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Adherence to Advisory Committee on Immunization Practices in diabetes mellitus patients in Saudi Arabia:A multicenter retrospective study

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World Journal of Diabetes 2024年第3期15卷 440-454页

作者： Saleh Fahad Alqifari Aya K Esmail Dalal M Alarifi Ghalya Y Alsuliman Maram M Alhati May R Mutlaq Mohammed Aldhaeefi Shaden A Alshuaibi Palanisamy Amirthalingam Abrar Abdallah Afaf S Wasel Heba R Hamad Shoroq Alamin Tasneem H Atia tariq alqahtani Department of Pharmacy Practice University of TabukTabuk 71491Saudi Arabia Department of Clinical Sciences Sulaiman Alrajhi UniversityQassim 52726Saudi Arabia Department of Family Medicine International Medical CenterJeddah 23214Saudi Arabia Clinical and Administrative Pharmacy Sciences College of PharmacyHoward UniversityWashingtonDC 20059United States Department of Pharmacology King Saud Bin Abdulaziz University for Health SciencesKing Abdullah International Medical Research CenterRiyadh 11481Saudi Arabia

BACKGROUND Patients with diabetes mellitus(DM)are predisposed to an increased risk of infection signifying the importance of vaccination to protect against its potentially severe *** Centers for Disease Control and Prevention/Advisory Committee on Immunization Practices(CDC/ACIP)issued immunization recommendations to protect this patient *** To assess the adherence of patients with DM to the CDC/ACIP immunization recommendations in Saudi Arabia and to identify the factors associated with the vaccine adherence *** An observational retrospective study conducted in 2023 was used to collect data on the vaccination records from 13 diabetes care centers in Saudi Arabia with 1000 eligible patients in phase I with data collected through chart review and 709 patients in phase II through online *** Among participants,10.01%(n=71)had never received any vaccine,while 85.89%(n=609)received at least one dose of the coronavirus disease 2019(COVID-19)vaccine,and 34.83%(n=247)had received the annual influenza *** 2.96%(n=21),2.11%(n=15),and 1.12%(n=8)received herpes zoster,tetanus,diphtheria,and pertussis(Tdap),and human papillomavirus(HPV)vaccines,*** patients with DM in Saudi Arabia,the rate of vaccination for annual influenza and COVID-19 vaccines was higher compared to other vaccinations such as herpes zoster,Tdap,pneumococcal,and *** such as vaccine recommendations provided by family physicians or specialists,site of care,income level,DM-related hospitalization history,residency site,hemoglobin A1c(HbA1c)level,and health sector type can significantly influence the vaccination rate in patients with *** non-vaccinated patients with DM,the most reported barriers were lack of knowledge and fear of side *** signifies the need for large-scale research in this area to identify additional factors that might facilitate adherence to CDC/ACIP vaccine recommendations in patients with *** In S

关键词： Diabetes mellitus Vaccine recommendation COVID-19 vaccine Influenza vaccine Pneumococcal vaccine Immunization Retrospective study

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Big Data Analytics with Optimal Deep Learning Model for Medical Image Classification

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Computer Systems Science & Engineering 2023年第2期44卷 1433-1449页

作者： tariq Mohammed alqahtani Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia

In recent years,huge volumes of healthcare data are getting generated in various *** advancements made in medical imaging are tremendous owing to which biomedical image acquisition has become easier and *** to such massive generation of big data,the utilization of new methods based on Big Data Analytics(BDA),Machine Learning(ML),and Artiﬁcial Intelligence(AI)have become *** this aspect,the current research work develops a new Big Data Analytics with Cat Swarm Optimization based deep Learning(BDA-CSODL)technique for medical image classiﬁcation on Apache Spark *** aim of the proposed BDA-CSODL technique is to classify the medical images and diagnose the disease ***-CSODL technique involves different stages of operations such as preprocessing,segmentation,fea-ture extraction,and classiﬁ*** addition,BDA-CSODL technique also fol-lows multi-level thresholding-based image segmentation approach for the detection of infected regions in medical ***,a deep convolutional neural network-based Inception v3 method is utilized in this study as feature *** Gradient Descent(SGD)model is used for parameter tuning ***,CSO with Long Short-Term Memory(CSO-LSTM)model is employed as a classiﬁcation model to determine the appropriate class labels to *** SGD and CSO design approaches help in improving the overall image classiﬁcation performance of the proposed BDA-CSODL technique.A wide range of simulations was conducted on benchmark medical image datasets and the com-prehensive comparative results demonstrate the supremacy of the proposed BDA-CSODL technique under different measures.

关键词： Big data analytics healthcare deep learning image classiﬁcation biomedical imaging machine learning

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Transparent and Accurate COVID-19 Diagnosis:Integrating Explainable AI with Advanced Deep Learning in CT Imaging

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 3101-3123页

作者： Mohammad Mehedi Hassan Salman A.alqahtani Mabrook S.AlRakhami Ahmed Zohier Elhendi Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Science Technology and Innovation Department King Saud UniversityRiyadh11543Saudi Arabia

In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly *** its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is *** bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning *** integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data *** approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model *** pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s *** unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic *** method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare *** innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.

关键词： Explainable AI COVID-19 CT images deep learning

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A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

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Computers, Materials & Continua 2024年第5期79卷 2945-2970页

作者： Muhammad Salman Qamar Ihsan ulHaq Amil Daraz Atif MAlamri Salman A.alqahtani Muhammad Fahad Munir Department of Electrical and Computer Engineering International Islamic UniversityIslamabad44000Pakistan School of Information Science and Engineering NingboTech UniversityNingbo315100China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable *** data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network *** mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring *** unique determination of this study is the shortest path to reach *** the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static *** this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the *** methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide *** addition,a method of using MS scheduling for efficient data collection is *** simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

关键词： Wireless Sensor Networks(WSNs) mobile sink(MS) rendezvous point(RP) machine learning Artificial Neural Networks(ANNs)

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Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

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Computers, Materials & Continua 2024年第7期80卷 1075-1104页

作者： Saad Akbar Humera Azam Sulaiman Sulmi Almutairi Omar alqahtani Habib Shah Aliya Aleryani Department of Computer Science University of KarachiKarachi75270Pakistan Department of Computing Faculty of Engineering Science and TechnologyHamdard UniversityKarachi75540Pakistan Department of Health Informatics College of Applied Medical SciencesQassim UniversityQassim51452Saudi Arabia Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature *** methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of ***,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)*** evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are *** 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 *** classification and 97%in distinguishing normal ***,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.

关键词： COVID-19 detection deep neural networks support vector machine CXIs InceptionV3 VGG16 VGG19 t-SNE embedding CLAHE attention mechanism XAI

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Congenital absence of the lateral meniscus:A case report

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World Journal of Orthopedics 2024年第5期15卷 477-482页

作者： Hamad Ali Alkhunayfir Abdulaziz Ali alqahtani Abdulrahman Jalwi Korkoman Department of Orthopedic Surgery Prince Muhammed bin Abdulaziz HospitalRiyadh 61422Saudi Arabia Department of Orthopedic Surgery Prince Sultan Military Medical CityRiyadh 12233Saudi Arabia Department of Orthopedic Surgery University of BishaBisha 67714Saudi Arabia

BACKGROUND Congenital absence of the menisci is a rare anatomical variation characterized by the absence or underdevelopment of one or both menisci in the knee *** menisci are crucial in load distribution,joint stability,and shock *** the clinical presentation,diagnosis,and management of this condition is important for optimal patient *** SUMMARY A 27-year-old male with a long-standing history of knee pain underwent diagnostic arthroscopy,revealing a congenital absence of the *** patient's clinical findings,imaging results,surgical procedures,and pertinent images are *** case presents a unique aspect with the congenital absence of the meniscus,contributing valuable insights to the literature on rare anatomical *** This case of congenital absence of the menisci highlights the diagnostic challenges posed by rare *** diagnostic arthroscopy played a crucial role in identifying the absence of the meniscus and providing an explanation for the patient's persistent knee *** case underscores the importance of individualized treatment approaches,including physical therapy,for optimal management of rare meniscal *** research is warranted to explore effective management strategies for the aforementioned cases and to expand our knowledge of these rare conditions.

关键词： Lateral meniscus Arthroscopy Knee joint Menisci reformation Magnetic resonance imaging Case report

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Using Flowable Composite as Gingival Retraction Cord in Prosthodontics Dentistry: Two Case Reports: A Dental Technique

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Open Journal of Stomatology 2024年第2期14卷 118-131页

作者： Fahad Hussain alqahtani Saudi Boards in Restorative Dentistry Center of Advanced Consultants in Healthcare Education and Training Riyadh Saudi Arabia

One of the most challenging factors affecting impression quality and good marginal fit is the gingival retraction method, which aims to separate the gingiva from the tooth to expose the finishing line of the prepared tooth. Bleeding and contamination from the crevicular fluid may compete for space with the impression material within the sulcus, leading to indistinct preparations and a lack of detail at the margin. In addition, such procedures might damage the PDL and cause permanent recession. Flowable composite cable, as opposed to traditional retraction cords, offers the proper marginal seal and visibility during the impression record because of its lower viscosity, improved adaptability, and elastic properties. The aim of this report is to present a brand-new, innovative method for controlling gingival hemorrhage in addition to the gingival retraction in restorative and prosthodontic treatment by utilizing a flowable composite during the final impression record procedure. In these case reports, the flowable composite cord technique is used instead of traditional gingival cords to modify and refine the gingival tissue before taking the traditional final impression or digital scan. Two cases were done for patients undergoing fixed prosthodontics and veneer treatment within a 2-year follow-up period. In addition, the flowable composite cord was used as temporary restoration before the final prosthetic cementation or under the provisional crowns. As a result, clean and healthy gingival tissues render cementation of the final prosthesis quicker and easier to perform. The patients in both cases claimed that their prosthodontic treatments, which involved the application of a flowable composite cord to temporarily retraction the gingiva, had produced good outcomes at the yearlong follow-up. In conclusion, the flowable composite cord technique is found to be a useful tool to improve the quality of impressions in fixed prosthodontics by providing excellent access for impre

关键词： Flowable Composite Gingival Retraction Methods Impression Material Marginal Integrity Final Cementation

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DNBP-CCA:A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

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Computers, Materials & Continua 2024年第5期79卷 2851-2878页

作者： Abdulwadood Alawadhi Mohd.Hasbullah Omar Abdullah Almogahed Noradila Nordin Salman A.alqahtani Atif M.Alamri InterNetWorks Research Laboratory School of ComputingUniversiti Utara MalaysiaSintok06010Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaParit RajaJohor86400Malaysia School of Games&Creative Technology University for the Creative ArtsFarnhamGU97DSUK Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.

关键词： Internet of Medical Things body area networks backoff period tsk fuzzy model clear channel assessment media access control

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Mental health status among COVID-19 patients survivors of critical illness in Saudi Arabia:A 6-month follow-up questionnaire study

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World Journal of Clinical Cases 2024年第15期12卷 2560-2567页

作者： Abdullah M Alhammad Namareq F Aldardeer Abdulaziz alqahtani Mohammad H Aljawadi Bushra Alnefaie Raghad Alonazi Mansour Almuqbil Abdulrahman Alsaadon Rakan M alqahtani Rashid Alballaa Bader Alshehri Mohammed I Alarifi Fahad D Alosaimi Department of Clinical Pharmacy College of PharmacyKing Saud UniversityRiyadh 12372Saudi Arabia Department of Pharmacy Services King Faisal Specialist Hospital and Research CentreJeddah 21499Saudi Arabia Corporate Department of Pharmacy Services King Saud University Medical CityRiyadh 12372Saudi Arabia College of Pharmacy King Saud UniversityRiyadh 12372Saudi Arabia Department of Critical Care Medicine College of MedicineKing Saud UniversityRiyadh 12372Saudi Arabia Department of Psychiatry College of MedicineKing Saud UniversityRiyadh 12372Saudi Arabia

BACKGROUND Psychological assessment after intensive care unit(ICU)discharge is increasingly used to assess patients'cognitive and psychological ***,few studies have examined those who recovered from coronavirus disease 2019(COVID-19).There is a paucity of data from the Middle East assessing the post-ICU discharge mental health status of patients who had *** To evaluate anxiety and depression among patients who had severe *** This is a prospective single-center follow-up questionnaire-based study of adults who were admitted to the ICU or under ICU consultation for>24 h for *** patients were contacted via *** patient’s anxiety and depression six months after ICU discharge were assessed using the Hospital Anxiety and Depression Scale(HADS).The primary outcome was the mean HADS *** secondary outcomes were risk factors of anxiety and/or *** Patients who were admitted to the ICU because of COVID-19 were screened(n=518).Of these,48 completed the *** mean age was 56.3±17.2 *** patients(62.5%)were *** main comorbidities were endocrine(n=24,50%)and cardiovascular(n=21,43.8%)*** mean overall HADS score for anxiety and depression at 6 months post-ICU discharge was 11.4(SD±8.5).A HADS score of>7 for anxiety and depression was detected in 15 patients(30%)and 18 patients(36%),*** from the multivariable ordered logistic regression demonstrated that vasopressor use was associated with the development of anxiety and depression[odds ratio(OR)39.06,95% confidence interval:1.309-1165.8;P<0.05].CONCLUSION Six months after ICU discharge,30% of patients who had COVID-19 demonstrated a HADS score that confirmed anxiety and *** compare the psychological status of patients following an ICU admission(with vs without COVID-19),further studies are warranted.

关键词： COVID-19 Critically ill Anxiety Depression HADS Saudi Arabia

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Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 3013-3032页

作者： Chunlin Xiong Zhengqiu Weng Jia Liu Liang Gu Fayez alqahtani Amr Gafar Pradip Kumar Sharma Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518052China Sangfor Technologies Inc. Shenzhen518055China School of Data Science and Artificial Intelligence Wenzhou University of TechnologyWenzhou325035China College of Computer Science&Technology Zhejiang University of TechnologyHangzhou310023China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Math&Computer Science Department Faculty of ScienceMenofia UniversityShebin El-KomEgypt Computing Science Department University of AberdeenAberdeenUK

With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protection of mobile users’privacy *** present,mobile user authenticationmethods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception,but there are still shortcomings such as low data collection efficiency,untrustworthy participating nodes,and lack of *** this end,this paper proposes a privacy-enhanced mobile user authentication method with motion sensors,which mainly includes:(1)Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication,reduce the probability of the model being bypassed by attackers,and reduce the overhead of data centralized processing and the risk of privacy leakage;(2)Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation;(3)Combine Variational Mode Decomposition(VMD)and Long Short-TermMemory(LSTM)to analyze and model the motion sensor data of mobile devices to improve the accuracy of model *** experimental results on the real environment dataset of 1513 people show that themethod proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.

关键词： Mobile authentication blockchain federated learning smart contract certificateless encryption VMD LSTM

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