Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

Medical Diagnosis Based on Multi-Attribute Group Decision-Making Using Extension Fuzzy Sets,Aggregation Operators and Basic Uncertainty Information Granule

Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.

Relationship between the use of smart medical services and mental health status

In this editorial,I comment on the article by Zhang et al.To emphasize the importance of the topic,I discuss the relationship between the use of smart medical devices and mental health.Smart medical services have the potential to positively influence mental health by providing monitoring,insights,and inter-ventions.However,they also come with challenges that need to be addressed.Understanding the primary purpose for which individuals use these smart tech-nologies is essential to tailoring them to specific mental health needs and prefe-rences.

Getting physical with medical education:Exercise based virtual anatomy review classes for medical students

The benefits of regular physical activity are well known.Yet,few studies have examined the effectiveness of integrating physical activity(PA)into curricula within a post-secondary setting.To investigate the incorporation of PA into medical curriculum,we developed a series of optional exercise-based review sessions designed to reinforce musculoskeletal(MSK)anatomy course material.These synchronous sessions were co-taught by a group fitness instructor and an anatomy instructor.The fitness instructor would lead students through both strength and yoga style exercises,while the anatomy instructor asked questions about relevant anatomical structures related to course material previously covered.After the sessions,participants were asked to evaluate the classes on their self-reported exam preparedness in improving MSK anatomy knowledge,PA levels,and mental wellbeing.Thirty participants completed surveys;a majority agreed that the classes increased understanding of MSK concepts(90.0%)and activity levels(97.7%).Many(70.0%)felt that the classes helped reduce stress.The majority of respondents(90.0%)agreed that the classes contributed to increased feelings of social connectedness.Overall,medical students saw benefit in PA based interventions to supplement MSK course concepts.Along with increasing activity levels and promoting health behaviours,integrating PA into medical curriculum may improve comprehension of learning material,alleviate stress and foster social connectivity among medical students.

Process,Material,and Regulatory Considerations for 3D Printed Medical Devices and Tissue Constructs

Three-dimensional(3D)printing is a highly automated platform that facilitates material deposition in a layer-by-layer approach to fabricate pre-defined 3D complex structures on demand.It is a highly promising technique for the fabrication of personalized medical devices or even patient-specific tissue constructs.Each type of 3D printing technique has its unique advantages and limitations,and the selection of a suitable 3D printing technique is highly dependent on its intended application.In this review paper,we present and highlight some of the critical processes(printing parameters,build orientation,build location,and support structures),material(batch-to-batch consistency,recycling,protein adsorption,biocompatibility,and degradation properties),and regulatory considerations(sterility and mechanical properties)for 3D printing of personalized medical devices.The goal of this review paper is to provide the readers with a good understanding of the various key considerations(process,material,and regulatory)in 3D printing,which are critical for the fabrication of improved patient-specific 3D printed medical devices and tissue constructs.

EDSUCh:A robust ensemble data summarization method for effective medical diagnosis

Identifying rare patterns for medical diagnosis is a challenging task due to heterogeneity and the volume of data.Data summarization can create a concise version of the original data that can be used for effective diagnosis.In this paper,we propose an ensemble summarization method that combines clustering and sampling to create a summary of the original data to ensure the inclusion of rare patterns.To the best of our knowledge,there has been no such technique available to augment the performance of anomaly detection techniques and simultaneously increase the efficiency of medical diagnosis.The performance of popular anomaly detection algorithms increases significantly in terms of accuracy and computational complexity when the summaries are used.Therefore,the medical diagnosis becomes more effective,and our experimental results reflect that the combination of the proposed summarization scheme and all underlying algorithms used in this paper outperforms the most popular anomaly detection techniques.

Advancements in medical treatment for pancreatic neuroendocrine tumors:A beacon of hope

This editorial highlights the remarkable advancements in medical treatment strategies for pancreatic neuroendocrine tumors(pan-NETs),emphasizing tailored approaches for specific subtypes.Cytoreductive surgery and somatostatin analogs(SSAs)play pivotal roles in managing tumors,while palliative options such as molecular targeted therapy,peptide receptor radionuclide therapy,and chemotherapy are reserved for SSA-refractory patients.Gastrinomas,insul-inomas,glucagonomas,carcinoid tumors and VIPomas necessitate distinct thera-peutic strategies.Understanding the genetic basis of pan-NETs and exploring immunotherapies could lead to promising avenues for future research.This review underscores the evolving landscape of pan-NET treatment,offering renewed hope and improved outcomes for patients facing this complex disease.

Reconstruction of Knowledge and Medical Images in the Convergence of Chinese and Western Medicine:Taking “Sweet Meat” as an Example

The pancreas is neither part of the five Zang organs(五脏) nor the six Fu organs(六腑).Thus,it has received little attention in Chinese medical literature.In the late 19th century,medical missionaries in China started translating and introducing anatomical and physiological knowledge about the pancreas.As for the word pancreas,an early and influential translation was “sweet meat”(甜肉),proposed by Benjamin Hobson(合信).The translation “sweet meat” is not faithful to the original meaning of “pancreas”,but is a term coined by Hobson based on his personal habits,and the word “sweet” appeared by chance.However,in the decades since the term “sweet meat” became popular,Chinese medicine practitioners,such as Tang Zonghai(唐宗海),reinterpreted it by drawing new medical illustrations for “sweet meat” and giving new connotations to the word “sweet”.This discussion and interpretation of “sweet meat” in modern China,particularly among Chinese medicine professionals,is not only a dissemination and interpretation of the knowledge of “pancreas”,but also a construction of knowledge around the term “sweet meat”.

Associative Tasks Computing Offloading Scheme in Internet of Medical Things with Deep Reinforcement Learning

The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.

The"4+4"Medical Doctor(MD)Pilot Program at PUMC:Implementation,Performance,and Prospects

Peking Union Medical College(PUMC)launched the"4+4"Medical Doctor(MD)pilot program in 2018,admitting students with non-medical backgrounds from top universities,aligning with national medical talent training policies to foster diverse and eager learners in medicine.On the occasion of the graduation of the first class of the"4+4"MD pilot class at PUMC in 2023,we reviewed the teaching reform in the pilot program and carried out a systematic survey and interviews with students,faculties,and management staff of the pilot class.This article reports on the measures taken by the pilot class at PUMC in enrollment and curriculum setting,and demonstrates the achievements of the pilot class in terms of student academic background structure,knowledge acquisition and skill learning,scientific research ability,and course evaluation.The results indicated that the pilot class had met the national demand for the"Medicine+X"talent training model.More specifically,with a diverse academic backgrounds,the pilot class graduates had academic levels comparable to the eight-year medical education graduates,and their scientific research abilities were satisfactory.The pilot program at PUMC will optimize the curriculum setting,strengthen the construction of faculty,learning resources,and teaching facilities,and reform the academic evaluation methods,thus deepening the reform of medical education and improving the"4+4"MD program as a novel medical education model.

Curved Spine:The Shape of Spine in Taoist Body Diagrams and Its Influence on Medical Body Diagrams

From the early Taoist diagrams of the human body to the end of the Qing dynasty and the beginning of the Republic of China,Taoists exaggerated and deformed the human spine in a shape-shifting manner.It is likely that medical practitioners were influenced by this style of representation,and there are also numerous diagrams of the human body with the curved spine in the lateral-view diagrams of viscera and Ming Tang Tu(明堂图Acupuncture and Moxibustion Chart),which constantly show the human torso in an elliptical“egg shape”.No later than the Ming dynasty,medical practitioners began to depict the actual physiological spinal curve of the human body.By the Qing dynasty,the depiction of the spinal curve in medical diagrams of the human figure showed a tendency to part ways with the Taoist freehand style of the previous generation.Although the representation of the curve of the spine was very crude,later medical images of the human body at least gradually straightened the spine and no longer depicted it in a shape-shifting manner.However,the curved spine in Taoist diagrams of the human body continued to exist,and the presentation of the curved spine never changed.This way of depicting its appearance,which is very different from reality,is shaped by Taoism's special way of perceiving and viewing the body,and may also contain another form of truth.

Traditional Chinese Medical Cases and Its Role in Pedagogy:A Narrative Analysis

Narrative medicine has gained significant attention in recent decades.The similarities between“parallel charts”and“medical cases”in traditional Chinese medicine(TCM)primarily lie in their authenticity.However,they differ in structure and narrative methods.Furthermore,medical case teaching is a prevalent pedagogical approach in TCM education that practitioners must master.This study explores the connection between TCM medical case teaching and narrative medicine,and concludes that the evolution of modern TCM case teaching aligns with the international standards of narrative medicine while integrating key TCM characteristics to enhance its value.This approach is essential for fostering humanistic sentiments,empathy,and reflective capabilities among future well-rounded TCM practitioners.

Secure approach to sharing digitized medical data in a cloud environment

Without proper security mechanisms, medical records stored electronically can be accessed more easily than physical files. Patient health information is scattered throughout the hospital environment, including laboratories, pharmacies, and daily medical status reports. The electronic format of medical reports ensures that all information is available in a single place. However, it is difficult to store and manage large amounts of data. Dedicated servers and a data center are needed to store and manage patient data. However, self-managed data centers are expensive for hospitals. Storing data in a cloud is a cheaper alternative. The advantage of storing data in a cloud is that it can be retrieved anywhere and anytime using any device connected to the Internet. Therefore, doctors can easily access the medical history of a patient and diagnose diseases according to the context. It also helps prescribe the correct medicine to a patient in an appropriate way. The systematic storage of medical records could help reduce medical errors in hospitals. The challenge is to store medical records on a third-party cloud server while addressing privacy and security concerns. These servers are often semi-trusted. Thus, sensitive medical information must be protected. Open access to records and modifications performed on the information in those records may even cause patient fatalities. Patient-centric health-record security is a major concern. End-to-end file encryption before outsourcing data to a third-party cloud server ensures security. This paper presents a method that is a combination of the advanced encryption standard and the elliptical curve Diffie-Hellman method designed to increase the efficiency of medical record security for users. Comparisons of existing and proposed techniques are presented at the end of the article, with a focus on the analyzing the security approaches between the elliptic curve and secret-sharing methods. This study aims to provide a high level of security for patient health records.

Blockchain-Assisted Electronic Medical Data-Sharing:Developments,Approaches and Perspectives

Medical blockchain data-sharing is a technique that employs blockchain technology to facilitate the sharing of electronic medical data.The blockchain is a decentralized digital ledger that ensures data-sharing security,transparency,and traceability through cryptographic technology and consensus algorithms.Consequently,medical blockchain data-sharing methods have garnered significant attention and research efforts.Nevertheless,current methods have different storage and transmission measures for original data in the medical blockchain,resulting in large differences in performance and privacy.Therefore,we divide the medical blockchain data-sharing method into on-chain sharing and off-chain sharing according to the original data storage location.Among them,off-chain sharing can be subdivided into on-cloud sharing and local sharing according to whether the data is moved.Subsequently,we provide a detailed analysis of basic processes and research content for each method.Finally,we summarize the challenges posed by the current methods and discuss future research directions.

A cross-sectional study to assess medication safety,knowledge,attitude,and practices regarding nutrition and medication among pregnant women

Objective:To assess pregnant women's knowledge,attitude,and practice regarding nutrition and medication usage,analyse the prescribing pattern,and categorize them based on the Food and Drug Administration(FDA)guidelines.Methods:A cross-sectional study was conducted with 264 pregnant women in the obstetrics and gynaecology department of a tertiary care hospital from October 2022 to August 2023.A knowledge,attitude,and practice(KAP)questionnaire was prepared in English language by the researchers and validated by an expert panel consisting of 12 members.The validated questionnaire was then translated into regional languages,Kannada and Malayalam.The reliability of the questionnaire was assessed with test-retest method with a representative sample population of 30 subjects(10 subjects for each language).The subjects'knowledge,attitude,and practice were evaluated using the validated KAP questionnaire.The safety of the medication was assessed using the FDA drug safety classification for pregnancy.Results:The mean scores for nutritional and medication usage knowledge,attitude,and practice were 4.14±1.15,4.50±1.09,and 3.00±1.47,respectively.Among 30 prescribed medications,3 belong to category A(no risk in human studies),8 belong to category B(no risk in animal studies),18 belong to category C(risk cannot be ruled out)and 1 drug is not classified.A significant association was observed between medication knowledge and practice(r=0.159,P=0.010).Conclusions:Most of the study population knows the need to maintain good dietary and medication practices during pregnancy.Counselling pregnant women regarding diet and medication usage is crucial in maternal care.

Mediating role of inner strength in the relationship between medication literacy and medication adherence among kidney transplant patients

Objective:Compared with long-term renal replacement therapy,kidney transplantation is the ideal treatment for end-stage renal disease(ESRD),significantly extending patient life and improving quality of life.Kidney transplant patients need to adhere to lifelong immunosuppressive medication regimens,but their medication adherence is generally poor compared with other organ transplant recipients.Medication adherence is closely related to medication literacy and psychological status,yet related studies are limited.This study aims to investigate the current status of medication adherence,inner strength,and medication literacy in kidney transplant patients,analyze the relationships among these 3 factors,and explore the mediating role of inner strength in the relationship between medication literacy and medication adherence.Methods:A cross-sectional survey was conducted from March to October 2023 involving 421 patients aged≥18 years who visited kidney transplantation outpatient clinics at 4 tertiary hospitals in Hunan Province.The inner strength,medication literacy,and medication adherence of kidney transplant patients were investigated using the Inner Strength Scale(ISS),the Chinese version of the Medication Literacy Assessment in Spanish and English(MedLitRxSE),and the Chinese version of the Morisky Medication Adherence Scale-8(C-MMAS-8),respectively.Univariate analysis was performed to examine the effects of demographic and clinical data on medication adherence.Correlation analysis was conducted to explore the relationships among medication literacy,medication adherence,and inner strength.Significant variables from univariate and correlation analyses were further analyzed using multiple linear regression,and the mediating effect of inner strength was explored.Results:Among the 421 questionnaires collected,408 were valid,with an effective rate of 96.91%.The scores of C-MMAS-8,MedLitRxSE,and ISS were 6.64±1.16,100.63±14.67,and 8.47±4.03,respectively.Among the 408 patients,only 86(21.08%)patients had a high level of medication adherence,whereas 230(56.37%)patients had a medium level of medication adherence,and 92(22.55%)patients had poor medication adherence.Univariate analysis indicated that the kidney transplant patients’age,marital status,education levels,years since their kidney transplant operation,number of hospitalizations after the kidney transplant,and adverse drug reactions showed significant differences in medication adherence(all P<0.05).Correlation analysis showed that inner strength positively correlated with both medication literacy(r=0.183,P<0.001)and medication adherence(r=0.201,P<0.001).Additionally,there was a positive correlation between medication adherence and medication literacy(r=0.236,P<0.001).Inner strength accounted for 13.22%of the total effect in the mediating role between medication literacy and medication adherence.Conclusion:The level of medication adherence among kidney transplant patients needs improvement,and targeted intervention measures are essential.Inner strength mediates the relationship between medication literacy and medication adherence in these patients.Healthcare professionals should focus on enhancing medication literacy and supporting patients’inner strength to improve medication adherence.

Analysis and improving countermeasures of medical disputes from the perspective of legal changes

Under the background of medical disputes growing in number,scale and intensity,tracing back legal changes in medical field as a breakthrough point,this paper took a legal perspective to illustrate changes in medical dispute settlements from legislative orientation to legal system improvement.In view of the fact that early legislation in medical field was biased towards identification and punishment of doctors’responsibility,and later intensive legislation in balancing increasing"medical trouble"phenomenon with limited effects and difficulties to abide by the law,this paper proposed to improve doctor-patient dispute settlements system in China referencing from foreign law experience,to reduce investigation of doctors at the judicial level,and to establish a settlement mechanism on doctors’apology at the legislative level,so as to promote a healthy development of doctor-patient relationship.

Advances in Biomedical Applications of Hydrogels from Seaweed-Derived Sulfated Polysaccharides:Carrageenan,Fucoidan,and Ulvan

Sulfated polysaccharides extracted from seaweeds,including Carrageenan,Fucoidan and Ulvan,are crucial bioactive compounds known for their diverse beneficial properties,such as anti-inflammatory,antitumor,immunomodulatory,antiviral,and anticoagulant effects.These polysaccharides form hydrogels hold immense promise in biomedicine,particularly in tissue engineering,drug delivery systems and wound healing.This review comprehensively explores the sources and structural characteristics of the three important sulfated polysaccharides extracted from different algae species.It elucidates the gelation mechanisms of these polysaccharides into hydrogels.Furthermore,the biomedical applications of these three sulfated polysaccharide hydrogels in wound healing,drug delivery,and tissue engineering are discussed,highlighting their potential in the biomedicine.

Medical management of urolithiasis: Great efforts and limited progress

Objective:To provide a comprehensive review on the existing literature on medical management of urolithiasis.Methods:A thorough literature review was performed using Medline,PubMed/PMC,Embase,and the Cochrane Database of Systematic Reviews up to December 2022 to identify publications on the medical management of urolithiasis.Studies that assessed dietary and pharmacologic management of urolithiasis were reviewed;studies on medical expulsive therapy were not included in this review.Results:Medical management of urolithiasis ranges from the prophylactic management of kidney stone disease to dissolution therapies.While most treatment concepts have been long established,large randomized controlled trials are scarce.Dietary modification and increased fluid intake remain cornerstones in the conservative management of urolithiasis.A major limitation for medical management of urolithiasis is poor patient compliance.Conclusion:Medical management of urolithiasis is more important in patients with recurrent urolithiasis and patients with metabolic abnormalities putting them at higher risk of developing stones.Although medical management can be effective in limiting stone recurrence,medical interventions often fail due to poor compliance.

Robust zero-watermarking algorithm based on discrete wavelet transform and daisy descriptors for encrypted medical image

In the intricate network environment,the secure transmission of medical images faces challenges such as information leakage and malicious tampering,significantly impacting the accuracy of disease diagnoses by medical professionals.To address this problem,the authors propose a robust feature watermarking algorithm for encrypted medical images based on multi-stage discrete wavelet transform(DWT),Daisy descriptor,and discrete cosine transform(DCT).The algorithm initially encrypts the original medical image through DWT-DCT and Logistic mapping.Subsequently,a 3-stage DWT transformation is applied to the encrypted medical image,with the centre point of the LL3 sub-band within its low-frequency component serving as the sampling point.The Daisy descriptor matrix for this point is then computed.Finally,a DCT transformation is performed on the Daisy descriptor matrix,and the low-frequency portion is processed using the perceptual hashing algorithm to generate a 32-bit binary feature vector for the medical image.This scheme utilises cryptographic knowledge and zero-watermarking technique to embed watermarks without modifying medical images and can extract the watermark from test images without the original image,which meets the basic re-quirements of medical image watermarking.The embedding and extraction of water-marks are accomplished in a mere 0.160 and 0.411s,respectively,with minimal computational overhead.Simulation results demonstrate the robustness of the algorithm against both conventional attacks and geometric attacks,with a notable performance in resisting rotation attacks.