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Messinis S, Temenos N, Protonotarios NE, Rallis I, Kalogeras D, Doulamis N. Enhancing Internet of Medical Things security with artificial intelligence: A comprehensive review. Comput Biol Med 2024; 170:108036. [PMID: 38295478 DOI: 10.1016/j.compbiomed.2024.108036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/08/2024] [Accepted: 01/26/2024] [Indexed: 02/02/2024]
Abstract
Over the past five years, interest in the literature regarding the security of the Internet of Medical Things (IoMT) has increased. Due to the enhanced interconnectedness of IoMT devices, their susceptibility to cyber-attacks has proportionally escalated. Motivated by the promising potential of AI-related technologies to improve certain cybersecurity measures, we present a comprehensive review of this emerging field. In this review, we attempt to bridge the corresponding literature gap regarding modern cybersecurity technologies that deploy AI techniques to improve their performance and compensate for security and privacy vulnerabilities. In this direction, we have systematically gathered and classified the extensive research on this topic. Our findings highlight the fact that the integration of machine learning (ML) and deep learning (DL) techniques improves both the performance of cybersecurity measures and their speed, reliability, and effectiveness. This may be proven to be useful for improving the security and privacy of IoMT devices. Furthermore, by considering the numerous advantages of AI technologies as opposed to their core cybersecurity counterparts, including blockchain, anomaly detection, homomorphic encryption, differential privacy, federated learning, and so on, we provide a structured overview of the current scientific trends. We conclude with considerations for future research, emphasizing the promising potential of AI-driven cybersecurity in the IoMT landscape, especially in patient data protection and in data-driven healthcare.
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Affiliation(s)
- Sotirios Messinis
- Institute of Communication and Computer Systems (ICCS), National Technical University of Athens, Athens, 15780, Greece.
| | - Nikos Temenos
- School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, Athens, 15780, Greece.
| | | | - Ioannis Rallis
- School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, Athens, 15780, Greece.
| | - Dimitrios Kalogeras
- Institute of Communication and Computer Systems (ICCS), National Technical University of Athens, Athens, 15780, Greece.
| | - Nikolaos Doulamis
- School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, Athens, 15780, Greece.
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2
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Gu Y, Yang X, Sun M, Wang C, Yang H, Yang C, Wang J, Kong G, Lv J, Zhang W. Graph-guided deep hashing networks for similar patient retrieval. Comput Biol Med 2024; 169:107865. [PMID: 38157772 DOI: 10.1016/j.compbiomed.2023.107865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/19/2023] [Accepted: 12/17/2023] [Indexed: 01/03/2024]
Abstract
With the rapid growth and widespread application of electronic health records (EHRs), similar patient retrieval has become an important task for downstream clinical decision support such as diagnostic reference, treatment planning, etc. However, the high dimensionality, large volume, and heterogeneity of EHRs pose challenges to the efficient and accurate retrieval of patients with similar medical conditions to the current case. Several previous studies have attempted to alleviate these issues by using hash coding techniques, improving retrieval efficiency but merely exploring underlying characteristics among instances to preserve retrieval accuracy. In this paper, drug categories of instances recorded in EHRs are regarded as the ground truth to determine the pairwise similarity, and we consider the abundant semantic information within such multi-labels and propose a novel framework named Graph-guided Deep Hashing Networks (GDHN). To capture correlation dependencies among the multi-labels, we first construct a label graph where each node represents a drug category, then a graph convolution network (GCN) is employed to derive the multi-label embedding of each instance. Thus, we can utilize the learned multi-label embeddings to guide the patient hashing process to obtain more informative and discriminative hash codes. Extensive experiments have been conducted on two datasets, including a real-world dataset concerning IgA nephropathy from Peking University First Hospital, and a publicly available dataset from MIMIC-III, compared with traditional hashing methods and state-of-the-art deep hashing methods using three evaluation metrics. The results demonstrate that GDHN outperforms the competitors at different hash code lengths, validating the superiority of our proposal.
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Affiliation(s)
- Yifan Gu
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China; State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Xuebing Yang
- State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Mengxuan Sun
- State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Chutong Wang
- State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Hongyu Yang
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Chao Yang
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Jinwei Wang
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Guilan Kong
- National Institute of Health Data Science, Peking University, Beijing, China; Advanced Institute of Information Technology, Peking University, Hangzhou, China
| | - Jicheng Lv
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China.
| | - Wensheng Zhang
- State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences, Beijing, China; Guangzhou University, Guangzhou, China.
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Agarwal V, Bajpai M. Imaging and Non-imaging Analytical Techniques Used for Drug Nanosizing and their Patents: An Overview. RECENT PATENTS ON NANOTECHNOLOGY 2024; 18:494-518. [PMID: 37953622 DOI: 10.2174/0118722105243388230920013508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 07/06/2023] [Accepted: 07/18/2023] [Indexed: 11/14/2023]
Abstract
BACKGROUND Nanosizing is widely recognized as an effective technique for improving the solubility, dissolution rate, onset of action, and bioavailability of poorly water-soluble drugs. To control the execution and behavior of the output product, more advanced and valuable analytical techniques are required. OBJECTIVE The primary intent of this review manuscript was to furnish the understanding of imaging and non-imaging techniques related to nanosizing analysis by focusing on related patents. In addition, the study also aimed to collect and illustrate the information on various classical (laser diffractometry, photon correlation spectroscopy, zeta potential, laser Doppler electrophoresis, X-ray diffractometry, differential scanning calorimeter, scanning electron microscopy, transmission electron microscopy), new, and advanced analytical techniques (improved dynamic light scattering method, Brunauer-Emmett- Teller method, ultrasonic attenuation, biosensor), as well as commercial techniques, like inductively coupled plasma mass spectroscopy, aerodynamic particle sizer, scanning mobility particle sizer, and matrix- assisted laser desorption/ionization mass spectroscopy, which all relate to nano-sized particles. METHODS The present manuscript has taken a fresh look at the various aspects of the analytical techniques utilized in the process of nanosizing, and has achieved this through the analysis of a wide range of peer-reviewed literature. All summarized literature studies provide the information that can meet the basic needs of nanotechnology. RESULTS A variety of analytical techniques related to the nanosizing process have already been established and have great potential to weed out several issues. However, the current scenarios require more relevant, accurate, and advanced analytical techniques that can minimize the time and deviations associated with different instrumental and process parameters. To meet this requirement, some new and more advanced analytical techniques have recently been discovered, like ultrasonic attenuation technique, BET technique, biosensors, etc. Conclusion: The present overview certifies the significance of different analytical techniques utilized in the nanosizing process. The overview also provides information on various patents related to sophisticated analytical tools that can meet the needs of such an advanced field. The data show that the nanotechnology field will flourish in the coming future.
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Affiliation(s)
- Vijay Agarwal
- Rajkumar Goel Institute of Technology (Pharmacy), Delhi-Meerut Road, Ghaziabad, UP, India
| | - Meenakshi Bajpai
- Institute of Pharmaceutical Research, G.L.A. University, Mathura-Delhi Road, Mathura, UP, India
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Aminizadeh S, Heidari A, Toumaj S, Darbandi M, Navimipour NJ, Rezaei M, Talebi S, Azad P, Unal M. The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 241:107745. [PMID: 37579550 DOI: 10.1016/j.cmpb.2023.107745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 07/15/2023] [Accepted: 08/02/2023] [Indexed: 08/16/2023]
Abstract
Medical data processing has grown into a prominent topic in the latest decades with the primary goal of maintaining patient data via new information technologies, including the Internet of Things (IoT) and sensor technologies, which generate patient indexes in hospital data networks. Innovations like distributed computing, Machine Learning (ML), blockchain, chatbots, wearables, and pattern recognition can adequately enable the collection and processing of medical data for decision-making in the healthcare era. Particularly, to assist experts in the disease diagnostic process, distributed computing is beneficial by digesting huge volumes of data swiftly and producing personalized smart suggestions. On the other side, the current globe is confronting an outbreak of COVID-19, so an early diagnosis technique is crucial to lowering the fatality rate. ML systems are beneficial in aiding radiologists in examining the incredible amount of medical images. Nevertheless, they demand a huge quantity of training data that must be unified for processing. Hence, developing Deep Learning (DL) confronts multiple issues, such as conventional data collection, quality assurance, knowledge exchange, privacy preservation, administrative laws, and ethical considerations. In this research, we intend to convey an inclusive analysis of the most recent studies in distributed computing platform applications based on five categorized platforms, including cloud computing, edge, fog, IoT, and hybrid platforms. So, we evaluated 27 articles regarding the usage of the proposed framework, deployed methods, and applications, noting the advantages, drawbacks, and the applied dataset and screening the security mechanism and the presence of the Transfer Learning (TL) method. As a result, it was proved that most recent research (about 43%) used the IoT platform as the environment for the proposed architecture, and most of the studies (about 46%) were done in 2021. In addition, the most popular utilized DL algorithm was the Convolutional Neural Network (CNN), with a percentage of 19.4%. Hence, despite how technology changes, delivering appropriate therapy for patients is the primary aim of healthcare-associated departments. Therefore, further studies are recommended to develop more functional architectures based on DL and distributed environments and better evaluate the present healthcare data analysis models.
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Affiliation(s)
| | - Arash Heidari
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran; Department of Software Engineering, Haliç University, Istanbul, Turkiye.
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
| | - Mehdi Darbandi
- Department of Electrical and Electronic Engineering, Eastern Mediterranean University, Gazimagusa 99628, Turkiye
| | - Nima Jafari Navimipour
- Department of Computer Engineering, Kadir Has University, Istanbul, Turkiye; Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin 64002, Taiwan.
| | - Mahsa Rezaei
- Tabriz University of Medical Sciences, Faculty of Surgery, Tabriz, Iran
| | - Samira Talebi
- Department of Computer Science, University of Texas at San Antonio, TX, USA
| | - Poupak Azad
- Department of Computer Science, University of Manitoba, Winnipeg, Canada
| | - Mehmet Unal
- Department of Computer Engineering, Nisantasi University, Istanbul, Turkiye
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5
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Ran AR, Wang X, Chan PP, Wong MOM, Yuen H, Lam NM, Chan NCY, Yip WWK, Young AL, Yung HW, Chang RT, Mannil SS, Tham YC, Cheng CY, Wong TY, Pang CP, Heng PA, Tham CC, Cheung CY. Developing a privacy-preserving deep learning model for glaucoma detection: a multicentre study with federated learning. Br J Ophthalmol 2023:bjo-2023-324188. [PMID: 37857452 DOI: 10.1136/bjo-2023-324188] [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: 07/03/2023] [Accepted: 09/23/2023] [Indexed: 10/21/2023]
Abstract
BACKGROUND Deep learning (DL) is promising to detect glaucoma. However, patients' privacy and data security are major concerns when pooling all data for model development. We developed a privacy-preserving DL model using the federated learning (FL) paradigm to detect glaucoma from optical coherence tomography (OCT) images. METHODS This is a multicentre study. The FL paradigm consisted of a 'central server' and seven eye centres in Hong Kong, the USA and Singapore. Each centre first trained a model locally with its own OCT optic disc volumetric dataset and then uploaded its model parameters to the central server. The central server used FedProx algorithm to aggregate all centres' model parameters. Subsequently, the aggregated parameters are redistributed to each centre for its local model optimisation. We experimented with three three-dimensional (3D) networks to evaluate the stabilities of the FL paradigm. Lastly, we tested the FL model on two prospectively collected unseen datasets. RESULTS We used 9326 volumetric OCT scans from 2785 subjects. The FL model performed consistently well with different networks in 7 centres (accuracies 78.3%-98.5%, 75.9%-97.0%, and 78.3%-97.5%, respectively) and stably in the 2 unseen datasets (accuracies 84.8%-87.7%, 81.3%-84.8%, and 86.0%-87.8%, respectively). The FL model achieved non-inferior performance in classifying glaucoma compared with the traditional model and significantly outperformed the individual models. CONCLUSION The 3D FL model could leverage all the datasets and achieve generalisable performance, without data exchange across centres. This study demonstrated an OCT-based FL paradigm for glaucoma identification with ensured patient privacy and data security, charting another course toward the real-world transition of artificial intelligence in ophthalmology.
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Affiliation(s)
- An Ran Ran
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Xi Wang
- Zhejiang Lab, Hangzhou, China
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Poemen P Chan
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- Hong Kong Eye Hospital, Hong Kong SAR, China
| | | | - Hunter Yuen
- Hong Kong Eye Hospital, Hong Kong SAR, China
| | - Nai Man Lam
- Hong Kong Eye Hospital, Hong Kong SAR, China
| | - Noel C Y Chan
- Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong SAR, China
- Ophthalmology and Visual Sciences, Alice Ho Miu Ling Nethersole Hospital, Hong Kong SAR, China
| | - Wilson W K Yip
- Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong SAR, China
- Ophthalmology and Visual Sciences, Alice Ho Miu Ling Nethersole Hospital, Hong Kong SAR, China
| | - Alvin L Young
- Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong SAR, China
- Ophthalmology and Visual Sciences, Alice Ho Miu Ling Nethersole Hospital, Hong Kong SAR, China
| | | | - Robert T Chang
- Ophthalmology, Stanford University School of Medicine, Stanford, California, USA
| | - Suria S Mannil
- Ophthalmology, Stanford University School of Medicine, Stanford, California, USA
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-National University of Singapore Medical School, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-National University of Singapore Medical School, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Tsinghua University, Beijing, China
- School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Chi Pui Pang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Pheng-Ann Heng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Clement C Tham
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- Hong Kong Eye Hospital, Hong Kong SAR, China
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
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6
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Wu TC, Ho CTB. Blockchain Revolutionizing in Emergency Medicine: A Scoping Review of Patient Journey through the ED. Healthcare (Basel) 2023; 11:2497. [PMID: 37761695 PMCID: PMC10530815 DOI: 10.3390/healthcare11182497] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/29/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Blockchain technology has revolutionized the healthcare sector, including emergency medicine, by integrating AI, machine learning, and big data, thereby transforming traditional healthcare practices. The increasing utilization and accumulation of personal health data also raises concerns about security and privacy, particularly within emergency medical settings. METHOD Our review focused on articles published in databases such as Web of Science, PubMed, and Medline, discussing the revolutionary impact of blockchain technology within the context of the patient journey through the ED. RESULTS A total of 33 publications met our inclusion criteria. The findings emphasize that blockchain technology primarily finds its applications in data sharing and documentation. The pre-hospital and post-discharge applications stand out as distinctive features compared to other disciplines. Among various platforms, Ethereum and Hyperledger Fabric emerge as the most frequently utilized options, while Proof of Work (PoW) and Proof of Authority (PoA) stand out as the most commonly employed consensus algorithms in this emergency care domain. The ED journey map and two scenarios are presented, exemplifying the most distinctive applications of emergency medicine, and illustrating the potential of blockchain. Challenges such as interoperability, scalability, security, access control, and cost could potentially arise in emergency medical contexts, depending on the specific scenarios. CONCLUSION Our study examines the ongoing research on blockchain technology, highlighting its current influence and potential future advancements in optimizing emergency medical services. This approach empowers frontline medical professionals to validate their practices and recognize the transformative potential of blockchain in emergency medical care, ultimately benefiting both patients and healthcare providers.
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Affiliation(s)
- Tzu-Chi Wu
- Institute of Technology Management, National Chung-Hsing University, Taichung 40227, Taiwan;
- Department of Emergency Medicine, Show Chwan Memorial Hospital, Changhua 500009, Taiwan
| | - Chien-Ta Bruce Ho
- Institute of Technology Management, National Chung-Hsing University, Taichung 40227, Taiwan;
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7
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Santosh KC, GhoshRoy D, Nakarmi S. A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022. Healthcare (Basel) 2023; 11:2388. [PMID: 37685422 PMCID: PMC10486542 DOI: 10.3390/healthcare11172388] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/16/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
The emergence of the COVID-19 pandemic in Wuhan in 2019 led to the discovery of a novel coronavirus. The World Health Organization (WHO) designated it as a global pandemic on 11 March 2020 due to its rapid and widespread transmission. Its impact has had profound implications, particularly in the realm of public health. Extensive scientific endeavors have been directed towards devising effective treatment strategies and vaccines. Within the healthcare and medical imaging domain, the application of artificial intelligence (AI) has brought significant advantages. This study delves into peer-reviewed research articles spanning the years 2020 to 2022, focusing on AI-driven methodologies for the analysis and screening of COVID-19 through chest CT scan data. We assess the efficacy of deep learning algorithms in facilitating decision making processes. Our exploration encompasses various facets, including data collection, systematic contributions, emerging techniques, and encountered challenges. However, the comparison of outcomes between 2020 and 2022 proves intricate due to shifts in dataset magnitudes over time. The initiatives aimed at developing AI-powered tools for the detection, localization, and segmentation of COVID-19 cases are primarily centered on educational and training contexts. We deliberate on their merits and constraints, particularly in the context of necessitating cross-population train/test models. Our analysis encompassed a review of 231 research publications, bolstered by a meta-analysis employing search keywords (COVID-19 OR Coronavirus) AND chest CT AND (deep learning OR artificial intelligence OR medical imaging) on both the PubMed Central Repository and Web of Science platforms.
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Affiliation(s)
- KC Santosh
- 2AI: Applied Artificial Intelligence Research Lab, Vermillion, SD 57069, USA
| | - Debasmita GhoshRoy
- School of Automation, Banasthali Vidyapith, Tonk 304022, Rajasthan, India;
| | - Suprim Nakarmi
- Department of Computer Science, University of South Dakota, Vermillion, SD 57069, USA;
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8
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Di Salvatore V, Crispino E, Maleki A, Nicotra G, Russo G, Pappalardo F. Computational identification of differentially-expressed genes as suggested novel COVID-19 biomarkers: A bioinformatics analysis of expression profiles. Comput Struct Biotechnol J 2023; 21:3339-3354. [PMID: 37347079 PMCID: PMC10259169 DOI: 10.1016/j.csbj.2023.06.007] [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: 02/01/2023] [Revised: 06/07/2023] [Accepted: 06/07/2023] [Indexed: 06/23/2023] Open
Abstract
COVID-19 was declared a pandemic in March 2020, and since then, it has not stopped spreading like wildfire in almost every corner of the world, despite the many efforts made to stem its spread. SARS-CoV-2 has one of the biggest genomes among RNA viruses and presents unique characteristics that differentiate it from other coronaviruses, making it even more challenging to find a cure or vaccine that is efficient enough. This work aims, using RNA sequencing (RNA-Seq) data, to evaluate whether the expression of specific human genes in the host can vary in different grades of disease severity and to determine the molecular origins of the differences in response to SARS-CoV-2 infection in different patients. In addition to quantifying gene expression, data coming from RNA-Seq allow for the discovery of new transcripts, the identification of alternative splicing events, the detection of allele-specific expression, and the detection of post-transcriptional alterations. For this reason, we performed differential expression analysis on different expression profiles of COVID-19 patients, using RNA-Seq data coming from NCBI public repository, and we obtained the lists of all differentially expressed genes (DEGs) emerging from 7 experimental conditions. We performed a Gene Set Enrichment Analysis (GSEA) on these genes to find possible correlations between DEGs and known disease phenotypes. We mainly focused on DEGs coming out from the analysis of the contrasts involving severe conditions to infer any possible relation between a worsening of the clinical picture and an over-representation of specific genes. Based on the obtained results, this study indicates a small group of genes that result up-regulated in the severe form of the disease. EXOSC5, MESD, REXO2, and TRMT2A genes are not differentially expressed or not present in the other conditions, being for that reason, good biomarkers candidates for the severe form of COVID-19 disease. The use of specific over-expressed genes, whether up-regulated or down-regulated, which have an individual role in each different condition of COVID-19 as a biomarker, can assist in early diagnosis.
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Affiliation(s)
| | - Elena Crispino
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
| | - Avisa Maleki
- Department of Mathematics and Computer Science, University of Catania, Catania, Italy
| | - Giulia Nicotra
- Department of Drug and Health Sciences, University of Catania, Catania, Italy
| | - Giulia Russo
- Department of Drug and Health Sciences, University of Catania, Catania, Italy
- Mimesis SRL, Catania, Italy
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9
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Chakraborty GS, Batra S, Singh A, Muhammad G, Torres VY, Mahajan M. A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling. Diagnostics (Basel) 2023; 13:diagnostics13101806. [PMID: 37238290 DOI: 10.3390/diagnostics13101806] [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: 04/07/2023] [Revised: 05/16/2023] [Accepted: 05/17/2023] [Indexed: 05/28/2023] Open
Abstract
COVID-19 is an infectious disease caused by the deadly virus SARS-CoV-2 that affects the lung of the patient. Different symptoms, including fever, muscle pain and respiratory syndrome, can be identified in COVID-19-affected patients. The disease needs to be diagnosed in a timely manner, otherwise the lung infection can turn into a severe form and the patient's life may be in danger. In this work, an ensemble deep learning-based technique is proposed for COVID-19 detection that can classify the disease with high accuracy, efficiency, and reliability. A weighted average ensemble (WAE) prediction was performed by combining three CNN models, namely Xception, VGG19 and ResNet50V2, where 97.25% and 94.10% accuracy was achieved for binary and multiclass classification, respectively. To accurately detect the disease, different test methods have been proposed and developed, some of which are even being used in real-time situations. RT-PCR is one of the most successful COVID-19 detection methods, and is being used worldwide with high accuracy and sensitivity. However, complexity and time-consuming manual processes are limitations of this method. To make the detection process automated, researchers across the world have started to use deep learning to detect COVID-19 applied on medical imaging. Although most of the existing systems offer high accuracy, different limitations, including high variance, overfitting and generalization errors, can be found that can degrade the system performance. Some of the reasons behind those limitations are a lack of reliable data resources, missing preprocessing techniques, a lack of proper model selection, etc., which eventually create reliability issues. Reliability is an important factor for any healthcare system. Here, transfer learning with better preprocessing techniques applied on two benchmark datasets makes the work more reliable. The weighted average ensemble technique with hyperparameter tuning ensures better accuracy than using a randomly selected single CNN model.
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Affiliation(s)
- Gouri Shankar Chakraborty
- Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India
| | - Salil Batra
- Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India
| | - Aman Singh
- Higher Polytechnic School, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain
- Department of Engineering, Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
- Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, Uttarakhand, India
| | - Ghulam Muhammad
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Vanessa Yelamos Torres
- Department of Engineering, Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
- Engineering Research & Innovation Group, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain
- Department of Project Management, Universidad Internacional Iberoamericana, Campeche C.P. 24560, Mexico
| | - Makul Mahajan
- Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India
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10
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Rezazadeh B, Asghari P, Rahmani AM. Computer-aided methods for combating Covid-19 in prevention, detection, and service provision approaches. Neural Comput Appl 2023; 35:14739-14778. [PMID: 37274420 PMCID: PMC10162652 DOI: 10.1007/s00521-023-08612-y] [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: 07/27/2022] [Accepted: 04/11/2023] [Indexed: 06/06/2023]
Abstract
The infectious disease Covid-19 has been causing severe social, economic, and human suffering across the globe since 2019. The countries have utilized different strategies in the last few years to combat Covid-19 based on their capabilities, technological infrastructure, and investments. A massive epidemic like this cannot be controlled without an intelligent and automatic health care system. The first reaction to the disease outbreak was lockdown, and researchers focused more on developing methods to diagnose the disease and recognize its behavior. However, as the new lifestyle becomes more normalized, research has shifted to utilizing computer-aided methods to monitor, track, detect, and treat individuals and provide services to citizens. Thus, the Internet of things, based on fog-cloud computing, using artificial intelligence approaches such as machine learning, and deep learning are practical concepts. This article aims to survey computer-based approaches to combat Covid-19 based on prevention, detection, and service provision. Technically and statistically, this article analyzes current methods, categorizes them, presents a technical taxonomy, and explores future and open issues.
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Affiliation(s)
- Bahareh Rezazadeh
- Computer Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Parvaneh Asghari
- Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Amir Masoud Rahmani
- Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002 Taiwan
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11
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Mohammedqasem R, Mohammedqasim H, Asad Ali Biabani S, Ata O, Alomary MN, Almehmadi M, Amer Alsairi A, Azam Ansari M. Multi-objective deep learning framework for COVID-19 dataset problems. JOURNAL OF KING SAUD UNIVERSITY. SCIENCE 2023; 35:102527. [PMID: 36590237 PMCID: PMC9795799 DOI: 10.1016/j.jksus.2022.102527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 12/08/2022] [Accepted: 12/23/2022] [Indexed: 05/28/2023]
Abstract
Background It has been reported that a deadly virus known as COVID-19 has arisen in China and has spread rapidly throughout the country. The globe was shattered, and a large number of people on the planet died. It quickly became an epidemic due to the absence of apparent symptoms and causes for patients, confusion appears due to the lack of sufficient laboratory results, and its intelligent algorithms were used to make decisions on clinical outcomes. Methods This study developed a new framework for medical datasets with high missing values based on deep-learning optimization models. The robustness of our model is achieved by combining: Data Missing Care (DMC) Framework to overcome the problem of high missing data in medical datasets, and Grid-Search optimization used to develop an improved deep predictive training model for patients with COVID-19 by setting multiple hyperparameters and tuning assessments on three deep learning algorithms: ANN (Artificial Neural Network), CNN (Convolutional Neural Network), and Recurrent Neural Networks (RNN). Results The experiment results conducted on three medical datasets showed the effectiveness of our hybrid approach and an improvement in accuracy and efficiency since all the evaluation metrics were close to ideal for all deep learning classifiers. We got the best evaluation in terms of accuracy 98%, precession 98.5%, F1-score 98.6%, and ROC Curve (95% to 99%) for the COVID-19 dataset provided by GitHub. The second dataset is also Covid-19 provided by Albert Einstein Hospital with high missing data after applying our approach the accuracy reached more than 91%. Third dataset for Cervical Cancer provided by Kaggle all the evaluation metrics reached more than 95%. Conclusions The proposed formula for processing this type of data can replace the traditional formats in optimization while providing high accuracy and less time to classify patients. Whereas, the experimental results of our approach, supported by comprehensive statistical analysis, can improve the overall evaluation performance of the problem of classifying medical data sets with high missing values. Therefore, this approach can be used in many areas such as energy management, environment, and medicine.
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Affiliation(s)
- Roa'a Mohammedqasem
- Department of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey
| | - Hayder Mohammedqasim
- Department of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey
| | - Sardar Asad Ali Biabani
- Science and Technology Unit, Umm Al- Qura University, Makkah, Saudi Arabia & Deanship of Scientific Research, Umm Al- Qura University, Makkah, Saudi Arabia
| | - Oguz Ata
- Department of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey
| | - Mohammad N Alomary
- National Centre for Biotechnology, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia
| | - Mazen Almehmadi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Ahad Amer Alsairi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Mohammad Azam Ansari
- Department of Epidemic Disease Research, Institute for Research & Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
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12
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Coutinho MG, Câmara GB, Barbosa RDM, Fernandes MA. SARS-CoV-2 virus classification based on stacked sparse autoencoder. Comput Struct Biotechnol J 2023; 21:284-298. [PMCID: PMC9742810 DOI: 10.1016/j.csbj.2022.12.007] [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: 06/21/2022] [Revised: 12/04/2022] [Accepted: 12/05/2022] [Indexed: 12/13/2022] Open
Abstract
Since December 2019, the world has been intensely affected by the COVID-19 pandemic, caused by the SARS-CoV-2. In the case of a novel virus identification, the early elucidation of taxonomic classification and origin of the virus genomic sequence is essential for strategic planning, containment, and treatments. Deep learning techniques have been successfully used in many viral classification problems associated with viral infection diagnosis, metagenomics, phylogenetics, and analysis. Considering that motivation, the authors proposed an efficient viral genome classifier for the SARS-CoV-2 using the deep neural network based on the stacked sparse autoencoder (SSAE). For the best performance of the model, we explored the utilization of image representations of the complete genome sequences as the SSAE input to provide a classification of the SARS-CoV-2. For that, a dataset based on k-mers image representation was applied. We performed four experiments to provide different levels of taxonomic classification of the SARS-CoV-2. The SSAE technique provided great performance results in all experiments, achieving classification accuracy between 92% and 100% for the validation set and between 98.9% and 100% when the SARS-CoV-2 samples were applied for the test set. In this work, samples of the SARS-CoV-2 were not used during the training process, only during subsequent tests, in which the model was able to infer the correct classification of the samples in the vast majority of cases. This indicates that our model can be adapted to classify other emerging viruses. Finally, the results indicated the applicability of this deep learning technique in genome classification problems.
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Affiliation(s)
- Maria G.F. Coutinho
- Laboratory of Machine Learning and Intelligent Instrumentation, IMD/nPITI, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Gabriel B.M. Câmara
- Laboratory of Machine Learning and Intelligent Instrumentation, IMD/nPITI, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Raquel de M. Barbosa
- Department of Pharmacy and Pharmaceutical Technology, University of Granada, 18071 Granada, Spain
| | - Marcelo A.C. Fernandes
- Laboratory of Machine Learning and Intelligent Instrumentation, IMD/nPITI, Federal University of Rio Grande do Norte, Natal, Brazil,Department of Computer and Automation Engineering, Federal University of Rio Grande do Norte, Natal, Brazil,Corresponding author at: Department of Computer and Automation Engineering, Federal University of Rio Grande do Norte, Natal, Brazil
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13
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A Multimodal Deep Learning Approach to Predicting Systemic Diseases from Oral Conditions. Diagnostics (Basel) 2022; 12:diagnostics12123192. [PMID: 36553200 PMCID: PMC9777898 DOI: 10.3390/diagnostics12123192] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Background: It is known that oral diseases such as periodontal (gum) disease are closely linked to various systemic diseases and disorders. Deep learning advances have the potential to make major contributions to healthcare, particularly in the domains that rely on medical imaging. Incorporating non-imaging information based on clinical and laboratory data may allow clinicians to make more comprehensive and accurate decisions. Methods: Here, we developed a multimodal deep learning method to predict systemic diseases and disorders from oral health conditions. A dual-loss autoencoder was used in the first phase to extract periodontal disease-related features from 1188 panoramic radiographs. Then, in the second phase, we fused the image features with the demographic data and clinical information taken from electronic health records (EHR) to predict systemic diseases. We used receiver operation characteristics (ROC) and accuracy to evaluate our model. The model was further validated by an unseen test dataset. Findings: According to our findings, the top three most accurately predicted chapters, in order, are the Chapters III, VI and IX. The results indicated that the proposed model could predict systemic diseases belonging to Chapters III, VI and IX, with AUC values of 0.92 (95% CI, 0.90-94), 0.87 (95% CI, 0.84-89) and 0.78 (95% CI, 0.75-81), respectively. To assess the robustness of the models, we performed the evaluation on the unseen test dataset for these chapters and the results showed an accuracy of 0.88, 0.82 and 0.72 for Chapters III, VI and IX, respectively. Interpretation: The present study shows that the combination of panoramic radiograph and clinical oral features could be considered to train a fusion deep learning model for predicting systemic diseases and disorders.
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Wu Z, Xuan S, Xie J, Lin C, Lu C. How to ensure the confidentiality of electronic medical records on the cloud: A technical perspective. Comput Biol Med 2022; 147:105726. [PMID: 35759991 DOI: 10.1016/j.compbiomed.2022.105726] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 06/08/2022] [Accepted: 06/11/2022] [Indexed: 11/30/2022]
Abstract
From a technical perspective, for electronic medical records (EMR), this paper proposes an effective confidential management solution on the cloud, whose basic idea is to deploy a trusted local server between the untrusted cloud and each trusted client of a medical information management system, responsible for running an EMR cloud hierarchical storage model and an EMR cloud segmentation query model. (1) The EMR cloud hierarchical storage model is responsible for storing light EMR data items (such as patient basic information) on the local server, while encrypting heavy EMR data items (such as patient medical images) and storing them on the cloud, to ensure the confidentiality of electronic medical records on the cloud. (2) The EMR cloud segmentation query model performs EMR related query operations through the collaborative interaction between the local server and the cloud server, to ensure the accuracy and efficiency of each EMR query statement. Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed solution for confidentiality management of electronic medical records on the cloud, i.e., which can ensure the confidentiality of electronic medical records on the untrusted cloud, without compromising the availability of an existing medical information management system.
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Affiliation(s)
- Zongda Wu
- Department of Computer Science and Engineering, Shaoxing University, Shaoxing, 312000, Zhejiang, China.
| | - Shaolong Xuan
- Department of Computer Science and Engineering, Shaoxing University, Shaoxing, 312000, Zhejiang, China.
| | - Jian Xie
- Department of Computer Science and Engineering, Shaoxing University, Shaoxing, 312000, Zhejiang, China.
| | - Chongze Lin
- Zhejiang Economics Information Centre, Hangzhou, 310006, Zhejiang, China.
| | - Chenglang Lu
- Zhejiang Institute of Mechanical and Electrical Engineering, Hangzhou, 310053, Zhejiang, China.
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15
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Heidari A, Jafari Navimipour N, Unal M, Toumaj S. Machine learning applications for COVID-19 outbreak management. Neural Comput Appl 2022; 34:15313-15348. [PMID: 35702664 PMCID: PMC9186489 DOI: 10.1007/s00521-022-07424-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 05/10/2022] [Indexed: 12/29/2022]
Abstract
Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.
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Affiliation(s)
- Arash Heidari
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
- Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran
| | | | - Mehmet Unal
- Department of Computer Engineering, Nisantasi University, Istanbul, Turkey
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
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