, Pasha AA, Khan AI, Zafar A, Ahmad MT. A Novel COVID-19 Diagnostic System Using Biosensor Incorporated Artificial Intelligence Technique. Diagnostics (Basel) 2023; 13:diagnostics13111886. [PMID: 37296738 DOI: 10.3390/diagnostics13111886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 04/29/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023] Open
Abstract
COVID-19, continually developing and raising increasingly significant issues, has impacted human health and caused countless deaths. It is an infectious disease with a high incidence and mortality rate. The spread of the disease is also a significant threat to human health, especially in the developing world. This study suggests a method called shuffle shepherd optimization-based generalized deep convolutional fuzzy network (SSO-GDCFN) to diagnose the COVID-19 disease state, types, and recovered categories. The results show that the accuracy of the proposed method is as high as 99.99%; similarly, precision is 99.98%; sensitivity/recall is 100%; specificity is 95%; kappa is 0.965%; AUC is 0.88%; and MSE is less than 0.07% as well as 25 s. Moreover, the performance of the suggested method has been confirmed by comparison of the simulation results from the proposed approach with those from several traditional techniques. The experimental findings demonstrate strong performance and high accuracy for categorizing COVID-19 stages with minimal reclassifications over the conventional methods.
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Affiliation(s)
- Md Mottahir Alam
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz, Jeddah 21589, Saudi Arabia
| | - Md Moddassir Alam
- Department of Health Information Management and Technology, College of Applied Medical Sciences, University of Hafr Al-Batin, Hafr Al-Batin 39524, Saudi Arabia
| | - Hidayath Mirza
- Department of Electrical Engineering, College of Engineering, Jazan University, P.O. Box 706, Jazan 45142, Saudi Arabia
| | - Nishat Sultana
- Department of Business Administration, Applied College, Jazan University, P.O. Box 706, Jazan 45142, Saudi Arabia
| | - Nazia Sultana
- Government Medical College Siddipet, Ensanpalli, Siddipet District, Telangana 502114, India
| | - Amjad Ali Pasha
- Aerospace Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Asif Irshad Khan
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Aasim Zafar
- Department of Computer Science, Aligarh Muslim University, Aligarh 202002, India
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Bruno A, Ignesti G, Salvetti O, Moroni D, Martinelli M. Efficient Lung Ultrasound Classification. Bioengineering (Basel) 2023; 10:bioengineering10050555. [PMID: 37237625 DOI: 10.3390/bioengineering10050555] [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: 03/11/2023] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 05/28/2023] Open
Abstract
A machine learning method for classifying lung ultrasound is proposed here to provide a point of care tool for supporting a safe, fast, and accurate diagnosis that can also be useful during a pandemic such as SARS-CoV-2. Given the advantages (e.g., safety, speed, portability, cost-effectiveness) provided by the ultrasound technology over other examinations (e.g., X-ray, computer tomography, magnetic resonance imaging), our method was validated on the largest public lung ultrasound dataset. Focusing on both accuracy and efficiency, our solution is based on an efficient adaptive ensembling of two EfficientNet-b0 models reaching 100% of accuracy, which, to our knowledge, outperforms the previous state-of-the-art models by at least 5%. The complexity is restrained by adopting specific design choices: ensembling with an adaptive combination layer, ensembling performed on the deep features, and minimal ensemble using two weak models only. In this way, the number of parameters has the same order of magnitude of a single EfficientNet-b0 and the computational cost (FLOPs) is reduced at least by 20%, doubled by parallelization. Moreover, a visual analysis of the saliency maps on sample images of all the classes of the dataset reveals where an inaccurate weak model focuses its attention versus an accurate one.
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Affiliation(s)
- Antonio Bruno
- Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy
| | - Giacomo Ignesti
- Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy
| | - Ovidio Salvetti
- Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy
| | - Davide Moroni
- Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy
| | - Massimo Martinelli
- Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy
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10
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Ortiz-Barrios M, Arias-Fonseca S, Ishizaka A, Barbati M, Avendaño-Collante B, Navarro-Jiménez E. Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study. JOURNAL OF BUSINESS RESEARCH 2023; 160:113806. [PMID: 36895308 PMCID: PMC9981538 DOI: 10.1016/j.jbusres.2023.113806] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 01/18/2023] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymakers. This paper aims to use Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to support ICU bed capacity management during Covid-19. The proposed approach was validated in a Spanish hospital chain where we initially identified the predictors of ICU admission in Covid-19 patients. Second, we applied Random Forest (RF) to predict ICU admission likelihood using patient data collected in the Emergency Department (ED). Finally, we included the RF outcomes in a DES model to assist decision-makers in evaluating new ICU bed configurations responding to the patient transfer expected from downstream services. The results evidenced that the median bed waiting time declined between 32.42 and 48.03 min after intervention.
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Affiliation(s)
- Miguel Ortiz-Barrios
- Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 080002, Colombia
| | - Sebastián Arias-Fonseca
- Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 080002, Colombia
| | - Alessio Ishizaka
- NEOMA Business School, 1 rue du Maréchal Juin, Mont-Saint-Aignan 76130, France
| | - Maria Barbati
- Department of Economics, University Ca' Foscari, Cannaregio 873, Fondamenta San Giobbe, 30121 Venice, Italy
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11
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Krämer M, Ingwersen M, Teichgräber U, Güttler F. Added value of chest CT in a machine learning-based prediction model to rule out COVID-19 before inpatient admission: A retrospective university network study. Eur J Radiol 2023; 163:110827. [PMID: 37087928 PMCID: PMC10080860 DOI: 10.1016/j.ejrad.2023.110827] [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: 01/21/2023] [Revised: 03/28/2023] [Accepted: 04/04/2023] [Indexed: 04/25/2023]
Abstract
PURPOSE During the coronavirus disease 2019 (COVID-19) pandemic, hospitals still face the challenge of timely identification of infected individuals before inpatient admission. An artificial intelligence approach based on an established clinical network may improve prospective pandemic preparedness. METHOD Supervised machine learning was used to construct diagnostic models to predict COVID-19. A pooled database was retrospectively generated from 4437 participant data that were collected between January 2017 and October 2020 at 12 German centers that belong to the radiological cooperative network of the COVID-19 (RACOON) consortium. A total of 692 (15.6 %) participants were COVID-19 positive according to the reference of the reverse transcription-polymerase chain reaction test. The diagnostic models included chest CT features (model R), clinical examination and laboratory test features (model CL), or all three feature categories (model RCL). Performance outcomes included accuracy, sensitivity, specificity, negative and positive predictive value, and area under the receiver operating curve (AUC). RESULTS Performance of predictive models improved significantly by adding chest CT features to clinical evaluation and laboratory test features. Without (model CL) and with inclusion of chest CT (model RCL), sensitivity was 0.82 and 0.89 (p < 0.0001), specificity was 0.84 and 0.89 (p < 0.0001), negative predictive value was 0.96 and 0.97 (p < 0.0001), AUC was 0.92 and 0.95 (p < 0.0001), and proportion of false negative classifications was 2.6 % and 1.7 % (p < 0.0001), respectively. CONCLUSIONS Addition of chest CT features to machine learning-based predictive models improves the effectiveness in ruling out COVID-19 before inpatient admission to regular wards.
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Affiliation(s)
- Martin Krämer
- Department of Radiology, Friedrich Schiller University, Jena University Hospital, Jena, Germany.
| | - Maja Ingwersen
- Department of Radiology, Friedrich Schiller University, Jena University Hospital, Jena, Germany.
| | - Ulf Teichgräber
- Department of Radiology, Friedrich Schiller University, Jena University Hospital, Jena, Germany.
| | - Felix Güttler
- Department of Radiology, Friedrich Schiller University, Jena University Hospital, Jena, Germany.
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12
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Chen X, You G, Chen Q, Zhang X, Wang N, He X, Zhu L, Li Z, Liu C, Yao S, Ge J, Gao W, Yu H. Development and evaluation of an artificial intelligence system for children intussusception diagnosis using ultrasound images. iScience 2023; 26:106456. [PMID: 37063466 PMCID: PMC10090215 DOI: 10.1016/j.isci.2023.106456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/16/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
Accurate identification of intussusception in children is critical for timely non-surgical management. We propose an end-to-end artificial intelligence algorithm, the Children Intussusception Diagnosis Network (CIDNet) system, that utilizes ultrasound images to rapidly diagnose intussusception. 9999 ultrasound images of 4154 pediatric patients were divided into training, validation, test, and independent reader study datasets. The independent reader study cohort was used to compare the diagnostic performance of the CIDNet system to six radiologists. Performance was evaluated using, among others, balance accuracy (BACC) and area under the receiver operating characteristic curve (AUC). The CIDNet system performed the best in diagnosing intussusception with a BACC of 0.8464 and AUC of 0.9716 in the test dataset compared to other deep learning algorithms. The CIDNet system compared favorably with expert radiologists by outstanding identification performance and robustness (BACC:0.9297; AUC:0.9769). CIDNet is a stable and precise technological tool for identifying intussusception in ultrasound scans of children.
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Affiliation(s)
- Xiong Chen
- Department of Paediatric Urology, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
- Department of Paediatric Surgery, Guangzhou Institute of Paediatrics, Guangzhou Medical University, Guangzhou 510623, P. R. China
| | - Guochang You
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, P. R. China
| | - Qinchang Chen
- Department of Pediatric Cardiology, Guangdong Provincial Key Laboratory of Structural Heart Disease, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangdong Cardiovascular Institute, Guangzhou 510080, P. R. China
| | - Xiangxiang Zhang
- Department of Ultrasound, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
| | - Na Wang
- Department of Ultrasound, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
| | - Xuehua He
- Department of Ultrasound, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
| | - Liling Zhu
- Department of Ultrasound, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
| | - Zhouzhou Li
- Department of Ultrasound, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
| | - Chen Liu
- Department of Ultrasound, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
| | - Shixiang Yao
- Department of Ultrasound, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
| | - Junshuang Ge
- Clinical Data Center, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
| | - Wenjing Gao
- Clinical Data Center, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
- Corresponding author
| | - Hongkui Yu
- Department of Ultrasound, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
- Corresponding author
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13
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Vodanović M, Subašić M, Milošević D, Savić Pavičin I. Artificial Intelligence in Medicine and Dentistry. Acta Stomatol Croat 2023; 57:70-84. [PMID: 37288152 PMCID: PMC10243707 DOI: 10.15644/asc57/1/8] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/01/2023] [Indexed: 09/14/2023] Open
Abstract
INTRODUCTION Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry. OBJECTIVE Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages. CONCLUSION The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.
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Affiliation(s)
- Marin Vodanović
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
| | - Marko Subašić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Denis Milošević
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Ivana Savić Pavičin
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
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14
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Tallapragada VS, Manga NA, Kumar GP. A novel COVID diagnosis and feature extraction based on discrete wavelet model and classification using X-ray and CT images. MULTIMEDIA TOOLS AND APPLICATIONS 2023; 82:1-42. [PMID: 36712955 PMCID: PMC9859748 DOI: 10.1007/s11042-023-14367-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/22/2022] [Accepted: 01/02/2023] [Indexed: 06/18/2023]
Abstract
Recently, the Covid-19 pandemic has affected several lives of people globally, and there is a need for a massive number of screening tests to diagnose the existence of coronavirus. For the medical specialist, detecting COVID-19 cases is a difficult task. There is a need for fast, cheap and accurate diagnostic tools. The chest X-ray and the computerized tomography (CT) play a significant role in the COVID-19 diagnosis. The advancement of deep learning (DL) approaches helps to introduce a COVID diagnosis system to achieve maximum detection rate with minimum time complexity. This research proposed a discrete wavelet optimized network model for COVID-19 diagnosis and feature extraction to overcome these problems. It consists of three stages pre-processing, feature extraction and classification. The raw images are filtered in the pre-processing phase to eliminate unnecessary noises and improve the image quality using the MMG hybrid filtering technique. The next phase is feature extraction, in this stage, the features are extracted, and the dimensionality of the features is diminished with the aid of a modified discrete wavelet based Mobile Net model. The third stage is the classification here, the convolutional Aquila COVID detection network model is developed to classify normal and COVID-19 positive cases from the collected images of the COVID-CT and chest X-ray dataset. Finally, the performance of the proposed model is compared with some of the existing models in terms of accuracy, specificity, sensitivity, precision, f-score, negative predictive value (NPV) and positive predictive value (PPV), respectively. The proposed model achieves the performance of 99%, 100%, 98.5%, and 99.5% for the CT dataset, and the accomplished accuracy, specificity, sensitivity, and precision values of the proposed model for the X-ray dataset are 98%, 99%, 98% and 97% respectively. In addition, the statistical and cross validation analysis is conducted to validate the effectiveness of the proposed model.
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Affiliation(s)
| | | | - G.V. Pradeep Kumar
- Department of ECE, Chaitanya Bharathi Institute of Technology, Hyderabad, India
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15
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Irkham I, Ibrahim AU, Nwekwo CW, Al-Turjman F, Hartati YW. Current Technologies for Detection of COVID-19: Biosensors, Artificial Intelligence and Internet of Medical Things (IoMT): Review. SENSORS (BASEL, SWITZERLAND) 2022; 23:426. [PMID: 36617023 PMCID: PMC9824404 DOI: 10.3390/s23010426] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 12/14/2022] [Accepted: 12/21/2022] [Indexed: 06/17/2023]
Abstract
Despite the fact that COVID-19 is no longer a global pandemic due to development and integration of different technologies for the diagnosis and treatment of the disease, technological advancement in the field of molecular biology, electronics, computer science, artificial intelligence, Internet of Things, nanotechnology, etc. has led to the development of molecular approaches and computer aided diagnosis for the detection of COVID-19. This study provides a holistic approach on COVID-19 detection based on (1) molecular diagnosis which includes RT-PCR, antigen-antibody, and CRISPR-based biosensors and (2) computer aided detection based on AI-driven models which include deep learning and transfer learning approach. The review also provide comparison between these two emerging technologies and open research issues for the development of smart-IoMT-enabled platforms for the detection of COVID-19.
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Affiliation(s)
- Irkham Irkham
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Padjadjaran University, Bandung 40173, Indonesia
| | | | - Chidi Wilson Nwekwo
- Department of Biomedical Engineering, Near East University, Mersin 99138, Turkey
| | - Fadi Al-Turjman
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 99138, Turkey
- Artificial Intelligence Engineering Department, AI and Robotics Institute, Near East University, Mersin 99138, Turkey
| | - Yeni Wahyuni Hartati
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Padjadjaran University, Bandung 40173, Indonesia
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16
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Cui X, Chang Y, Yang C, Cong Z, Wang B, Leng Y. Development and Trends in Artificial Intelligence in Critical Care Medicine: A Bibliometric Analysis of Related Research over the Period of 2010-2021. J Pers Med 2022; 13:jpm13010050. [PMID: 36675711 PMCID: PMC9860734 DOI: 10.3390/jpm13010050] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 12/22/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND The intensive care unit is a center for massive data collection, making it the best field to embrace big data and artificial intelligence. OBJECTIVE This study aimed to provide a literature overview on the development of artificial intelligence in critical care medicine (CCM) and tried to give valuable information about further precision medicine. METHODS Relevant studies published between January 2010 and June 2021 were manually retrieved from the Science Citation Index Expanded database in Web of Science (Clarivate), using keywords. RESULTS Research related to artificial intelligence in CCM has been increasing over the years. The USA published the most articles and had the top 10 active affiliations. The top ten active journals are bioinformatics journals and are in JCR Q1. Prediction, diagnosis, and treatment strategy exploration of sepsis, pneumonia, and acute kidney injury were the most focused topics. Electronic health records (EHRs) were the most widely used data and the "-omics" data should be integrated further. CONCLUSIONS Artificial intelligence in CCM has developed over the past decade. With the introduction of constantly growing data volume and novel data types, more investigation on artificial intelligence ethics and model correctness and extrapolation should be performed for generalization.
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Affiliation(s)
- Xiao Cui
- Department of Critical Care Medicine, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, China
| | - Yundi Chang
- Department of Critical Care Medicine, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, China
| | - Cui Yang
- Department of Critical Care Medicine, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, China
| | - Zhukai Cong
- Department of Anesthesiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, China
| | - Baocheng Wang
- National Science Library, Chinese Academy of Sciences, 33 Beisihuan Xilu, Haidian District, Beijing 100090, China
- School of Economics and Management, University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, China
- Correspondence: (B.W.); (Y.L.)
| | - Yuxin Leng
- Department of Critical Care Medicine, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, China
- Correspondence: (B.W.); (Y.L.)
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17
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Yin Z, Chen L, Lyu W, Luo B. Reversible Attack based on Adversarial Perturbation and Reversible Data Hiding in YUV Colorspace. Pattern Recognit Lett 2022. [DOI: 10.1016/j.patrec.2022.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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18
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Ross G, Zhao Y, Bosman A, Geballa-Koukoula A, Zhou H, Elliott C, Nielen M, Rafferty K, Salentijn G. Data handling and ethics of emerging smartphone-based (bio)sensors – Part 1: Best practices and current implementation. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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19
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Hsu SY, Wang CY, Kao YK, Liu KY, Lin MC, Yeh LR, Wang YM, Chen CI, Kao FC. Using Deep Neural Network Approach for Multiple-Class Assessment of Digital Mammography. Healthcare (Basel) 2022; 10:healthcare10122382. [PMID: 36553906 PMCID: PMC9778490 DOI: 10.3390/healthcare10122382] [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: 10/17/2022] [Revised: 11/16/2022] [Accepted: 11/25/2022] [Indexed: 11/29/2022] Open
Abstract
According to the Health Promotion Administration in the Ministry of Health and Welfare statistics in Taiwan, over ten thousand women have breast cancer every year. Mammography is widely used to detect breast cancer. However, it is limited by the operator's technique, the cooperation of the subjects, and the subjective interpretation by the physician. It results in inconsistent identification. Therefore, this study explores the use of a deep neural network algorithm for the classification of mammography images. In the experimental design, a retrospective study was used to collect imaging data from actual clinical cases. The mammography images were collected and classified according to the breast image reporting and data-analyzing system (BI-RADS). In terms of model building, a fully convolutional dense connection network (FC-DCN) is used for the network backbone. All the images were obtained through image preprocessing, a data augmentation method, and transfer learning technology to build a mammography image classification model. The research results show the model's accuracy, sensitivity, and specificity were 86.37%, 100%, and 72.73%, respectively. Based on the FC-DCN model framework, it can effectively reduce the number of training parameters and successfully obtain a reasonable image classification model for mammography.
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Affiliation(s)
- Shih-Yen Hsu
- Department of Information Engineering, I-Shou University, Kaohsiung City 84001, Taiwan
| | - Chi-Yuan Wang
- Department of Medical Imaging and Radiological Science, I-Shou University, Kaohsiung City 82445, Taiwan
| | - Yi-Kai Kao
- Division of Colorectal Surgery, Department of Surgery, E-DA Hospital, Kaohsiung City 82445, Taiwan
| | - Kuo-Ying Liu
- Department of Radiology, E-DA Cancer Hospital, I-Shou University, Kaohsiung City 82445, Taiwan
| | - Ming-Chia Lin
- Department of Nuclear Medicine, E-DA Hospital, I-Shou University, Kaohsiung City 82445, Taiwan
| | - Li-Ren Yeh
- Department of Anesthesiology, E-DA Cancer Hospital, I-Shou University, Kaohsiung City 82445, Taiwan
- Department of Medical Imaging and Radiology, Shu-Zen College of Medicine and Management, Kaohsiung City 82144, Taiwan
| | - Yi-Ming Wang
- Department of Information Engineering, I-Shou University, Kaohsiung City 84001, Taiwan
- Department of Critical Care Medicine, E-DA Hospital, I-Shou University, Kaohsiung City 82445, Taiwan
| | - Chih-I Chen
- Division of Colon and Rectal Surgery, Department of Surgery, E-DA Hospital, Kaohsiung City 82445, Taiwan
- Division of General Medicine Surgery, Department of Surgery, E-DA Hospital, Kaohsiung City 82445, Taiwan
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung City 82445, Taiwan
- The School of Chinese Medicine for Post Baccalaureate, I-Shou University, Kaohsiung City 82445, Taiwan
- Correspondence: (C.-I.C.); (F.-C.K.)
| | - Feng-Chen Kao
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung City 82445, Taiwan
- Department of Orthopedics, E-DA Hospital, Kaohsiung City 82445, Taiwan
- Department of Orthopedics, Dachang Hospital, Kaohsiung City 82445, Taiwan
- Correspondence: (C.-I.C.); (F.-C.K.)
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20
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Lasker A, Obaidullah SM, Chakraborty C, Roy K. Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review. SN COMPUTER SCIENCE 2022; 4:65. [PMID: 36467853 PMCID: PMC9702883 DOI: 10.1007/s42979-022-01464-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 10/18/2022] [Indexed: 11/26/2022]
Abstract
Lung, being one of the most important organs in human body, is often affected by various SARS diseases, among which COVID-19 has been found to be the most fatal disease in recent times. In fact, SARS-COVID 19 led to pandemic that spreads fast among the community causing respiratory problems. Under such situation, radiological imaging-based screening [mostly chest X-ray and computer tomography (CT) modalities] has been performed for rapid screening of the disease as it is a non-invasive approach. Due to scarcity of physician/chest specialist/expert doctors, technology-enabled disease screening techniques have been developed by several researchers with the help of artificial intelligence and machine learning (AI/ML). It can be remarkably observed that the researchers have introduced several AI/ML/DL (deep learning) algorithms for computer-assisted detection of COVID-19 using chest X-ray and CT images. In this paper, a comprehensive review has been conducted to summarize the works related to applications of AI/ML/DL for diagnostic prediction of COVID-19, mainly using X-ray and CT images. Following the PRISMA guidelines, total 265 articles have been selected out of 1715 published articles till the third quarter of 2021. Furthermore, this review summarizes and compares varieties of ML/DL techniques, various datasets, and their results using X-ray and CT imaging. A detailed discussion has been made on the novelty of the published works, along with advantages and limitations.
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Affiliation(s)
- Asifuzzaman Lasker
- Department of Computer Science & Engineering, Aliah University, Kolkata, India
| | - Sk Md Obaidullah
- Department of Computer Science & Engineering, Aliah University, Kolkata, India
| | - Chandan Chakraborty
- Department of Computer Science & Engineering, National Institute of Technical Teachers’ Training & Research Kolkata, Kolkata, India
| | - Kaushik Roy
- Department of Computer Science, West Bengal State University, Barasat, India
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21
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Grüne B, Kugler S, Ginzel S, Wolff A, Buess M, Kossow A, Küfer-Weiß A, Rüping S, Neuhann F. Symptom diaries as a digital tool to detect SARS-CoV-2 infections and differentiate between prevalent variants. Front Public Health 2022; 10:1030939. [PMID: 36452944 PMCID: PMC9701827 DOI: 10.3389/fpubh.2022.1030939] [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: 08/29/2022] [Accepted: 10/14/2022] [Indexed: 11/16/2022] Open
Abstract
The COVID-19 pandemic and the high numbers of infected individuals pose major challenges for public health departments. To overcome these challenges, the health department in Cologne has developed a software called DiKoMa. This software offers the possibility to track contact and index persons, but also provides a digital symptom diary. In this work, the question of whether these can also be used for diagnostic purposes will be investigated. Machine learning makes it possible to identify infections based on early symptom profiles and to distinguish between the predominant dominant variants. Focusing on the occurrence of the symptoms in the first week, a decision tree is trained for the differentiation between contact and index persons and the prevailing dominant variants (Wildtype, Alpha, Delta, and Omicron). The model is evaluated, using sex- and age-stratified cross-validation and validated by symptom profiles of the first 6 days. The variants achieve an AUC-ROC from 0.89 for Omicron and 0.6 for Alpha. No significant differences are observed for the results of the validation set (Alpha 0.63 and Omicron 0.87). The evaluation of symptom combinations using artificial intelligence can determine the individual risk for the presence of a COVID-19 infection, allows assignment to virus variants, and can contribute to the management of epidemics and pandemics on a national and international level. It can help to reduce the number of specific tests in times of low labor capacity and could help to early identify new virus variants.
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Affiliation(s)
| | - Sabine Kugler
- Department of Knowledge Discovery, Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Sankt Augustin, Germany,*Correspondence: Sabine Kugler
| | - Sebastian Ginzel
- Department of Knowledge Discovery, Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Sankt Augustin, Germany
| | - Anna Wolff
- Health Department Cologne, Cologne, Germany
| | | | - Annelene Kossow
- Health Department Cologne, Cologne, Germany,Institute for Hygiene, University Hospital Muenster, Muenster, Germany
| | | | - Stefan Rüping
- Department of Knowledge Discovery, Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Sankt Augustin, Germany
| | - Florian Neuhann
- Heidelberg Institute for Global Health Heidelberg University Hospital, Heidelberg, Germany,School of Medicine Lewy Mwanawasa Medical University, Lusaka, Zambia
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22
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Lohiya DV, Pathak SS. Role of Technology in Detection of COVID-19. Cureus 2022; 14:e29138. [PMID: 36259008 PMCID: PMC9573002 DOI: 10.7759/cureus.29138] [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: 07/14/2022] [Accepted: 09/13/2022] [Indexed: 12/01/2022] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus caused coronavirus infection termed as COVID-19, an illness that has spread devastation all over the world. It was developed first in China and had swiftly spread throughout the world. COVID has created imposed burden on health in the lives of all individuals around the globe. This article provides a number of unprecedented detection technologies used in the detection of infection. COVID has created a large number of symptoms in the young, adolescent as well as elderly population. Old age people are susceptible to fatal serious symptoms because of low immunity. With these goals in mind, this article includes substantial condemning descriptions of the majority of initiatives in order to create diagnostic tools for easy diagnosis. It also provides the reader with a multidisciplinary viewpoint on how traditional approaches such as serology and reverse transcriptase polymerase chain reaction (RT-PCR) along with the frontline techniques such as clustered regularly interspaced short palindromic repeats (CRISPR)/Cas and artificial intelligence/machine learning have been utilized to gather information. The story will inspire creative new ways for successful detection therapy and to prevent this pandemic among a wide audience of operating and aspiring biomedical scientists and engineers.
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23
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Sun J, Peng L, Li T, Adila D, Zaiman Z, Melton-Meaux GB, Ingraham NE, Murray E, Boley D, Switzer S, Burns JL, Huang K, Allen T, Steenburg SD, Gichoya JW, Kummerfeld E, Tignanelli CJ. Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study. Radiol Artif Intell 2022; 4:e210217. [PMID: 35923381 PMCID: PMC9344211 DOI: 10.1148/ryai.210217] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 03/31/2022] [Accepted: 05/11/2022] [Indexed: 05/27/2023]
Abstract
Purpose To conduct a prospective observational study across 12 U.S. hospitals to evaluate real-time performance of an interpretable artificial intelligence (AI) model to detect COVID-19 on chest radiographs. Materials and Methods A total of 95 363 chest radiographs were included in model training, external validation, and real-time validation. The model was deployed as a clinical decision support system, and performance was prospectively evaluated. There were 5335 total real-time predictions and a COVID-19 prevalence of 4.8% (258 of 5335). Model performance was assessed with use of receiver operating characteristic analysis, precision-recall curves, and F1 score. Logistic regression was used to evaluate the association of race and sex with AI model diagnostic accuracy. To compare model accuracy with the performance of board-certified radiologists, a third dataset of 1638 images was read independently by two radiologists. Results Participants positive for COVID-19 had higher COVID-19 diagnostic scores than participants negative for COVID-19 (median, 0.1 [IQR, 0.0-0.8] vs 0.0 [IQR, 0.0-0.1], respectively; P < .001). Real-time model performance was unchanged over 19 weeks of implementation (area under the receiver operating characteristic curve, 0.70; 95% CI: 0.66, 0.73). Model sensitivity was higher in men than women (P = .01), whereas model specificity was higher in women (P = .001). Sensitivity was higher for Asian (P = .002) and Black (P = .046) participants compared with White participants. The COVID-19 AI diagnostic system had worse accuracy (63.5% correct) compared with radiologist predictions (radiologist 1 = 67.8% correct, radiologist 2 = 68.6% correct; McNemar P < .001 for both). Conclusion AI-based tools have not yet reached full diagnostic potential for COVID-19 and underperform compared with radiologist prediction.Keywords: Diagnosis, Classification, Application Domain, Infection, Lung Supplemental material is available for this article.. © RSNA, 2022.
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Affiliation(s)
- Ju Sun
- From the Department of Computer Science and Engineering (J.S., L.P.,
T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.),
Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of
Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and
Department of Radiology (T.A.), University of Minnesota, 420 Delaware St SE,
Minneapolis, MN 55455; Departments of Computer Science (Z.Z.) and Radiology
(J.W.G.), Emory University, Atlanta, Ga; M Health Fairview Informatics,
Minneapolis, Minn (E.M.); The School of Medicine (J.L.B., K.H.) and Department
of Radiology (S.D.S.), Indiana University, Indianapolis, Ind; and Department of
Surgery, North Memorial Health Hospital, Robbinsdale, Minn (C.J.T.)
| | - Le Peng
- From the Department of Computer Science and Engineering (J.S., L.P.,
T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.),
Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of
Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and
Department of Radiology (T.A.), University of Minnesota, 420 Delaware St SE,
Minneapolis, MN 55455; Departments of Computer Science (Z.Z.) and Radiology
(J.W.G.), Emory University, Atlanta, Ga; M Health Fairview Informatics,
Minneapolis, Minn (E.M.); The School of Medicine (J.L.B., K.H.) and Department
of Radiology (S.D.S.), Indiana University, Indianapolis, Ind; and Department of
Surgery, North Memorial Health Hospital, Robbinsdale, Minn (C.J.T.)
| | - Taihui Li
- From the Department of Computer Science and Engineering (J.S., L.P.,
T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.),
Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of
Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and
Department of Radiology (T.A.), University of Minnesota, 420 Delaware St SE,
Minneapolis, MN 55455; Departments of Computer Science (Z.Z.) and Radiology
(J.W.G.), Emory University, Atlanta, Ga; M Health Fairview Informatics,
Minneapolis, Minn (E.M.); The School of Medicine (J.L.B., K.H.) and Department
of Radiology (S.D.S.), Indiana University, Indianapolis, Ind; and Department of
Surgery, North Memorial Health Hospital, Robbinsdale, Minn (C.J.T.)
| | - Dyah Adila
- From the Department of Computer Science and Engineering (J.S., L.P.,
T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.),
Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of
Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and
Department of Radiology (T.A.), University of Minnesota, 420 Delaware St SE,
Minneapolis, MN 55455; Departments of Computer Science (Z.Z.) and Radiology
(J.W.G.), Emory University, Atlanta, Ga; M Health Fairview Informatics,
Minneapolis, Minn (E.M.); The School of Medicine (J.L.B., K.H.) and Department
of Radiology (S.D.S.), Indiana University, Indianapolis, Ind; and Department of
Surgery, North Memorial Health Hospital, Robbinsdale, Minn (C.J.T.)
| | - Zach Zaiman
- From the Department of Computer Science and Engineering (J.S., L.P.,
T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.),
Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of
Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and
Department of Radiology (T.A.), University of Minnesota, 420 Delaware St SE,
Minneapolis, MN 55455; Departments of Computer Science (Z.Z.) and Radiology
(J.W.G.), Emory University, Atlanta, Ga; M Health Fairview Informatics,
Minneapolis, Minn (E.M.); The School of Medicine (J.L.B., K.H.) and Department
of Radiology (S.D.S.), Indiana University, Indianapolis, Ind; and Department of
Surgery, North Memorial Health Hospital, Robbinsdale, Minn (C.J.T.)
| | - Genevieve B. Melton-Meaux
- From the Department of Computer Science and Engineering (J.S., L.P.,
T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.),
Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of
Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and
Department of Radiology (T.A.), University of Minnesota, 420 Delaware St SE,
Minneapolis, MN 55455; Departments of Computer Science (Z.Z.) and Radiology
(J.W.G.), Emory University, Atlanta, Ga; M Health Fairview Informatics,
Minneapolis, Minn (E.M.); The School of Medicine (J.L.B., K.H.) and Department
of Radiology (S.D.S.), Indiana University, Indianapolis, Ind; and Department of
Surgery, North Memorial Health Hospital, Robbinsdale, Minn (C.J.T.)
| | - Nicholas E. Ingraham
- From the Department of Computer Science and Engineering (J.S., L.P.,
T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.),
Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of
Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and
Department of Radiology (T.A.), University of Minnesota, 420 Delaware St SE,
Minneapolis, MN 55455; Departments of Computer Science (Z.Z.) and Radiology
(J.W.G.), Emory University, Atlanta, Ga; M Health Fairview Informatics,
Minneapolis, Minn (E.M.); The School of Medicine (J.L.B., K.H.) and Department
of Radiology (S.D.S.), Indiana University, Indianapolis, Ind; and Department of
Surgery, North Memorial Health Hospital, Robbinsdale, Minn (C.J.T.)
| | - Eric Murray
- From the Department of Computer Science and Engineering (J.S., L.P.,
T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.),
Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of
Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and
Department of Radiology (T.A.), University of Minnesota, 420 Delaware St SE,
Minneapolis, MN 55455; Departments of Computer Science (Z.Z.) and Radiology
(J.W.G.), Emory University, Atlanta, Ga; M Health Fairview Informatics,
Minneapolis, Minn (E.M.); The School of Medicine (J.L.B., K.H.) and Department
of Radiology (S.D.S.), Indiana University, Indianapolis, Ind; and Department of
Surgery, North Memorial Health Hospital, Robbinsdale, Minn (C.J.T.)
| | - Daniel Boley
- From the Department of Computer Science and Engineering (J.S., L.P.,
T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.),
Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of
Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and
Department of Radiology (T.A.), University of Minnesota, 420 Delaware St SE,
Minneapolis, MN 55455; Departments of Computer Science (Z.Z.) and Radiology
(J.W.G.), Emory University, Atlanta, Ga; M Health Fairview Informatics,
Minneapolis, Minn (E.M.); The School of Medicine (J.L.B., K.H.) and Department
of Radiology (S.D.S.), Indiana University, Indianapolis, Ind; and Department of
Surgery, North Memorial Health Hospital, Robbinsdale, Minn (C.J.T.)
| | - Sean Switzer
- From the Department of Computer Science and Engineering (J.S., L.P.,
T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.),
Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of
Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and
Department of Radiology (T.A.), University of Minnesota, 420 Delaware St SE,
Minneapolis, MN 55455; Departments of Computer Science (Z.Z.) and Radiology
(J.W.G.), Emory University, Atlanta, Ga; M Health Fairview Informatics,
Minneapolis, Minn (E.M.); The School of Medicine (J.L.B., K.H.) and Department
of Radiology (S.D.S.), Indiana University, Indianapolis, Ind; and Department of
Surgery, North Memorial Health Hospital, Robbinsdale, Minn (C.J.T.)
| | - John L. Burns
- From the Department of Computer Science and Engineering (J.S., L.P.,
T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.),
Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of
Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and
Department of Radiology (T.A.), University of Minnesota, 420 Delaware St SE,
Minneapolis, MN 55455; Departments of Computer Science (Z.Z.) and Radiology
(J.W.G.), Emory University, Atlanta, Ga; M Health Fairview Informatics,
Minneapolis, Minn (E.M.); The School of Medicine (J.L.B., K.H.) and Department
of Radiology (S.D.S.), Indiana University, Indianapolis, Ind; and Department of
Surgery, North Memorial Health Hospital, Robbinsdale, Minn (C.J.T.)
| | - Kun Huang
- From the Department of Computer Science and Engineering (J.S., L.P.,
T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.),
Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of
Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and
Department of Radiology (T.A.), University of Minnesota, 420 Delaware St SE,
Minneapolis, MN 55455; Departments of Computer Science (Z.Z.) and Radiology
(J.W.G.), Emory University, Atlanta, Ga; M Health Fairview Informatics,
Minneapolis, Minn (E.M.); The School of Medicine (J.L.B., K.H.) and Department
of Radiology (S.D.S.), Indiana University, Indianapolis, Ind; and Department of
Surgery, North Memorial Health Hospital, Robbinsdale, Minn (C.J.T.)
| | - Tadashi Allen
- From the Department of Computer Science and Engineering (J.S., L.P.,
T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.),
Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of
Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and
Department of Radiology (T.A.), University of Minnesota, 420 Delaware St SE,
Minneapolis, MN 55455; Departments of Computer Science (Z.Z.) and Radiology
(J.W.G.), Emory University, Atlanta, Ga; M Health Fairview Informatics,
Minneapolis, Minn (E.M.); The School of Medicine (J.L.B., K.H.) and Department
of Radiology (S.D.S.), Indiana University, Indianapolis, Ind; and Department of
Surgery, North Memorial Health Hospital, Robbinsdale, Minn (C.J.T.)
| | - Scott D. Steenburg
- From the Department of Computer Science and Engineering (J.S., L.P.,
T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.),
Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of
Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and
Department of Radiology (T.A.), University of Minnesota, 420 Delaware St SE,
Minneapolis, MN 55455; Departments of Computer Science (Z.Z.) and Radiology
(J.W.G.), Emory University, Atlanta, Ga; M Health Fairview Informatics,
Minneapolis, Minn (E.M.); The School of Medicine (J.L.B., K.H.) and Department
of Radiology (S.D.S.), Indiana University, Indianapolis, Ind; and Department of
Surgery, North Memorial Health Hospital, Robbinsdale, Minn (C.J.T.)
| | - Judy Wawira Gichoya
- From the Department of Computer Science and Engineering (J.S., L.P.,
T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.),
Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of
Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and
Department of Radiology (T.A.), University of Minnesota, 420 Delaware St SE,
Minneapolis, MN 55455; Departments of Computer Science (Z.Z.) and Radiology
(J.W.G.), Emory University, Atlanta, Ga; M Health Fairview Informatics,
Minneapolis, Minn (E.M.); The School of Medicine (J.L.B., K.H.) and Department
of Radiology (S.D.S.), Indiana University, Indianapolis, Ind; and Department of
Surgery, North Memorial Health Hospital, Robbinsdale, Minn (C.J.T.)
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Subramanian M, Shanmuga Vadivel K, Hatamleh WA, Alnuaim AA, Abdelhady M, V E S. The role of contemporary digital tools and technologies in COVID-19 crisis: An exploratory analysis. EXPERT SYSTEMS 2022; 39:e12834. [PMID: 34898797 PMCID: PMC8646626 DOI: 10.1111/exsy.12834] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/10/2021] [Accepted: 09/09/2021] [Indexed: 05/17/2023]
Abstract
Following the COVID-19 pandemic, there has been an increase in interest in using digital resources to contain pandemics. To avoid, detect, monitor, regulate, track, and manage diseases, predict outbreaks and conduct data analysis and decision-making processes, a variety of digital technologies are used, ranging from artificial intelligence (AI)-powered machine learning (ML) or deep learning (DL) focused applications to blockchain technology and big data analytics enabled by cloud computing and the internet of things (IoT). In this paper, we look at how emerging technologies such as the IoT and sensors, AI, ML, DL, blockchain, augmented reality, virtual reality, cloud computing, big data, robots and drones, intelligent mobile apps, and 5G are advancing health care and paving the way to combat the COVID-19 pandemic. The aim of this research is to look at possible technologies, processes, and tools for addressing COVID-19 issues such as pre-screening, early detection, monitoring infected/quarantined individuals, forecasting future infection rates, and more. We also look at the research possibilities that have arisen as a result of the use of emerging technology to handle the COVID-19 crisis.
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Affiliation(s)
- Malliga Subramanian
- Department of Computer Science and EngineeringKongu Engineering CollegePerunduraiTamilnaduIndia
| | | | - Wesam Atef Hatamleh
- Department of Computer Science, College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia
| | - Abeer Ali Alnuaim
- Department of Computer Science and Engineering, College of Applied Studies and Community ServicesKing Saud UniversityRiyadhSaudi Arabia
| | - Mohamed Abdelhady
- Electrical and Computer Engineering DepartmentCleveland State UniversityClevelandOhioUSA
| | - Sathishkumar V E
- Department of Computer Science and EngineeringKongu Engineering CollegePerunduraiTamilnaduIndia
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25
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Gülbay M, Baştuğ A, Özkan E, Öztürk BY, Mendi BAR, Bodur H. Evaluation of the models generated from clinical features and deep learning-based segmentations: Can thoracic CT on admission help us to predict hospitalized COVID-19 patients who will require intensive care? BMC Med Imaging 2022; 22:110. [PMID: 35672719 PMCID: PMC9172094 DOI: 10.1186/s12880-022-00833-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 05/27/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and artificial intelligence segmentation-based volumetric and CT-radiomics parameters on admission. METHODS Twenty-eight clinical/laboratory features, 21 volumetric parameters, and 74 radiomics parameters obtained by deep learning (DL)-based segmentations from CT examinations of 191 severe COVID-19 inpatients admitted between March 2020 and March 2021 were collected. Patients were divided into Group 1 (117 patients discharged from the inpatient service) and Group 2 (74 patients transferred to the ICU), and the differences between the groups were evaluated with the T-test and Mann-Whitney test. The sensitivities and specificities of significantly different parameters were evaluated by ROC analysis. Subsequently, 152 (79.5%) patients were assigned to the training/cross-validation set, and 39 (20.5%) patients were assigned to the test set. Clinical, radiological, and combined logit-fit models were generated by using the Bayesian information criterion from the training set and optimized via tenfold cross-validation. To simultaneously use all of the clinical, volumetric, and radiomics parameters, a random forest model was produced, and this model was trained by using a balanced training set created by adding synthetic data to the existing training/cross-validation set. The results of the models in predicting ICU patients were evaluated with the test set. RESULTS No parameter individually created a reliable classifier. When the test set was evaluated with the final models, the AUC values were 0.736, 0.708, and 0.794, the specificity values were 79.17%, 79.17%, and 87.50%, the sensitivity values were 66.67%, 60%, and 73.33%, and the F1 values were 0.67, 0.62, and 0.76 for the clinical, radiological, and combined logit-fit models, respectively. The random forest model that was trained with the balanced training/cross-validation set was the most successful model, achieving an AUC of 0.837, specificity of 87.50%, sensitivity of 80%, and F1 value of 0.80 in the test set. CONCLUSION By using a machine learning algorithm that was composed of clinical and DL-segmentation-based radiological parameters and that was trained with a balanced data set, COVID-19 patients who may require intensive care could be successfully predicted.
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Affiliation(s)
- Mutlu Gülbay
- Department of Radiology, Ankara City Hospital, Üniversiteler Mahallesi 1604. Cadde No: 9, 06800, Çankaya, Ankara, Turkey.
| | - Aliye Baştuğ
- Department of Infectious Diseases and Clinical Microbiology, University of Health Sciences Turkey, Gülhane Faculty of Medicine, Ankara City Hospital, Ankara, Turkey
| | - Erdem Özkan
- Department of Radiology, Ankara City Hospital, Üniversiteler Mahallesi 1604. Cadde No: 9, 06800, Çankaya, Ankara, Turkey
| | - Büşra Yüce Öztürk
- Department of Clinical Microbiology and Infectious Diseases, Ankara City Hospital, Ankara, Turkey
| | - Bökebatur Ahmet Raşit Mendi
- Department of Radiology, Ankara City Hospital, Üniversiteler Mahallesi 1604. Cadde No: 9, 06800, Çankaya, Ankara, Turkey
| | - Hürrem Bodur
- Department of Infectious Diseases and Clinical Microbiology, University of Health Sciences Turkey, Gülhane Faculty of Medicine, Ankara City Hospital, Ankara, Turkey
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26
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In Silico Finite Element Modeling of Stress Distribution in Osteosynthesis after Pertrochanteric Fractures. J Clin Med 2022; 11:jcm11071885. [PMID: 35407491 PMCID: PMC8999495 DOI: 10.3390/jcm11071885] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/15/2022] [Accepted: 03/25/2022] [Indexed: 12/13/2022] Open
Abstract
A stabilization method of pertrochanteric femur fractures is a contentious issue. Here, we assess the feasibility of rapid in silico 2D finite element modeling (FEM) to predict the distribution of stresses arising during the two most often used stabilization methods: gamma nail fixation (GNF) and dynamic hip screw (DHS). The modeling was based on standard pre-surgery radiographs of hip joints of 15 patients with pertrochanteric fractures of type A1, A2, and A3 according to the AO/OTA classification. The FEM showed that the stresses were similar for both GNF and DHS, with the medians ranging between 53-60 MPa and consistently lower for A1 than A3 fractures. Stresses also appeared in the fixation materials being about two-fold higher for GNF. Given similar bone stresses caused by both GNF and DHS but shorter surgery time, less extensive dissection, and faster patient mobilization, we submit that the GNF stabilization appears to be the most optimal system for pertrochanteric fractures. In silico FEM appears a viable perioperative method that helps predict the distribution of compressive stresses after osteosynthesis of pertrochanteric fractures. The promptness of modeling fits well into the rigid time framework of hip fracture surgery and may help optimize the fixation procedure for the best outcome. The study extends the use of FEM in complex orthopedic management. However, further datasets are required to firmly position the FEM in the treatment of pertrochanteric fractures.
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Wang J, Yang X, Zhou B, Sohn JJ, Zhou J, Jacob JT, Higgins KA, Bradley JD, Liu T. Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic. J Imaging 2022; 8:65. [PMID: 35324620 PMCID: PMC8952297 DOI: 10.3390/jimaging8030065] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 03/01/2022] [Accepted: 03/02/2022] [Indexed: 12/25/2022] Open
Abstract
Ultrasound imaging of the lung has played an important role in managing patients with COVID-19-associated pneumonia and acute respiratory distress syndrome (ARDS). During the COVID-19 pandemic, lung ultrasound (LUS) or point-of-care ultrasound (POCUS) has been a popular diagnostic tool due to its unique imaging capability and logistical advantages over chest X-ray and CT. Pneumonia/ARDS is associated with the sonographic appearances of pleural line irregularities and B-line artefacts, which are caused by interstitial thickening and inflammation, and increase in number with severity. Artificial intelligence (AI), particularly machine learning, is increasingly used as a critical tool that assists clinicians in LUS image reading and COVID-19 decision making. We conducted a systematic review from academic databases (PubMed and Google Scholar) and preprints on arXiv or TechRxiv of the state-of-the-art machine learning technologies for LUS images in COVID-19 diagnosis. Openly accessible LUS datasets are listed. Various machine learning architectures have been employed to evaluate LUS and showed high performance. This paper will summarize the current development of AI for COVID-19 management and the outlook for emerging trends of combining AI-based LUS with robotics, telehealth, and other techniques.
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Affiliation(s)
- Jing Wang
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
| | - Xiaofeng Yang
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
| | - Boran Zhou
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
| | - James J. Sohn
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA 23219, USA;
| | - Jun Zhou
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
| | - Jesse T. Jacob
- Division of Infectious Diseases, Department of Medicine, Emory University, Atlanta, GA 30322, USA;
| | - Kristin A. Higgins
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
| | - Jeffrey D. Bradley
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
| | - Tian Liu
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
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Huo W, Zheng G, Yan J, Le S, Han L. Interacting with medical artificial intelligence: Integrating self-responsibility attribution, human–computer trust, and personality. COMPUTERS IN HUMAN BEHAVIOR 2022. [DOI: 10.1016/j.chb.2022.107253] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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Boddu RSK, Karmakar P, Bhaumik A, Nassa VK, Bhattacharya S. Analyzing the impact of machine learning and artificial intelligence and its effect on management of lung cancer detection in covid-19 pandemic. MATERIALS TODAY: PROCEEDINGS 2022; 56:2213-2216. [PMID: 34877264 PMCID: PMC8641302 DOI: 10.1016/j.matpr.2021.11.549] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Cancer victims, particularly those with lung cancer, are more susceptible and at higher danger of COVID-19 and associated consequences as a result of their compromised immune systems, which makes them particularly sensitive. Because of a variety of circumstances, cancer patients' diagnosis, treatment, and aftercare are very complicated and time-consuming during an epidemic. In such circumstances, advances in artificial intelligence (AI) and machine learning algorithms (ML) offer the capacity to boost cancer sufferer diagnosis, therapy, and care via the use of cutting technologies. For example, using clinical and imaging data combined with machine learning methods, the researchers may be able to distinguish among lung alterations induced by corona virus and those produced by immunotherapy and radiation. During this epidemic, artificial intelligence (AI) may be utilized to guarantee that the appropriate individuals are recruited in cancer clinical trials more quickly and effectively than in the past, which was done in a conventional and complicated manner. In order to better care for cancer patients and find novel and more effective therapies, It is critical that we move beyond traditional research methods and use artificial intelligence (AI) and machine learning to update our research (ML). Artificial intelligence (AI) and machine learning (ML) are being utilised to help with several aspects of the COVID-19 epidemic, such as epidemiology, molecular research and medication development, medical diagnosis and treatment, and socioeconomics. The use of artificial intelligence (AI) and machine learning (ML) in the diagnosis and treatment of COVID-19 patients is also being investigated. The combination of artificial intelligence and machine learning in COVID-19 may help to identify positive patients more quickly. In order to understand the dynamics of an epidemic that is relevant to artificial intelligence, when used in different patient groups, AI-based algorithms can quickly detect CT scans with COVID-19 linked pneumonia, as well as discriminate non-COVID connected pneumonia with high specificity and accuracy. It is possible to utilize the existing difficulties and future views presented in this study to guide an optimal implementation of AI and machine learning technologies in an epidemic.
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Affiliation(s)
| | - Partha Karmakar
- Government of West Bengal, Bikash Bhawan, Salt Lake, Kolkata, W.B., India
| | - Ankan Bhaumik
- Dept. of Applied Mathematics With Oceanology and Computer Programming, Vidyasagar University, Midnapore, India
| | - Vinay Kumar Nassa
- Department of Computer Science Engineering, South Point Group of Institutions Sonepat, Haryana, India
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30
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Chen RM. Analysing deaths and confirmed cases of COVID-19 pandemic by analytical approaches. THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS 2022; 231:3603-3617. [PMID: 35340737 PMCID: PMC8935271 DOI: 10.1140/epjs/s11734-022-00535-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 03/05/2022] [Indexed: 05/05/2023]
Abstract
In this work, the time series of growth rates regarding confirmed cases and deaths of COVID-19 for several sampled countries are investigated via an introduction of an orthonormal basis. This basis, which is served as the feature benchmark, reveals the hidden features of COVID-19 via the magnitude of Fourier coefficients. These coefficients are ranked in the form of ranking vectors for all the sampled countries. Based on these and Manhattan metric, we then perform spectral clustering to categorise the countries. Unlike the classical cosine similarity analysis which, relatively speaking, is a composite index and hard to identify the features of the categorised countries, spectral analysis delves into the internal structures or dynamical trend of the time series. This research shows there is no single feature that dominates the trend of the growth rates. It also reveals that results from the spectral analysis are different from the ones of cosine similarity. In the end, some approximated values of the confirmed cases and deaths are also calculated by the spectral analysis.
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Affiliation(s)
- Ray-Ming Chen
- School of Mathematics and Statistics, Baise University, 21, Zhongshan No. 2 Road, Baise, Guangxi Province China
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31
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Chaddad A, Hassan L, Desrosiers C. Deep Radiomic Analysis for Predicting Coronavirus Disease 2019 in Computerized Tomography and X-Ray Images. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:3-11. [PMID: 34669582 DOI: 10.1109/tnnls.2021.3119071] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
This article proposes to encode the distribution of features learned from a convolutional neural network (CNN) using a Gaussian mixture model (GMM). These parametric features, called GMM-CNN, are derived from chest computed tomography (CT) and X-ray scans of patients with coronavirus disease 2019 (COVID-19). We use the proposed GMM-CNN features as input to a robust classifier based on random forests (RFs) to differentiate between COVID-19 and other pneumonia cases. Our experiments assess the advantage of GMM-CNN features compared with standard CNN classification on test images. Using an RF classifier (80% samples for training; 20% samples for testing), GMM-CNN features encoded with two mixture components provided a significantly better performance than standard CNN classification ( ). Specifically, our method achieved an accuracy in the range of 96.00%-96.70% and an area under the receiver operator characteristic (ROC) curve in the range of 99.29%-99.45%, with the best performance obtained by combining GMM-CNN features from both CT and X-ray images. Our results suggest that the proposed GMM-CNN features could improve the prediction of COVID-19 in chest CT and X-ray scans.
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Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments. Symmetry (Basel) 2021. [DOI: 10.3390/sym14010016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.
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An Exploration into the Detection of COVID-19 from Chest X-ray Scans Using the xRGM-NET Convolutional Neural Network. TECHNOLOGIES 2021. [DOI: 10.3390/technologies9040098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
COVID-19 has spread rapidly across the world since late 2019. As of December, 2021, there are over 250 million documented COVID-19 cases and over 5 million deaths worldwide, which have caused businesses, schools, and government operations to shut down. The most common method of detecting COVID-19 is the RT-PCR swab test, which suffers from a high false-negative rate and a very slow turnaround for results, often up to two weeks. Because of this, specialists often manually review X-ray images of the lungs to detect the presence of COVID-19 with up to 97% accuracy. Neural network algorithms greatly accelerate this review process, analyzing hundreds of X-rays in seconds. Using the Cohen COVID-19 X-ray Database and the NIH ChestX-ray8 Database, we trained and constructed the xRGM-NET convolutional neural network (CNN) to detect COVID-19 in X-ray scans of the lungs. To further aid medical professionals in the manual review of X-rays, we implemented the CNN activation mapping technique Score-CAM, which generates a heat map over an X-ray to illustrate which areas in the scan are most influential over the ultimate diagnosis. xRGM-NET achieved an overall classification accuracy of 97% with a sensitivity of 94% and specificity of 97%. Lightweight models like xRGM-NET can serve to improve the efficiency and accuracy of COVID-19 detection in developing countries or rural areas. In this paper, we report on our model and methods that were developed as part of a STEM enrichment summer program for high school students. We hope that our model and methods will allow other researchers to create lightweight and accurate models as more COVID-19 X-ray scans become available.
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Saki EF, Setiawan SA, Wicaksono DHB. Portable Tools for COVID-19 Point-of-Care Detection: A Review. IEEE SENSORS JOURNAL 2021; 21:23737-23750. [PMID: 35582343 PMCID: PMC8864949 DOI: 10.1109/jsen.2021.3110857] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/22/2021] [Accepted: 08/26/2021] [Indexed: 06/12/2023]
Abstract
Recently, several methods for SARS-CoV-2 detection have been developed to obtain rapid, portable, cheap, and easy-to-use diagnostic tools. This review paper summarizes and discusses studies on the development of point-of-care devices for SARS-CoV-2 diagnosis with comparisons between them from several aspects. Various detection methods of the recently developed portable COVID-19 biosensor will be presented in this review. The discussion is divided into four major classifications based on the target biomarkers of SARS-CoV-2, such as antibodies, nucleic acids, antigens, and metabolic products. An overview of the potential development for future study is also provided. Moreover, basic knowledge of biosensors is also explained for tutoring the implementation of theory into the research of COVID-19 biosensors. This review paper is aimed to provide a tutorial by collecting the information on the development of a point-of-care device for SARS-CoV-2 detection to provide information for further research and propose the new COVID-19 portable diagnostic tool.
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Affiliation(s)
- Elga F. Saki
- Department of Biomedical EngineeringFaculty of Life Sciences and TechnologySwiss German University (SGU)Tangerang15143Indonesia
| | | | - Dedy H. B. Wicaksono
- Department of Biomedical EngineeringFaculty of Life Sciences and TechnologySwiss German University (SGU)Tangerang15143Indonesia
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Riad A, Huang Y, Abdulqader H, Morgado M, Domnori S, Koščík M, Mendes JJ, Klugar M, Kateeb E. Universal Predictors of Dental Students' Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach. Vaccines (Basel) 2021; 9:1158. [PMID: 34696266 PMCID: PMC8539257 DOI: 10.3390/vaccines9101158] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/01/2021] [Accepted: 10/08/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND young adults represent a critical target for mass-vaccination strategies of COVID-19 that aim to achieve herd immunity. Healthcare students, including dental students, are perceived as the upper echelon of health literacy; therefore, their health-related beliefs, attitudes and behaviors influence their peers and communities. The main aim of this study was to synthesize a data-driven model for the predictors of COVID-19 vaccine willingness among dental students. METHODS a secondary analysis of data extracted from a recently conducted multi-center and multi-national cross-sectional study of dental students' attitudes towards COVID-19 vaccination in 22 countries was carried out utilizing decision tree and regression analyses. Based on previous literature, a proposed conceptual model was developed and tested through a machine learning approach to elicit factors related to dental students' willingness to get the COVID-19 vaccine. RESULTS machine learning analysis suggested five important predictors of COVID-19 vaccination willingness among dental students globally, i.e., the economic level of the country where the student lives and studies, the individual's trust of the pharmaceutical industry, the individual's misconception of natural immunity, the individual's belief of vaccines risk-benefit-ratio, and the individual's attitudes toward novel vaccines. CONCLUSIONS according to the socio-ecological theory, the country's economic level was the only contextual predictor, while the rest were individual predictors. Future research is recommended to be designed in a longitudinal fashion to facilitate evaluating the proposed model. The interventions of controlling vaccine hesitancy among the youth population may benefit from improving their views of the risk-benefit ratio of COVID-19 vaccines. Moreover, healthcare students, including dental students, will likely benefit from increasing their awareness of immunization and infectious diseases through curricular amendments.
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Affiliation(s)
- Abanoub Riad
- Department of Public Health, Faculty of Medicine, Masaryk University, 625 00 Brno, Czech Republic; (M.K.); (M.K.)
- International Association of Dental Students (IADS), 1216 Geneva, Switzerland; (H.A.); (M.M.); (S.D.)
- Czech National Centre for Evidence-Based Healthcare and Knowledge Translation (Cochrane Czech Republic, Czech EBHC: JBI Centre of Excellence, Masaryk University GRADE Centre), Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic
| | - Yi Huang
- Department of Psychology, Faculty of Social Studies, Masaryk University, 602 00 Brno, Czech Republic;
- Institute for Research of Children, Youth and Family, Faculty of Social Studies, Masaryk University, 602 00 Brno, Czech Republic
| | - Huthaifa Abdulqader
- International Association of Dental Students (IADS), 1216 Geneva, Switzerland; (H.A.); (M.M.); (S.D.)
| | - Mariana Morgado
- International Association of Dental Students (IADS), 1216 Geneva, Switzerland; (H.A.); (M.M.); (S.D.)
- Clinical Research Unit (CRU), Egas Moniz Cooperativa de Ensino Superior, 2829-511 Almada, Portugal;
| | - Silvi Domnori
- International Association of Dental Students (IADS), 1216 Geneva, Switzerland; (H.A.); (M.M.); (S.D.)
| | - Michal Koščík
- Department of Public Health, Faculty of Medicine, Masaryk University, 625 00 Brno, Czech Republic; (M.K.); (M.K.)
| | - José João Mendes
- Clinical Research Unit (CRU), Egas Moniz Cooperativa de Ensino Superior, 2829-511 Almada, Portugal;
| | - Miloslav Klugar
- Department of Public Health, Faculty of Medicine, Masaryk University, 625 00 Brno, Czech Republic; (M.K.); (M.K.)
- Czech National Centre for Evidence-Based Healthcare and Knowledge Translation (Cochrane Czech Republic, Czech EBHC: JBI Centre of Excellence, Masaryk University GRADE Centre), Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic
| | - Elham Kateeb
- Oral Health Research and Promotion Unit, Faculty of Dentistry, Al-Quds University, Jerusalem 510 00, Palestine;
- Public Health Committee, World Dental Federation (FDI), 1216 Geneva, Switzerland
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Hsiao WWW, Le TN, Pham DM, Ko HH, Chang HC, Lee CC, Sharma N, Lee CK, Chiang WH. Recent Advances in Novel Lateral Flow Technologies for Detection of COVID-19. BIOSENSORS 2021; 11:295. [PMID: 34562885 PMCID: PMC8466143 DOI: 10.3390/bios11090295] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 08/21/2021] [Accepted: 08/22/2021] [Indexed: 02/07/2023]
Abstract
The development of reliable and robust diagnostic tests is one of the most efficient methods to limit the spread of coronavirus disease 2019 (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). However, most laboratory diagnostics for COVID-19, such as enzyme-linked immunosorbent assay (ELISA) and reverse transcriptase-polymerase chain reaction (RT-PCR), are expensive, time-consuming, and require highly trained professional operators. On the other hand, the lateral flow immunoassay (LFIA) is a simpler, cheaper device that can be operated by unskilled personnel easily. Unfortunately, the current technique has some limitations, mainly inaccuracy in detection. This review article aims to highlight recent advances in novel lateral flow technologies for detecting SARS-CoV-2 as well as innovative approaches to achieve highly sensitive and specific point-of-care testing. Lastly, we discuss future perspectives on how smartphones and Artificial Intelligence (AI) can be integrated to revolutionize disease detection as well as disease control and surveillance.
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Affiliation(s)
- Wesley Wei-Wen Hsiao
- Department of Chemical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan; (T.-N.L.); (H.-C.C.); (N.S.); (C.-K.L.)
| | - Trong-Nghia Le
- Department of Chemical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan; (T.-N.L.); (H.-C.C.); (N.S.); (C.-K.L.)
| | - Dinh Minh Pham
- GENTIS JSC, 249A, Thuy Khue, Tay Ho, Hanoi 100000, Vietnam;
- Institute of Biotechnology, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet, Cau Giay, Hanoi 100000, Vietnam
| | - Hui-Hsin Ko
- Institute of Biological Chemistry, Academia Sinica, Taipei 115, Taiwan; (H.-H.K.); (C.-C.L.)
| | - Huan-Cheng Chang
- Department of Chemical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan; (T.-N.L.); (H.-C.C.); (N.S.); (C.-K.L.)
- Institute of Atomic and Molecular Sciences, Academia Sinica, Taipei 106, Taiwan
| | - Cheng-Chung Lee
- Institute of Biological Chemistry, Academia Sinica, Taipei 115, Taiwan; (H.-H.K.); (C.-C.L.)
| | - Neha Sharma
- Department of Chemical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan; (T.-N.L.); (H.-C.C.); (N.S.); (C.-K.L.)
| | - Cheng-Kang Lee
- Department of Chemical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan; (T.-N.L.); (H.-C.C.); (N.S.); (C.-K.L.)
| | - Wei-Hung Chiang
- Department of Chemical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan; (T.-N.L.); (H.-C.C.); (N.S.); (C.-K.L.)
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Efrati S, Catalogna M, Abu Hamed R, Hadanny A, Bar-Chaim A, Benveniste-Levkovitz P, Strugo R, Levtzion-Korach O. Early and long term antibody kinetics of asymptomatic and mild disease COVID-19 patients. Sci Rep 2021; 11:13780. [PMID: 34215811 PMCID: PMC8253728 DOI: 10.1038/s41598-021-93175-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 06/14/2021] [Indexed: 12/11/2022] Open
Abstract
Most patients infected with SARS-CoV-2 are asymptomatic or mildly symptomatic. However, the early and late antibody kinetics, and the association between antibody levels, clinical symptoms, and disease phase in these patients have not yet been fully defined. Confirmed SARS-CoV-2 patients and their household contacts were evaluated over a period four months. The evaluation procedure included symptom monitoring, viral load and serology analysis every ten days. A total of 1334 serum samples were collected from 135 patients and analyzed using three assays for IgG-N, IgG-S and IgM antibodies. Of the study participants, 97% were seropositive during the study, and two distinct clusters were identified. These clusters were significantly different in their inflammatory related symptoms. Peak IgG-S was 40.0 AU/ml for the non-inflammatory cluster and 71.5 AU/ml for the inflammatory cluster (P = 0.006), whereas IgG-N peaks were 4.3 and 5.87 (P = 0.023) respectively. Finally, a decision tree model was designed to predict the disease phase based on the serological titer levels, and had an overall accuracy of 80.7%. The specific profile of seroconversion and decay of serum antibodies can be used to predict the time-course from the acute infection.
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Affiliation(s)
- Shai Efrati
- Research and Development Unit, Shamir Medical Center, Zerifin, Israel.
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Merav Catalogna
- Research and Development Unit, Shamir Medical Center, Zerifin, Israel
| | - Ramzia Abu Hamed
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- Clinical Chemistry Laboratory, Shamir Medical Center, Zerifin, Israel
| | - Amir Hadanny
- Research and Development Unit, Shamir Medical Center, Zerifin, Israel
| | - Adina Bar-Chaim
- Clinical Chemistry Laboratory, Shamir Medical Center, Zerifin, Israel
| | | | | | - Osnat Levtzion-Korach
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- Medical Management, Shamir Medical Center, Zerifin, Israel
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Sun J, Peng L, Li T, Adila D, Zaiman Z, Melton GB, Ingraham N, Murray E, Boley D, Switzer S, Burns JL, Huang K, Allen T, Steenburg SD, Gichoya JW, Kummerfeld E, Tignanelli C. A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals. ARXIV 2021:arXiv:2106.02118v2. [PMID: 34099980 PMCID: PMC8183017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 06/07/2021] [Indexed: 11/22/2022]
Abstract
Importance An artificial intelligence (AI)-based model to predict COVID-19 likelihood from chest x-ray (CXR) findings can serve as an important adjunct to accelerate immediate clinical decision making and improve clinical decision making. Despite significant efforts, many limitations and biases exist in previously developed AI diagnostic models for COVID-19. Utilizing a large set of local and international CXR images, we developed an AI model with high performance on temporal and external validation. Objective Investigate real-time performance of an AI-enabled COVID-19 diagnostic support system across a 12-hospital system. Design Prospective observational study. Setting Labeled frontal CXR images (samples of COVID-19 and non-COVID-19) from the M Health Fairview (Minnesota, USA), Valencian Region Medical ImageBank (Spain), MIMIC-CXR, Open-I 2013 Chest X-ray Collection, GitHub COVID-19 Image Data Collection (International), Indiana University (Indiana, USA), and Emory University (Georgia, USA). Participants Internal (training, temporal, and real-time validation): 51,592 CXRs; Public: 27,424 CXRs; External (Indiana University): 10,002 CXRs; External (Emory University): 2002 CXRs. Main Outcome and Measure Model performance assessed via receiver operating characteristic (ROC), Precision-Recall curves, and F1 score. Results Patients that were COVID-19 positive had significantly higher COVID-19 Diagnostic Scores (median .1 [IQR: 0.0-0.8] vs median 0.0 [IQR: 0.0-0.1], p < 0.001) than patients that were COVID-19 negative. Pre-implementation the AI-model performed well on temporal validation (AUROC 0.8) and external validation (AUROC 0.76 at Indiana U, AUROC 0.72 at Emory U). The model was noted to have unrealistic performance (AUROC > 0.95) using publicly available databases. Real-time model performance was unchanged over 19 weeks of implementation (AUROC 0.70). On subgroup analysis, the model had improved discrimination for patients with "severe" as compared to "mild or moderate" disease, p < 0.001. Model performance was highest in Asians and lowest in whites and similar between males and females. Conclusions and Relevance AI-based diagnostic tools may serve as an adjunct, but not replacement, for clinical decision support of COVID-19 diagnosis, which largely hinges on exposure history, signs, and symptoms. While AI-based tools have not yet reached full diagnostic potential in COVID-19, they may still offer valuable information to clinicians taken into consideration along with clinical signs and symptoms.
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Affiliation(s)
- Ju Sun
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN
| | - Le Peng
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN
| | - Taihui Li
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN
| | - Dyah Adila
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN
| | - Zach Zaiman
- Department of Computer Science, Emory University, Atlanta, GA
| | - Genevieve B. Melton
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN
- Department of Surgery, University of Minnesota, Minneapolis, MN
| | - Nicholas Ingraham
- Department of Medicine, University of Minnesota, Division of Pulmonary and Critical Care, Minneapolis, MN
| | - Eric Murray
- M Health Fairview Informatics, Minneapolis, MN
| | - Daniel Boley
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN
| | - Sean Switzer
- Department of Medicine, University of Minnesota, Minneapolis, MN
| | - John L. Burns
- The School of Medicine, Indiana University, Indianapolis, IN
| | - Kun Huang
- The School of Medicine, Indiana University, Indianapolis, IN
| | - Tadashi Allen
- Department of Radiology, University of Minnesota, Minneapolis, MN
| | | | | | - Erich Kummerfeld
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN
| | - Christopher Tignanelli
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN
- Department of Surgery, University of Minnesota, Minneapolis, MN
- Department of Surgery, North Memorial Health Hospital, Robbinsdale, MN
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