Diagnosis Of Illness Research Articles

Enhancing multi-class lung disease classification in chest x-ray images: A hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach

ABSTRACT One of the most used diagnostic imaging techniques for identifying a variety of lung and bone-related conditions is the chest X-ray. Recent developments in deep learning have demonstrated several successful cases of illness diagnosis from chest X-rays. However, issues of stability and class imbalance still need to be resolved. Hence in this manuscript, multi-class lung disease classification in chest x-ray images using a hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach is proposed (MPNN-Hyb-MRF-VEA). Initially, the input chest X-ray images are taken from the Covid-Chest X-ray dataset. Anisotropic diffusion Kuwahara filtering (ADKF) is used to enhance the quality of these images and lower noise. To capture significant discriminative features, the Term frequency-inverse document frequency (TF-IDF) based feature extraction method is utilized in this case. The Multilayer Perceptron Neural Network (MPNN) serves as the classification model for multi-class lung disorders classification as COVID-19, pneumonia, tuberculosis (TB), and normal. A Hybrid Manta-Ray Foraging and Volcano Eruption Algorithm (Hyb-MRF-VEA) is introduced to further optimize and fine-tune the MPNN’s parameters. The Python platform is used to accurately evaluate the proposed methodology. The performance of the proposed method provides 23.21%, 12.09%, and 5.66% higher accuracy compared with existing methods like NFM, SVM, and CNN respectively.

Burden and outcome of respiratory morbidities among children and adolescents with sickle cell disease-A retrospective review of emergency presentations in some Nigerian tertiary institutions.

Despite the huge burden of sickle cell disease (SCD) among Nigerian children, the burden and outcome of respiratory illnesses remain undocumented. Thus, we aimed to describe the spectrum and outcome of respiratory illnesses among SCD childrenand adolescentadmissions in ten Nigerian tertiary hospitals. A retrospective review of the SCD admission records of children and adolescents with a confirmed diagnosis of respiratory illnesses from 2012 to 2021 in ten tertiary health facilities across five geopolitical zones in Nigeria was conducted. The data, collectedbetween March and June 2023, included the age, sex, diagnosis, complications, duration and outcome of hospitalization. Of the 72,333 paediatric admissions, 7,256 (10.0%) had SCD; the proportion of SCD from the total admission ranged from 2.1 to 16.3% in the facilities. Of the 7,256 children and adolescents with SCD, 1,213 (16.7%) had respiratory morbidities. Lower respiratory disease was the most common (70.0%) respiratory entity and the majority were pneumonia (40.1.0%), followed by acute chest syndrome (26.7%). Seventeen (1.4%) patients died; all had lower respiratory diseases [(acute chest syndrome ACS (11, 64.7%), pneumonia; 5, 29.4%, and asthma (1, 5.9%). Based on the proportion of deaths among overall SCD, the 17 death cases contributed 9.4% (95% CI 5.9 to 14.5). Factors associated with deaths included duration of hospitalization less than 72 hours and lower respiratory tract diseases. Sickle cell disease is a major contributor to hospitalization among Nigerian children and adolescents, with high respiratory morbidity and mortality. Pneumonia and acute chest syndrome were associated with mortality, andthe highest risk of death within the first 72 hours.

Speech under Stress: Analysis and Recognition using KNN

This work examines how speech characteristics might be analyzed to tailor automated conversation recognition to illness diagnosis. Voice analysis, which is now performed by a skilled physician using techniques based on auditory analysis, enables the diagnosis of illnesses that impact the vocal apparatus. The suggested work offers a cutting-edge way to monitor a patient's pathology. It is stress-free to use, quick, and affordable for the physician, and safe for the patient. This technique practices a parametric methodology (jitter, shimmer, harmonic to sound, etc.) to assess the sick vocal sound. Additionally, the technique for this work rest on on the feature extraction of Mel Frequency Cepstral Coefficient (MFCC) and the feature matching of Dynamic Time Warping (DTW). This article provides a step-by-step breakdown of the speech analysis procedure used on anxious individuals. The degree of classification accuracy attained for each of the retrieved characteristics confirms that our method is effective in differentiating between stressed individuals. Thus, the speech analysis feature on the PRAAT platform has produced positive results and shown to be an effective tool for differentiating between those who are under stress and those who are not. Using MATLAB 2018b, the work displays several 2D and 3D displays of teaching and anticipated matters. Keywords: Dynamic Time Warping, Mel Frequency Cepstral Coefficient, K nearest neighbor, Virtual Reality

Explainable AI-driven IoMT fusion: Unravelling techniques, opportunities, and challenges with Explainable AI in healthcare

Background and Objective:Artificial Intelligence (AI) has shown significant advancements across several industries, including healthcare, using better fusion methodologies, improved data accessibility, and heightened processing capabilities. Although deep learning algorithms excel in challenging scenarios, their widespread adoption presents a challenge due to their need for more transparency. The advent of Explainable AI (XAI) has addressed this limitation by offering AI-driven systems with attributes such as transparency, interpretability, and reliability. The need for transparency in critical sectors like healthcare has prompted an increased academic focus on investigating and understanding these frameworks. The present paper thoroughly examines the latest research and advancements in Explainable Artificial Intelligence (XAI), specifically focusing on its integration into the Internet of Medical Things (IoMT) in healthcare settings. The last section tackles outstanding issues concerning XAI and IoMT and outlines prospective directions for further research. Methods:A thorough investigation was carried out across many scholarly databases and online digital libraries, such as IEEE Xplore, ACM Digital Library, Wiley Interscience, Taylor and Francis, ScienceDirect, Scopus, Springer, Google Scholar, Citeseer Library, Semantic Scholar, and other relevant sources. An analysis was conducted on articles published from March 2004 to February 2024, specifically focusing on AI models that explain different healthcare issues. The search query included “Explainable AI” in conjunction with “Open Black Box, Healthcare, Transparent Models, Interpretable Deep Learning, Machine Learning, Medical Information System, Accountability, Smart Healthcare, and Responsible AI”, indicating the field’s dynamic nature. The authors also examined various techniques and datasets to elucidate healthcare difficulties, including incorporating the Internet of Medical Things (IoMT). Results:The evaluation included more than 105 published models that used clinical data input to diagnose diverse disorders, focusing on incorporating Internet of Medical Things (IoMT) components. In addition to illness diagnosis, these models also elucidate the decisions made. The models were classified according to the input data, used methodologies, and integration of the Internet of Medical Things (IoMT). The categorization included machine learning and deep learning methodologies, particularly on explainability models connected to the Internet of Medical Things (IoMT). These models were classified as model-agnostic, model-specific, global, local, ante-hoc, and post-hoc. Conclusion:This extensive study examines machine learning, and deep learning models developed based on clinical data and associated with the Internet of Medical Things (IoMT) for illness detection. These models improve efficiency and accuracy and provide essential assistance to medical personnel. Explaining each choice made by individuals contributes to the acceleration of sickness detection, resulting in decreased medical expenses for patients and healthcare systems. The integration of Explainable Artificial Intelligence (XAI) and the Internet of Medical Things (IoMT) highlights a fundamental change in healthcare towards more transparent and linked systems, leading to significant breakthroughs in medical decision-making.

Positive Deviance and Exploring the Role of Mental Health on Early Postoperative Recovery After Total Hip Arthroplasty: A Retrospective Analysis Using the PROMIS-10 Global Health.

Mental illnesses are well-known factors that contribute to poor outcomes among total hip arthroplasty (THA) patients. However, a dichotomized mental illness diagnosis may not reflect the complex biopsychosocial factors contributing to a patient's health. Investigating patients who achieve positive outcomes despite having risk factors, known as positive deviants, may help identify protective characteristics and decrease health disparities among this growing population of patients. Using preoperative Mental Health T-scores (MHT) from the PROMIS-10 Global Health questionnaire among patients with a mental illness diagnosis, the purpose of this study was to explore whether patients with above-average MHT, or positive deviants, experienced a different immediate postoperative recovery path compared with patients with below-average MHT. This was a retrospective chart review of patients undergoing elective primary THA. Patients with a formal diagnosis of a mental health condition were divided based on their MHT (above average [AA] >50, average [A] 40 to 50, below average [BA] <40). Postsurgical parameters included total opioid consumption, self-reported pain scores, and discharge disposition. A total of 299 patients were analyzed. After controlling for length of stay and type of mental illness, patients in the AA-MHT and A-MHT groups used 33.8 and 29.8 morphine milligram equivalents less than patients in the BA-MHT group during the inpatient stay, respectively. Patients in the AA-MHT group reported a 1.0 lower pain with activity score at discharge compared with patients in the BA-MHT group. The intersection between patients with a mental illness in need of a THA is becoming more commonplace. Data suggest that patients with a mental illness who report AA-MHT on the PROMIS-10 Global Health questionnaire may represent positive deviants or those with a more positive in-hospital recovery path compared with those patients with BA-MHT. Diagnostic study-retrospective cohort study.

Biobehavioral approach to distinguishing panic symptoms from medical illness.

Panic disorder is a common psychiatric diagnosis characterized by acute, distressing somatic symptoms that mimic medically-relevant symptoms. As a result, individuals with panic disorder overutilize personal and healthcare resources in an attempt to diagnose and treat physical symptoms that are often medically benign. A biobehavioral perspective on these symptoms is needed that integrates psychological and medical knowledge to avoid costly treatments and prolonged suffering. This narrative review examines six common somatic symptoms of panic attacks (non-cardiac chest pain, palpitations, dyspnea, dizziness, abdominal distress, and paresthesia), identified in the literature as the most severe, prevalent, or critical for differential diagnosis in somatic illness, including long COVID. We review somatic illnesses that are commonly comorbid or produce panic-like symptoms, their relevant risk factors, characteristics that assist in distinguishing them from panic, and treatment approaches that are typical for these conditions. Additionally, this review discusses key factors, including cultural considerations, to assist healthcare professionals in differentiating benign from medically relevant symptoms in panic sufferers.

Role of Artificial Intelligence in Endoscopic Intervention: A Clinical Review.

Gastrointestinal diseases are increasing in global prevalence. As a result, the contribution to both mortality and healthcare costs is increasing. While interventions utilizing scoping techniques or ultrasound are crucial to both the timely diagnosis and management of illness, a few limitations are associated with these techniques. Artificial intelligence, using computerized diagnoses, deep learning systems, or neural networks, is increasingly being employed in multiple aspects of medicine to improve the characteristics and outcomes of these tools. Therefore, this review aims to discuss applications of artificial intelligence in endoscopy, colonoscopy, and endoscopic ultrasound.

Self-determined motivational health coaching (‘SAMI’) during outpatient treatment to promote physical activity of people with serious mental illness: a pilot controlled trial

Background Brief motivational coaching, integrated into health care; seems promising to address physical inactivity of people with serious mental illness (SMI). Aims To test the impact of a self-determined health coaching approach (the “SAMI” intervention) during outpatient mental health treatment on moderate-to-vigorous physical activity (MVPA) of people with SMI. Methods Adults (mean age = 41.9, SD = 10.9) with an ICD-10 diagnosis of mental illness were semi-randomized to the SAMI-intervention group (IG) or control group (CG). The IG received 30 minutes of health coaching based on the self-determination theory (SDT). MVPA and sedentary time (ST) were measured with the International Physical Activity Questionnaire – short form (IPAQ-SF) and symptoms of mental illness with the Brief Symptom Inventory (BSI-18), each at baseline and follow-up (3–4 months). Differences in primary (MVPA) and secondary (ST, BSI-18) outcomes were evaluated using negative binomial regressions and general linear models. Results In the IG (n = 30), MVPA increased from 278 (interquartile range [IQR] = 175–551) to 435 (IQR = 161–675) min/week compared to a decrease from 250 (IQR = 180–518) to 155 (IQR = 0–383) min/week in the CG (n = 26; adjusted relative difference at follow-up: Incidence Rate Ratio [IRR] = 2.14, 95% CI: 1.17–3.93, p = 0.014). There were no statistically significant differences in ST and BSI-18. Conclusions Brief self-determined health coaching during outpatient treatment could increase post-treatment MVPA in people with SMI, potentially up to a clinically relevant level. However, great uncertainty (for all outcomes) weakens the assessment of clinical relevance.

Computer vision to enhance healthcare domain: An overview of features, implementation, and opportunities

The emergence of Artificial Intelligence (AI) has already brought several advantages to the healthcare sector. Computer Vision (CV) is one of the growing modern AI technologies. The distribution and administration of medications are about to change by using CV for medication management. This system scans pharmaceutical labels and keeps track of the process from delivery to administration using cameras, sensors, and computer algorithms. In order to assure accuracy in medicine delivery and dose, the system also makes it easier for doctors, nurses, and chemists to communicate. The computer vision-driven medication management system can significantly lower the number of medical mistakes that result from inaccurate or missing prescriptions, improper doses, or simply forgetting to take a particular drug. An exhaustive literature review has been done to identify work related to the research objectives. This paper is about CV and their need in healthcare. Various tasks associated with CV in the healthcare domain are discussed. Targeted healthcare goals through CV traits are briefed. Finally, the significant applications of CVs in healthcare were identified and discussed. Nowadays, CV has practical uses in healthcare. Its methods are widely used since they have shown excellent utility in several medical contexts, including medical imaging and surgical planning. The CV is used to study how to program computers to comprehend digital pictures. Numerous medical applications utilise this technology, such as automated abnormality identification, illness diagnosis, and surgical procedure guiding. CV is expanding quickly and has enormous promise to enhance healthcare. Some of the many CV applications in the healthcare sector include patient identification systems, medical picture analysis, surgical simulation and illness diagnosis.

Geriatric End-of-Life Screening Tool Prediction of 6-Month Mortality in Older Patients

Emergency department (ED) visits by older adults with life-limiting illnesses are a critical opportunity to establish patient care end-of-life preferences, but little is known about the optimal screening criteria for resource-constrained EDs. To externally validate the Geriatric End-of-Life Screening Tool (GEST) in an independent population and compare it with commonly used serious illness diagnostic criteria. This prognostic study assessed a cohort of patients aged 65 years and older who were treated in a tertiary care ED in Boston, Massachusetts, from 2017 to 2021. Patients arriving in cardiac arrest or who died within 1 day of ED arrival were excluded. Data analysis was performed from August 1, 2023, to March 27, 2024. GEST, a logistic regression algorithm that uses commonly available electronic health record (EHR) datapoints and was developed and validated across 9 EDs, was compared with serious illness diagnoses as documented in the EHR. Serious illnesses included stroke/transient ischemic attack, liver disease, cancer, lung disease, and age greater than 80 years, among others. The primary outcome was 6-month mortality following an ED encounter. Statistical analyses included area under the receiver operating characteristic curve, calibration analyses, Kaplan-Meier survival curves, and decision curves. This external validation included 82 371 ED encounters by 40 505 unique individuals (mean [SD] age, 76.8 [8.4] years; 54.3% women, 13.8% 6-month mortality rate). GEST had an external validation area under the receiver operating characteristic curve of 0.79 (95% CI, 0.78-0.79) that was stable across years and demographic subgroups. Of included encounters, 53.4% had a serious illness, with a sensitivity of 77.4% (95% CI, 76.6%-78.2%) and specificity of 50.5% (95% CI, 50.1%-50.8%). Varying GEST cutoffs from 5% to 30% increased specificity (5%: 49.1% [95% CI, 48.7%-49.5%]; 30%: 92.2% [95% CI, 92.0%-92.4%]) at the cost of sensitivity (5%: 89.3% [95% CI, 88.8-89.9]; 30%: 36.2% [95% CI, 35.3-37.1]). In a decision curve analysis, GEST outperformed serious illness criteria across all tested thresholds. When comparing patients referred to intervention by GEST with serious illness criteria, GEST reclassified 45.1% of patients with serious illness as having low risk of mortality with an observed mortality rate 8.1% and 2.6% of patients without serious illness as having high mortality risk with an observed mortality rate of 34.3% for a total reclassification rate of 25.3%. The findings of this study suggest that both serious illness criteria and GEST identified older ED patients at risk for 6-month mortality, but GEST offered more useful screening characteristics. Future trials of serious illness interventions for high mortality risk in older adults may consider transitioning from diagnosis code criteria to GEST, an automatable EHR-based algorithm.

Exploring the Role of Artificial Intelligence in Mental Healthcare: Current Trends and Future Directions - A Narrative Review for a Comprehensive Insight.

Mental health is an essential component of the health and well-being of a person and community, and it is critical for the individual, society, and socio-economic development of any country. Mental healthcare is currently in the health sector transformation era, with emerging technologies such as artificial intelligence (AI) reshaping the screening, diagnosis, and treatment modalities of psychiatric illnesses. The present narrative review is aimed at discussing the current landscape and the role of AI in mental healthcare, including screening, diagnosis, and treatment. Furthermore, this review attempted to highlight the key challenges, limitations, and prospects of AI in providing mental healthcare based on existing works of literature. The literature search for this narrative review was obtained from PubMed, Saudi Digital Library (SDL), Google Scholar, Web of Science, and IEEE Xplore, and we included only English-language articles published in the last five years. Keywords used in combination with Boolean operators ("AND" and "OR") were the following: "Artificial intelligence", "Machine learning", Deep learning", "Early diagnosis", "Treatment", "interventions", "ethical consideration", and "mental Healthcare". Our literature review revealed that, equipped with predictive analytics capabilities, AI can improve treatment planning by predicting an individual's response to various interventions. Predictive analytics, which uses historical data to formulate preventative interventions, aligns with the move toward individualized and preventive mental healthcare. In the screening and diagnostic domains, a subset of AI, such as machine learning and deep learning, has been proven to analyze various mental health data sets and predict the patterns associated with various mental health problems. However, limited studies have evaluated the collaboration between healthcare professionals and AI in delivering mental healthcare, as these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches. Ethical issues, cybersecurity, a lack of data analytics diversity, cultural sensitivity, and language barriers remain concerns for implementing this futuristic approach in mental healthcare. Considering these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches, it is imperative to explore these aspects. Therefore, future comparative trials with larger sample sizes and data sets are warranted to evaluate different AI models used in mental healthcare across regions to fill the existing knowledge gaps.

Determinants of self-reported chronic disease diagnoses among older persons in South Africa.

Chronic diseases tend to affect the quality of life for older persons worldwide, especially in resource-constrained developing countries. Chronic diseases contribute to a large number of deaths among the population of South Africa. This study examines the determinants of self-reported chronic disease diagnoses among older persons in South Africa. The study setting was South Africa. Cross-sectional data from the 2019 South Africa General Household Survey were analysed (n[weighted] = 4 887 334). We fitted a binary logistic regression model to determine the relationship between socio-demographic factors and being diagnosed with self-reported chronic diseases. We found that at least 5 in 10 older persons were diagnosed with self-reported chronic disease. The bivariate findings showed that age, population group, sex, marital status, level of education, disability status, household composition and province were significantly associated with self-reported chronic disease diagnoses. At the multivariate level, we found that age, sex, population group, marital status, educational level, disability status, household wealth status, household composition and province were key predictors of self-reported chronic disease diagnoses. We found that various factors were key determinants of being diagnosed with self-reported chronic diseases. This study offers important insights into the main correlations between older adults and self-reported chronic illness diagnoses. More study is required on the health of the elderly as it will help direct policy discussions and improve the development of health policies about the elderly.Contribution:This study highlights the need for a better understanding of, and continued research into, the determinants health among older populations to guide future healthcare strategies.

A dual-mode label-free electrochemical immunosensor for ultrasensitive detection of procalcitonin by on-site vulcanization of dual-MOF heterostructure

Detection of procalcitonin (PCT) is crucial for the early identification of sepsis. PCT is primarily utilized in the multiple diagnosis of bacterial and viral illnesses along with to guide the application of antibiotics. Considering their advantages of high specificity and straightforward usage, electrochemical immunosensors offer significant application prospects in the detection of disease indicators. A dual-mode electrochemical immunosensor was constructed in this study to reliably identify PCT. In light of the synergistic effect of the dual-MOF derived heterostructure, the immunosensor demonstrating excellent square wave voltammetry (SWV) signals as well as significant catalytic activity for the H2O2 redox process. In addition to maintaining a low detection limit (SWV: 0.31 fg/mL and i-t: 0.098 fg/mL), the immunosensor offers an extensive linear response range (0.000001–100 ng/mL). The excellent performance is on account of the introduction of the local on-site sulfurized dual-MOF heterostructure with abundant metal chalcogenides/MOF interfaces, which boosts the specific surface area, offers an abundance of active sites, enhances conductivity, and raises catalytic activity. Furthermore, the immunosensor exhibits outstanding specificity, stability and reproducibility for the determination of PCT in serum, which is of great crucial for the clinical screening and diagnosis of sepsis.

Predictive Analytics and Machine Learning in Disease Diagnosis: A Review of Recent Advances

Artificial intelligence (AI) refers to the utilisation of computer information to exhibit intelligent behaviour with minimal human intervention, whereas machine learning (ML) is seen as a subset of AI methodologies. Typically, this form of intelligence is widely recognised to have originated with the advent of robotics. Given the slow progression of diseases, it is crucial to make early predictions and administer appropriate medication. Hence, it is imperative to present a decision model that may aid in the diagnosis of chronic illnesses and forecast future patient prognoses. The primary objective of this study is to investigate the application of Predictive Analytics and ML in the field of Disease Diagnosis. The study will focus on reviewing the latest advancements in this area. This work specifically emphasises the significance of ML prediction models in disease diagnosis within the AI field, amidst other approaches available. The study utilises a qualitative research methodology. ML has been prominent in the medical field as it offers methods for analysing disease-related data, as indicated by this study. ML algorithms are crucial in attaining early disease detection. Another crucial finding in this research is that the accuracy and performance of the model can be enhanced by employing an alternative technique to generate a single ensemble model.

Prescriptions for Antipsychotics: Youth with Intellectual/Developmental Disabilities Compared to Youth without Intellectual/Developmental Disabilities.

The purpose of this study was to compare antipsychotic use by youth with intellectual/developmental disabilities to youth without IDD as they transitioned from pediatric to adult health care services. In addition, antipsychotic use was compared between youth with different types of IDD (autism, Fetal Alcohol Syndrome, Down syndrome and 'other'). Population level administrative health data was used to compare the dispensing of antipsychotics for youth aged 15 to 24 years with and without IDD, between 2010 and 2019. Because antipsychotics are associated with metabolic syndrome and type 2 diabetes, we also examined the dispensing data for metformin. In addition, we examined dispensed antipsychotics between different types of IDD. For both dispensing of antipsychotics and metformin, we used multi-variable logistic regression to derive adjusted odds ratios. There were 20,591 youth with IDD and 1,293,791 youth without IDD. Youth with IDD had significantly higher odds of being dispensed an antipsychotic (7.13 (6.82, 7.44)), even when a diagnosis of a psychotic illness was included in the regression. Higher odds were found in all age groups. Youth with IDD also had significantly higher odds of being dispensed metformin (3.739 (3.323, 4.208)) compared to youth without IDD. Youth with autism, FAS and 'other' types of IDD diagnoses all had higher odds of being dispensed an antipsychotic compared to youth with Down syndrome. Compared to youth without IDD, youth with IDD were more frequently dispensed antipsychotics and metformin. These findings have important implications for the health of youth with IDD.

Exosome Isolation and Detection: From Microfluidic Chips to Nanoplasmonic Biosensor.

Exosomes are becoming more widely acknowledged as significant circulating indicators for the prognosis and diagnosis of cancer. Circulating exosomes are essential to the development and spread of cancer, according to a growing body of research. Using existing technology, characterizing exosomes is quite difficult. Therefore, a direct, sensitive, and targeted approach to exosome detection will aid in illness diagnosis and prognosis. The review discusses the new strategies for exosome isolation and detection technologies from microfluidic chips to nanoplasmonic biosensors, analyzing the advantages and limitations of these new technologies. This review serves researchers to better understand exosome isolation and detection methods and to help develop better exosome isolating and detecting devices for clinical applications.

Perceptions of the determinants of mental health literacy among Vietnamese adolescents

Even though there is a large frequency of mental health issues among adolescents all over the world, there is a lack of knowledge regarding the mental health literacy of this demographic, particularly in economically developing nations. Literacy in mental health is necessary for the early diagnosis and treatment of mental illnesses before they become severe. The objectives of this study are to investigate the mental health literacy of students, the causes of mental illness, the disparities between genders, the reasons for mental disorders, and the variations between grade and causes of mental illnesses. A one-way analysis of variance (ANOVA) and an independent sample t-test were utilized to analyze the data. It was shown that environmental factors were the most significant contributors to mental illness. Furthermore, there was a substantial disparity between the degree of spirituality and the gender of the individual. Moreover, it was demonstrated that there are substantial distinctions between grade and environmental and biological factors. These findings have significant repercussions for educational initiatives that aim to promote students' mental health literacy at the high school level. Among the Vietnamese high school population, there is a demand for programs that teach pupils about mental health literacy. Literacy in mental health serves as the basis for mental health promotion, prevention, and care, with the primary goal of enhancing mental health and its associated outcomes.

Contactless Diseases Diagnoses Using Wireless Communication Sensing: Methods and Challenges Survey

Respiratory illness diagnosis and continuous monitoring are becoming popular as sensitive markers of chronic diseases. This interest has motivated the increased development of respiratory illness diagnosis by exploiting wireless communication as a sensing system. Several methods for diagnosing a respiratory illness are based on multiple sensors and techniques. Depending on whether the device embeds the sensor in contact with the body or not, these techniques are commonly categorized as contact based or contactless. Contactless methods have gained increasing popularity due to their ubiquitous nature, non-intrusiveness, and low cost. However, contactless methods are difficult to implement, with several challenges such as dynamic wireless communication environments. This article comprehensively reviews all contactless respiratory illnesses using wireless communication sensing methods, their associated challenges, and issues. In addition, applications of respiratory illness diagnosis methods using wireless communication are provided to investigate each method's potential development and applicability. Continuous and accurate diagnosis of respiratory illness using wireless communication sensing systems can assist caregivers in enhancing the care quality and bestowing patients with more freedom for both inpatients and outpatients. Furthermore, wireless communication monitoring systems could lead to treatment plans remotely more effectively, decrease the duration of patient stays in medical facilities, and reduce overall treatment costs.

Analysing and Evaluating the Performance of Deep-Learning-Based Arrhythmia Detection Using Electrocardiogram Signals

Cardiac arrhythmia is a cardiac irregularity that impacts a significant number of individuals globally. Certain arrhythmias may be benign or occur only once, while recurring arrhythmias have the potential to cause organ failure, increase the risk of stroke by a factor of five, and even lead to sudden cardiac death. Thus, in order to identify and treat arrhythmia and prevent potentially fatal cardiac problems, the rapid detection and categorization of Electrocardiogram (ECG) signals is of paramount importance. The non-invasive technique employs electrodes to examine the electrical potentials of the heart, facilitating the detection of structural and functional irregularities that contribute to the diagnosis of cardiovascular illnesses. Nevertheless, the high likelihood of manual interpretation, which is both time-consuming and susceptible to error caused by weariness, poses a significant challenge for cardiologists in identifying and diagnosing cardiac issues. In recent times, there has been a growing utilization of Deep Learning (DL) models in the field of arrhythmia prediction, with the aim of enhancing clinical decision-making and potentially mitigating the likelihood of valetudinarian fatality. These models enhance the diagnostic capabilities of electrocardiography by detecting pathological situations, extracting anatomically meaningful data, evaluating cardio-motion, and assessing the quality of echo images. As a result, they serve as an alternative tool for precise diagnosis and treatment of arrhythmias. In order to forecast and categorize arrhythmias based on ECG signal data, this study provides a comprehensive review of various deep learning approaches. The initial section of the study provides a concise overview of various deep learning-based prediction models that have been developed by multiple researchers for echocardiography systems. Subsequently, a comparative analysis is undertaken to comprehend the limitations of those algorithms and propose a novel approach to improve the accuracy of cardiac view classification in echocardiography systems.

Real-time tunnel lining leakage image semantic segmentation via multiple attention mechanisms

One of the key objectives in tunnel illness detection is identifying tunnel lining leakage, and deep learning-based image semantic segmentation approaches can automatically locate tunnel lining leakage. However, in order to meet the real-time processing needs of professional mobile inspection equipment, existing leakage image segmentation approaches have difficulties in identifying real-time, dealing with voids, and dealing with edge discontinuities in the leaking zone. To address the aforementioned issues, this study introduces the PP-LiteSeg-Attn model, which takes the real-time semantic segmentation model PP-LiteSeg-B as baseline model, and combines the multi-layer CBAM attention mechanism and the CoT attention mechanism. Using the publically available dataset Water-Leakage, we trained and validated the PP-LiteSeg-Attn model, and attained IoU and F1 values of 88.18% and 93.72%, respectively, outperforming similar models in both measures. Extensive experiments show that the segmentation speed of the PP-LiteSeg-Attn model reaches 112.28 FPS, which meets real-time requirements, and that the model can effectively solve problems such as the appearance of voids in the seepage area, discontinuity, and fuzzy segmentation of seepage edges. The PP-LiteSeg-Attn model is better applicable to complicated tunnel settings, offering technical references for real-time diagnosis of tunnel illnesses.