Preparation and Properties of pH-Responsive Intelligent Medical Dressing Based on Bacterial Cellulose/Polyacrylic Acid

Bacterial cellulose/polyacrylic acid (BC/PAA) pH-responsive hydrogels were prepared by free-radical polymerization (in situ) using BC as the raw material and AA as the monomer. The hydrogels were loaded with curcumin (Cur) to prepare pH-responsive intelligent medical dressings. The preparation process of the hydrogels was optimized by a single factor and response surface experiment using their swelling degree as an index. The structures of BC/PAA pH-responsive hydrogels were characterized by scanning electron microscope (SEM), Fourier Transform Infrared spectrometer (FTIR), X-ray diffraction (XRD), and tensile tester, and the swelling properties, mechanical properties, bacteriostatic properties, and drug release behavior were investigated. The results showed that the BC/PAA pH-responsive hydrogel has a three-dimensional network structure with the swelling rate up to 1600 g/g, compressive strength of up to 8 KPa, and good mechanical properties, and the drug release behavior was in line with the logistic dynamics model, and it has good inhibitory effects on common pathogens of wound infection: E. coli, S. aureus, and P. aeruginosa.

Intelligent Networked Control of Vasoactive Drug Infusion for Patients with Uncertain Sensitivity

Abnormal high blood pressure or hypertension is still the leading risk factor for death and disability worldwide.This paper presents a new intelligent networked control of medical drug infusion system to regulate the mean arterial blood pressure for hypertensive patients with different health status conditions.The infusion of vasoactive drugs to patients endures various issues,such as variation of sensitivity and noise,which require effective and powerful systems to ensure robustness and good performance.The developed intelligent networked system is composed of a hybrid control scheme of interval type-2 fuzzy(IT2F)logic and teaching-learning-based optimization(TLBO)algorithm.This networked IT2F control is capable of managing the uncertain sensitivity of the patient to anti-hypertensive drugs successfully.To avoid the manual selection of control parameter values,the TLBO algorithm is mainly used to automatically find the best parameter values of the networked IT2F controller.The simulation results showed that the optimized networked IT2F achieved a good performance under external disturbances.A comparative study has also been conducted to emphasize the outperformance of the developed controller against traditional PID and type-1 fuzzy controllers.Moreover,the comparative evaluation demonstrated that the performance of the developed networked IT2F controller is superior to other control strategies in previous studies to handle unknown patients’sensitivity to infused vasoactive drugs in a noisy environment.

Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient manner.Identifying different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI images.Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification.These methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI images.Utilizing the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor classification.Our approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and Engineering,showcasing a highly accurate framework with significant potential for healthcare technologies.The model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification report.This successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current methods.The integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider application.This research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

A review of medical artificial intelligence

Since the concept of“artificial intelligence”was introduced in 1956,it has led to numerous technological innovations in human medicine and completely changed the traditional model of medicine.In this study,we mainly explain the application of artificial intelligence in various fields of medicine from four aspects:machine learning,intelligent robot,image recognition technology,and expert system.In addition,we discuss the existing problems and future trends in these areas.In recent years,through the development of globalization,various research institutions around the world has conducted a number of researches on this subject.Therefore,medical artificial intelligence has attained significant breakthroughs and will demonstrate wide development prospection in the future.

Design of an Information Security Service for Medical Artificial Intelligence

The medical convergence industry has gradually adopted ICT devices,which has led to legacy security problems related to ICT devices.However,it has been difficult to solve these problems due to data resource issues.Such problems can cause a lack of reliability in medical artificial intelligence services that utilize medical information.Therefore,to provide reliable services focused on security internalization,it is necessary to establish a medical convergence environment-oriented security management system.This study proposes the use of system identification and countermeasures to secure systemreliabilitywhen using medical convergence environment information in medical artificial intelligence.We checked the life cycle of medical information and the flow and location of information,analyzed the security threats that may arise during the life cycle,and proposed technical countermeasures to overcome such threats.We verified the proposed countermeasures through a survey of experts.Security requirements were defined based on the information life cycle in the medical convergence environment.We also designed technical countermeasures for use in the security management systems of hospitals of diverse sizes.

New Year's greeting and overview of Artificial Intelligence in Medical Imaging in 2021

As editors of Artificial Intelligence in Medical Imaging(AIMI),it is our great pleasure to take this opportunity to wish all of our authors,subscribers,readers,Editorial Board members,independent expert referees,and staff of the Editorial Office a Very Happy New Year.On behalf of the Editorial Team,we would like to express our gratitude to all of the authors who have contributed their valuable manuscripts,our independent referees,and our subscribers and readers for their continuous support,dedication,and encouragement.Together with an excellent of team effort by our Editorial Board members and staff of the Editorial Office,AIMI advanced in 2020 and we look forward to greater achievements in 2021.

Cloud computing-enabled IIOT system for neurosurgical simulation using augmented reality data acces

In recent years,statistics have indicated that the number of patients with malignant brain tumors has increased sharply.However,most surgeons still perform surgical training using the traditional autopsy and prosthesis model,which encounters many problems,such as insufficient corpse resources,low efficiency,and high cost.With the advent of the 5G era,a wide range of Industrial Internet of Things(IIOT)applications have been developed.Virtual Reality(VR)and Augmented Reality(AR)technologies that emerged with 5G are developing rapidly for intelligent medical training.To address the challenges encountered during neurosurgery training,and combining with cloud computing,in this paper,a highly immersive AR-based brain tumor neurosurgery remote collaborative virtual surgery training system is developed,in which a VR simulator is embedded.The system enables real-time remote surgery training interaction through 5G transmission.Six experts and 18 novices were invited to participate in the experiment to verify the system.Subsequently,the two simulators were evaluated using face and construction validation methods.The results obtained by training the novices 50 times were further analyzed using the Learning Curve-Cumulative Sum(LC-CUSUM)evaluation method to validate the effectiveness of the two simulators.The results of the face and content validation demonstrated that the AR simulator in the system was superior to the VR simulator in terms of vision and scene authenticity,and had a better effect on the improvement of surgical skills.Moreover,the surgical training scheme proposed in this paper is effective,and the remote collaborative training effect of the system is ideal.

Medical artificial intelligence and the black box problem:a view based on the ethical principle of“do no harm”

One concern about the application of medical artificial intelligence(AI)regards the“black box”feature which can only be viewed in terms of itsinputs and outputs,with no way to understand the AI’s algorithm.Thisis problematic because patients,physicians,and even designers,do not understand why or how a treatment recommendation is produced by AI technologies.One view claims that the worry about black-box medicine is unreasonable because AI systems outperform human doctors in identifying the disease.Furthermore,under the medical AI-physicianpatient model,the physician can undertake the responsibility of interpreting the medical AI’s diagnosis.In this study,we focus on the potential harm caused by the unexplainability feature of medical AI and try to show that such possible harm is underestimated.We will seek to contribute to the literature from three aspects.First,we appealed to a thought experiment to show that although the medical AI systems perform better on accuracy,the harm caused by medical AI’s misdiagnoses may be more serious than that caused by human doctors’misdiagnoses in some cases.Second,in patient-centered medicine,physicians were obligated to provide adequate information to their patients in medical decision-making.However,the unexplainability feature of medical AI systems would limit the patient’s autonomy.Last,we tried to illustrate the psychological and financial burdens that may be caused by the unexplainablity feature of medical AI systems,which seems to be ignored by the previous ethical discussions.

A Lightweight and Robust User Authentication Protocol with User Anonymity for IoT-Based Healthcare

With the rise of the Internet of Things(IoT),the word“intelligent medical care”has increasingly become a major vision.Intelligent medicine adopts the most advanced IoT technology to realize the interaction between patients and people,medical institutions,andmedical equipment.However,with the openness of network transmission,the security and privacy of information transmission have become a major problem.Recently,Masud et al.proposed a lightweight anonymous user authentication protocol for IoT medical treatment,claiming that their method can resist various attacks.However,through analysis of the protocol,we observed that their protocol cannot effectively resist privileged internal attacks,sensor node capture attacks,and stolen authentication attacks,and their protocol does not have perfect forward security.Therefore,we propose a new protocol to resolve the security vulnerabilities in Masud’s protocol and remove some redundant parameters,so as tomake the protocolmore compact and secure.In addition,we evaluate the security and performance of the new protocol and prove that the overall performance of the new protocol is better than that of other related protocols.

Medical Knowledge Graph:Data Sources,Construction,Reasoning,and Applications

Medical knowledge graphs(MKGs)are the basis for intelligent health care,and they have been in use in a variety of intelligent medical applications.Thus,understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field.To this end,we offer an in-depth review of MKG in this work.Our research begins with the examination of four types of medical information sources,knowledge graph creation methodologies,and six major themes for MKG development.Furthermore,three popular models of reasoning from the viewpoint of knowledge reasoning are discussed.A reasoning implementation path(RIP)is proposed as a means of expressing the reasoning procedures for MKG.In addition,we explore intelligent medical applications based on RIP and MKG and classify them into nine major types.Finally,we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.