Modern Diagnostic Imaging Technique Applications and Risk Factors in the Medical Field: A Review

Synthesis of Hybrid Data Consisting of Chest Radiographs and Tabular Clinical Records Using Dual Generative Models for COVID-19 Positive Cases

To generate synthetic medical data incorporating image-tabular hybrid data by merging an image encoding/decoding model with a table-compatible generative model and assess their utility. We used 1342 cases from the Stony Brook University Covid-19-positive cases, comprising chest X-ray radiographs (CXRs) and tabular clinical data as a private dataset (pDS). We generated a synthetic dataset (sDS) through the following steps: (I) dimensionally reducing CXRs in the pDS using a pretrained encoder of the auto-encoding generative adversarial networks (αGAN) and integrating them with the correspondent tabular clinical data; (II) training the conditional tabular GAN (CTGAN) on this combined data to generate synthetic records, encompassing encoded image features and clinical data; and (III) reconstructing synthetic images from these encoded image features in the sDS using a pretrained decoder of the αGAN. The utility of sDS was assessed by the performance of the prediction models for patient outcomes (deceased or discharged). For the pDS test set, the area under the receiver operating characteristic (AUC) curve was calculated to compare the performance of prediction models trained separately with pDS, sDS, or a combination of both. We created an sDS comprising CXRs with a resolution of 256 × 256 pixels and tabular data containing 13 variables. The AUC for the outcome was 0.83 when the model was trained with the pDS, 0.74 with the sDS, and 0.87 when combining pDS and sDS for training. Our method is effective for generating synthetic records consisting of both images and tabular clinical data.

MicroRNA biomarkers as next-generation diagnostic tools for neurodegenerative diseases: a comprehensive review

Neurodegenerative diseases (NDs) are characterized by abnormalities within neurons of the brain or spinal cord that gradually lose function, eventually leading to cell death. Upon examination of affected tissue, pathological changes reveal a loss of synapses, misfolded proteins, and activation of immune cells-all indicative of disease progression-before severe clinical symptoms become apparent. Early detection of NDs is crucial for potentially administering targeted medications that may delay disease advancement. Given their complex pathophysiological features and diverse clinical symptoms, there is a pressing need for sensitive and effective diagnostic methods for NDs. Biomarkers such as microRNAs (miRNAs) have been identified as potential tools for detecting these diseases. We explore the pivotal role of miRNAs in the context of NDs, focusing on Alzheimer's disease, Parkinson's disease, Multiple sclerosis, Huntington's disease, and Amyotrophic Lateral Sclerosis. The review delves into the intricate relationship between aging and NDs, highlighting structural and functional alterations in the aging brain and their implications for disease development. It elucidates how miRNAs and RNA-binding proteins are implicated in the pathogenesis of NDs and underscores the importance of investigating their expression and function in aging. Significantly, miRNAs exert substantial influence on post-translational modifications (PTMs), impacting not just the nervous system but a wide array of tissues and cell types as well. Specific miRNAs have been found to target proteins involved in ubiquitination or de-ubiquitination processes, which play a significant role in regulating protein function and stability. We discuss the link between miRNA, PTM, and NDs. Additionally, the review discusses the significance of miRNAs as biomarkers for early disease detection, offering insights into diagnostic strategies.

Economic Indicators, Quantity and Quality of Health Care Resources Affecting Post-surgical Mortality

OBJECTIVE

to identify correlations between quality and quantity of health care resources, national economic indicators, and postoperative in-hospital mortality as reported in the EUSOS study.

METHODS

Different variables were identified from a series of publicly available database. Postoperative in-hospital mortality was identified as reported by EUSOS study. Spearman non-parametric and Coefficients of non-linear regression were calculated.

RESULTS

Quality of health care resources was strongly and negatively correlated to postoperative in-hospital mortality. Quantity of health care resources were negatively and moderately correlated to postoperative in-hospital mortality. National economic indicators were moderately and negatively correlated to postoperative in-hospital mortality. General mortality, as reported by WHO, was positively but very moderately correlated with postoperative in-hospital mortality.

CONCLUSIONS

Postoperative in-hospital mortality is strongly determined by quality of health care instead of quantity of health resources and health expenditures. We suggest that improving the quality of health care system might reduce postoperative in-hospital mortality.

A robust vessel-labeling pipeline with high tissue clearing compatibility for 3D mapping of vascular networks

The combination of vessel-labeling, tissue-clearing, and light-sheet imaging techniques provides a potent tool for accurately mapping vascular networks, enabling the assessment of vascular remodeling in vascular-related disorders. However, most vascular labeling methods face challenges such as inadequate labeling efficiency or poor compatibility with current tissue clearing technology, which significantly undermines the image quality. To address this limitation, we introduce a vessel-labeling pipeline, termed Ultralabel, which relies on a specially designed dye hydrogel containing lysine-fixable dextran and gelatins for double enhancement. Ultralabel demonstrates not only excellent vessel-labeling capability but also strong compatibility with all tissue clearing methods tested, which outperforms other vessel-labeling methods. Consequently, Ultralabel enables fine mapping of vascular networks in diverse organs, as well as multi-color labeling alongside other labeling techniques. Ultralabel should provide a robust and user-friendly method for obtaining 3D vascular networks in different biomedical applications.

Incidence and predictors of postoperative complications in Sub-Saharan Africa: a systematic review and meta-analysis

Background

Postoperative complications remain a significant challenge, especially in settings where healthcare access and infrastructure disparities exacerbate. This systematic review and meta-analysis aimed to determine the pooled incidence and risk factors of postoperative complications among patients undergoing essential surgery in Sub-Saharan Africa (SSA).

Method

PubMed/MEDLINE, EMBASE, CINAHL, Web of Science, and Google Scholar were searched from January 2010 to November 2022 for completed studies reporting the incidence and risk factors associated with postoperative complications among patients undergoing essential surgery in SSA. Severity of postoperative complications was ranked based on the Clavien-Dindo classification system, while risk factors were classified into three groups based on the Donabedian structure-process-outcome quality evaluation framework. Studies quality was appraised using the JBI Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI), and data were analyzed using Comprehensive Meta-Analysis (CMA) software. The study protocol adhered to the PRISMA guidelines and was registered in PROSPERO (CRD42023414342).

Results

The meta-analysis included 19 studies (10 cohort and 9 cross-sectional) comprising a total of 24,136 patients. The pooled incidence of postoperative complications in SSA was 20.2% (95% CI: 18.7%-21.8%), with a substantial heterogeneity of incidence observed. The incidence varied from 14.6% to 27.5% based on the Clavien-Dindo classification. The random-effects model indicated significant heterogeneity among the studies (Q = 54.202, I = 66.791%, p < 0.001). Contributing factors to postoperative complications were: structure-related factors, which included the availability and accessibility of resources, as well as the quality of both the surgical facility and the hospital.; process-related factors, which encompassed surgical skills, adherence to protocols, evidence-based practices, and the quality of postoperative care; and patient outcome-related factors such as age, comorbidities, alcohol use, and overall patient health status.

Conclusion

The meta-analysis reveals a high frequency of postoperative complications in SSA, with noticeable discrepancies among the studies. The analysis highlights a range of factors, encompassing structural, procedural, and patient outcome-related aspects, that contribute to these complications. The findings underscore the necessity for targeted interventions aimed at reducing complications and improving the overall quality of surgical care in the region.

Systematic Reviews Registration

https://www.crd.york.ac.uk/PROSPERO/, identifier (CRD42023414342).

Patient perspectives on ionising radiation exposure from computed tomography in Saudi Arabia: a knowledge and perception study

The objective of this study was to evaluate patient knowledge and understanding of ionising radiation and dosage, as well as the accompanying risks related to computed tomography scans. A total of 412 outpatients who underwent computed tomography (CT) scans were surveyed to assess their understanding of radiation dose and exposure risks. CT was correctly classified as an ionising radiation by 56.8% of the respondents. More than half of the patients reported that a CT scan increases the probability of inducing cancer. Awareness of varying radiation doses in different CT exams was noted in 75.2% of patients, but only 21.4% reported having discussions with their physician about radiation dose. Gender, age and employment were significantly correlated with knowledge levels. The survey findings indicate a limited understanding of the hazards associated with ionising radiation used in CT scans, highlighting a need for increased awareness and education on radiation protection to ensure informed consent.

Cancer Immunotherapy and Medical Imaging Research Trends from 2003 to 2023: A Bibliometric Analysis

Purpose

With the rapid development of immunotherapy, cancer treatment has entered a new phase. Medical imaging, as a primary diagnostic method, is closely related to cancer immunotherapy. However, until now, there has been no systematic bibliometric analysis of the state of this field. Therefore, the main purpose of this article is to clarify the past research trajectory, summarize current research hotspots, reveal dynamic scientific developments, and explore future research directions.

Patients and Methods

A comprehensive search was conducted on the Web of Science Core Collection (WoSCC) database to identify publications related to immunotherapy specifically for the medical imaging of carcinoma. The search spanned the period from the year 2003 to 2023. Several analytical tools were employed. These included CiteSpace (6.2.4), and the Microsoft Office Excel (2016).

Results

By searching the database, a total of 704 English articles published between 2003 and 2023 were obtained. We have observed a rapid increase in the number of publications since 2018. The two most active countries are the United States (n=265) and China (n=170). Pittock, Sean J and Abu-sbeih, Hamzah are very concerned about the relationship between cancer immunotherapy and medical images and have published more academic papers (n = 5; n = 4). Among the top 10 co-cited authors, Topalian Sl (n=43) cited ranked first, followed by Graus F (n=40) cited. According to clustering, timeline, and burst word analysis, the results show that the current research focus is on "MRI", "deep learning", "tumor microenvironment" and so on.

Conclusion

Medical imaging and cancer immunotherapy are hot topics. The United States is the country with the most publications and the greatest influence in this field, followed by China. "MRI", "PET/PET-CT", "deep learning", "immune-related adverse events" and "tumor microenvironment" are currently hot research topics and potential targets.

Deep Learning Approaches for Medical Image Analysis and Diagnosis

In addition to enhancing diagnostic accuracy, deep learning techniques offer the potential to streamline workflows, reduce interpretation time, and ultimately improve patient outcomes. The scalability and adaptability of deep learning algorithms enable their deployment across diverse clinical settings, ranging from radiology departments to point-of-care facilities. Furthermore, ongoing research efforts focus on addressing the challenges of data heterogeneity, model interpretability, and regulatory compliance, paving the way for seamless integration of deep learning solutions into routine clinical practice. As the field continues to evolve, collaborations between clinicians, data scientists, and industry stakeholders will be paramount in harnessing the full potential of deep learning for advancing medical image analysis and diagnosis. Furthermore, the integration of deep learning algorithms with other technologies, including natural language processing and computer vision, may foster multimodal medical data analysis and clinical decision support systems to improve patient care. The future of deep learning in medical image analysis and diagnosis is promising. With each success and advancement, this technology is getting closer to being leveraged for medical purposes. Beyond medical image analysis, patient care pathways like multimodal imaging, imaging genomics, and intelligent operating rooms or intensive care units can benefit from deep learning models.

Advancing MRI with magnetic nanoparticles: a comprehensive review of translational research and clinical trials

The nexus of advanced technology and medical therapeutics has ushered in a transformative epoch in contemporary medicine. Within this arena, Magnetic Resonance Imaging (MRI) emerges as a paramount tool, intertwining the advancements of technology with the art of healing. MRI's pivotal role is evident in its broad applicability, spanning from neurological diseases, soft-tissue and tumour characterization, to many more applications. Though already foundational, aspirations remain to further enhance MRI's capabilities. A significant avenue under exploration is the incorporation of innovative nanotechnological contrast agents. Forefront among these are Superparamagnetic Iron Oxide Nanoparticles (SPIONs), recognized for their adaptability and safety profile. SPION's intrinsic malleability allows them to be tailored for improved biocompatibility, while their functionality is further broadened when equipped with specific targeting molecules. Yet, the path to optimization is not devoid of challenges, from renal clearance concerns to potential side effects stemming from iron overload. This review endeavors to map the intricate journey of SPIONs as MRI contrast agents, offering a chronological perspective of their evolution and deployment. We provide an in-depth current outline of the most representative and impactful pre-clinical and clinical studies centered on the integration of SPIONs in MRI, tracing their trajectory from foundational research to contemporary applications.

A bilateral filtering-based image enhancement for Alzheimer disease classification using CNN

This study aims to develop an optimally performing convolutional neural network to classify Alzheimer's disease into mild cognitive impairment, normal controls, or Alzheimer's disease classes using a magnetic resonance imaging dataset. To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. Subsequently, a convolutional neural network model comprising four convolutional layers and two hidden layers was devised for classifying Alzheimer's disease into three (3) distinct categories, namely mild cognitive impairment, Alzheimer's disease, and normal controls. The model was trained and evaluated using a 10-fold cross-validation sampling approach with a learning rate of 0.001 and 200 training epochs at each instance. The proposed model yielded notable results, such as an accuracy of 93.45% and an area under the curve value of 0.99 when trained on the three classes. The model further showed superior results on binary classification compared with existing methods. The model recorded 94.39%, 94.92%, and 95.62% accuracies for Alzheimer's disease versus normal controls, Alzheimer's disease versus mild cognitive impairment, and mild cognitive impairment versus normal controls classes, respectively.

Spectral Photon-Counting Computed Tomography: Technical Principles and Applications in the Assessment of Cardiovascular Diseases

Spectral Photon-Counting Computed Tomography (SPCCT) represents a groundbreaking advancement in X-ray imaging technology. The core innovation of SPCCT lies in its photon-counting detectors, which can count the exact number of incoming x-ray photons and individually measure their energy. The first part of this review summarizes the key elements of SPCCT technology, such as energy binning, energy weighting, and material decomposition. Its energy-discriminating ability represents the key to the increase in the contrast between different tissues, the elimination of the electronic noise, and the correction of beam-hardening artifacts. Material decomposition provides valuable insights into specific elements' composition, concentration, and distribution. The capability of SPCCT to operate in three or more energy regimes allows for the differentiation of several contrast agents, facilitating quantitative assessments of elements with specific energy thresholds within the diagnostic energy range. The second part of this review provides a brief overview of the applications of SPCCT in the assessment of various cardiovascular disease processes. SPCCT can support the study of myocardial blood perfusion and enable enhanced tissue characterization and the identification of contrast agents, in a manner that was previously unattainable.

A Comprehensive Review on Biofilms in Otorhinolaryngology: Understanding the Pathogenesis, Diagnosis, and Treatment Strategies

Biofilms, structured communities of microorganisms encased in a self-produced matrix, pose significant challenges in otorhinolaryngology due to their role in chronic and recurrent infections affecting the ear, nose, and throat (ENT) region. This review provides an overview of biofilms, emphasizing their formation, pathogenesis, diagnosis, and treatment strategies in otorhinolaryngological disorders. Biofilms are pivotal in chronic rhinosinusitis (CRS), otitis media, laryngopharyngeal reflux (LPR), and tonsillitis, contributing to treatment resistance and disease recurrence. Current diagnostic techniques, including imaging modalities, microbiological cultures, and molecular techniques, are discussed, alongside emerging technologies. Treatment strategies, ranging from conventional antibiotics to alternative therapies, such as biofilm disruptors, phage therapy, and immunomodulation, are evaluated in terms of their efficacy and potential clinical applications. The review underscores the significance of understanding biofilms in otorhinolaryngology and highlights the need for tailored approaches to diagnosis and management to improve patient outcomes.

The importance, benefits, and future of nanobiosensors for infectious diseases

Infectious diseases, caused by pathogenic microorganisms such as bacteria, viruses, parasites, or fungi, are crucial for efficient disease management, reducing morbidity and mortality rates and controlling disease spread. Traditional laboratory-based diagnostic methods face challenges such as high costs, time consumption, and a lack of trained personnel in resource-poor settings. Diagnostic biosensors have gained momentum as a potential solution, offering advantages such as low cost, high sensitivity, ease of use, and portability. Nanobiosensors are a promising tool for detecting and diagnosing infectious diseases such as coronavirus disease, human immunodeficiency virus, and hepatitis. These sensors use nanostructured carbon nanotubes, graphene, and nanoparticles to detect specific biomarkers or pathogens. They operate through mechanisms like the lateral flow test platform, where a sample containing the biomarker or pathogen is applied to a test strip. If present, the sample binds to specific recognition probes on the strip, indicating a positive result. This binding event is visualized through a colored line. This review discusses the importance, benefits, and potential of nanobiosensors in detecting infectious diseases.

Unveiling the future: Advancements in MRI imaging for neurodegenerative disorders

Neurodegenerative disorders represent a significant and growing global health challenge, necessitating continuous advancements in diagnostic tools for accurate and early detection. This work explores the recent progress in Magnetic Resonance Imaging (MRI) techniques and their application in the realm of neurodegenerative disorders. The introductory section provides a comprehensive overview of the study's background, significance, and objectives. Recognizing the current challenges associated with conventional MRI, the manuscript delves into advanced imaging techniques such as high-resolution structural imaging (HR-MRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and positron emission tomography-MRI (PET-MRI) fusion. Each technique is critically examined regarding its potential to address theranostic limitations and contribute to a more nuanced understanding of the underlying pathology. A substantial portion of the work is dedicated to exploring the applications of advanced MRI in specific neurodegenerative disorders, including Parkinson's disease, Alzheimer's disease, Huntington's disease, and Amyotrophic Lateral Sclerosis (ALS). In addressing the future landscape, the manuscript examines technological advances, including the integration of machine learning and artificial intelligence in neuroimaging. The conclusion summarizes key findings, outlines implications for future research, and underscores the importance of these advancements in reshaping our understanding and approach to neurodegenerative disorders.

Medical Imaging in Pregnancy: Safety, Appropriate Utilization, and Alternative Modalities for Imaging Pregnant Patients

This article reviews the existing literature on diagnostic and medical imaging of pregnant women, the risks and safety measures of different medical imaging modalities, and alternative modalities for imaging pregnant patients. Different medical imaging modalities such as MRI, CT scan, ultrasound, nuclear medicine, and X-ray imaging help to evaluate women with recognized or unrecognized pregnancies and identify any underlying complications among pregnant patients. Fetuses are more sensitive to radiation and the effects of medical imaging as compared to adults since they have a rapidly developing cell system. During cell proliferation, migration, and differentiation, fetuses suffer greatly from imaging radiation since they are developing under a dynamic system. To ensure safety, pregnant women should discuss the benefits and risks of medical imaging with their physicians. In addition, radiologists should not perform any medical imaging procedure without the patient's consent, unless the patient cannot make any sound decision. Fetal risks of medical imaging include slow growth and development of the fetus, abortion, malformations, impaired brain function, abnormal childhood growth, and neurological development. Diagnostic imaging procedures are necessary when a condition that needs medical evaluation arises during pregnancy such as appendicitis.

Impedimetric Detection of Cancer Markers Based on Nanofiber Copolymers

The sensitive determination of folate receptors (FRs) in the early stages of cancer is of great significance for controlling the progression of cancerous cells. Many folic acid (FA)-based electrochemical biosensors have been utilized to detect FRs with promising performances, but most were complicated, non-reproducible, non-biocompatible, and time and cost consuming. Here, we developed an environmentally friendly and sensitive biosensor for FR detection. We proposed an electrochemical impedimetric biosensor formed by nanofibers (NFs) of bio-copolymers prepared by electrospinning. The biosensor combines the advantages of bio-friendly polymers, such as sodium alginate (SA) and polyethylene oxide (PEO) as an antifouling polymer, with FA as a biorecognition element. The NF nanocomposites were characterized using various techniques, including SEM, FTIR, zeta potential (ZP), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS). We evaluated the performance of the NF biosensor using EIS and demonstrated FR detection in plasma with a limit of detection of 3 pM. Furthermore, the biosensor showed high selectivity, reliability, and good stability when stored for two months. This biosensor was constructed from 'green credentials' holding polymers that are highly needed in the new paradigm shift in the medical industry.

Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences

To develop diagnostic imaging approaches, this paper emphasizes the transformational potential of merging geophysics with health sciences. Diagnostic imaging technology improvements have transformed the health sciences by enabling earlier and more precise disease identification, individualized therapy, and improved patient care. This review article examines the connection between geophysics and diagnostic imaging in the field of health sciences. Geophysics, which is typically used to explore Earth's subsurface, has provided new uses of its methodology in the medical field, providing innovative solutions to pressing medical problems. The article examines the different geophysical techniques like electrical imaging, seismic imaging, and geophysics and their corresponding imaging techniques used in health sciences like tomography, magnetic resonance imaging, ultrasound imaging, etc. The examination includes the description, similarities, differences, and challenges associated with these techniques and how modified geophysical techniques can be used in imaging methods in health sciences. Examining the progression of each method from geophysics to medical imaging and its contributions to illness diagnosis, treatment planning, and monitoring are highlighted. Also, the utilization of geophysical data analysis techniques like signal processing and inversion techniques in image processing in health sciences has been briefly explained, along with different mathematical and computational tools in geophysics and how they can be implemented for image processing in health sciences. The key findings include the development of machine learning and artificial intelligence in geophysics-driven medical imaging, demonstrating the revolutionary effects of data-driven methods on precision, speed, and predictive modeling.

Advancing athletic assessment by integrating conventional methods with cutting-edge biomedical technologies for comprehensive performance, wellness, and longevity insights

In modern athlete assessment, the integration of conventional biochemical and ergophysiologic monitoring with innovative methods like telomere analysis, genotyping/phenotypic profiling, and metabolomics has the potential to offer a comprehensive understanding of athletes' performance and potential longevity. Telomeres provide insights into cellular functioning, aging, and adaptation and elucidate the effects of training on cellular health. Genotype/phenotype analysis explores genetic variations associated with athletic performance, injury predisposition, and recovery needs, enabling personalization of training plans and interventions. Metabolomics especially focusing on low-molecular weight metabolites, reveal metabolic pathways and responses to exercise. Biochemical tests assess key biomarkers related to energy metabolism, inflammation, and recovery. Essential elements depict the micronutrient status of the individual, which is critical for optimal performance. Echocardiography provides detailed monitoring of cardiac structure and function, while burnout testing evaluates psychological stress, fatigue, and readiness for optimal performance. By integrating this scientific testing battery, a multidimensional understanding of athlete health status can be achieved, leading to personalized interventions in training, nutrition, supplementation, injury prevention, and mental wellness support. This scientifically rigorous approach hereby presented holds significant potential for improving athletic performance and longevity through evidence-based, individualized interventions, contributing to advances in the field of sports performance optimization.

Bioanalytical evaluation of wound depth and musculoskeletal injuries: Synergizing focused assessment with sonography for trauma with computed tomography/magnetic resonance imaging in orthopaedic trauma care

Orthopaedic trauma care frequently necessitates prompt and precise assessment of musculoskeletal injuries and wound depth. The potential for improved diagnostic accuracy and patient outcomes exists with the integration of sophisticated imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) with focused assessment with sonography for trauma (FAST). The purpose of this research was to examine the benefits and drawbacks of this integrative method in the clinical environment. From June 2022 to September 2023, 250 patients who were admitted to Ningbo University Affiliated People's Hospital, participated in this cross-sectional observational study. Following the administration of FAST, CT and MRI were utilized to evaluate orthopaedic injuries and skin wounds in patients. Analyses of data centred on the precision of diagnoses, the influence of treatment decisions and patient outcomes. Aged and gendered differently, the study participants sustained the variety of injuries and superficial wounds that were predominantly the result of traffic accidents. The FAST assay exhibited sensitivity of 65%, specificity of 80% and 72% overall accuracy. MRI demonstrated the finest diagnostic performance (85% sensitivity, 95% specificity and 89% accuracy), whereas CT scans offered improved diagnostic efficacy (80% sensitivity, 90% specificity and 84% accuracy). Treatment decisions were substantially impacted by integration of these imaging modalities, resulting in modifications in 20%-35% of cases, depending on the specific modality employed. Specifically, MRI played a pivotal role in informing treatment approaches, influencing non-surgical as well as surgical procedures. This study substantiates the significant advantages of integrating FAST with CT and MRI in orthopaedic trauma care, particularly in the accurate assessment of wound depth. The synergistic use of these imaging techniques not only enhances diagnostic precision but also positively impacts treatment strategies and patient outcomes, emphasizing the need for a comprehensive diagnostic approach in trauma care settings.

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.

Recent Progress in Enhanced Cancer Diagnosis, Prognosis, and Monitoring Using a Combined Analysis of the Number of Circulating Tumor Cells (CTCs) and Other Clinical Parameters

Analysis of circulating tumor cells (CTCs) holds promise to diagnose cancer or monitor its development. Among the methods, counting CTC numbers in blood samples could be the simplest way to implement it. Nevertheless, its clinical utility has not yet been fully accepted. The reasons could be due to the rarity and heterogeneity of CTCs in blood samples that could lead to misleading results from assays only based on single CTC counts. To address this issue, a feasible direction is to combine the CTC counts with other clinical data for analysis. Recent studies have demonstrated the use of this new strategy for early detection and prognosis evaluation of cancers, or even for the distinguishment of cancers with different stages. Overall, this approach could pave a new path to improve the technical problems in the clinical applications of CTC counting techniques. In this review, the information relevant to CTCs, including their characteristics, clinical use of CTC counting, and technologies for CTC enrichment, were first introduced. This was followed by discussing the challenges and new perspectives of CTC counting techniques for clinical applications. Finally, the advantages and the recent progress in combining CTC counts with other clinical parameters for clinical applications have been discussed.

High-Resolution Imaging Insights into Shoulder Joint Pain: A Comprehensive Review of Ultrasound and Magnetic Resonance Imaging (MRI)

Shoulder joint pain is a complex and prevalent clinical concern affecting individuals across various ages and lifestyles. This review delves into the pivotal role of high-resolution imaging techniques, namely ultrasound and magnetic resonance imaging (MRI), in the comprehensive assessment and management of shoulder joint pain. We explore the anatomical foundations of the shoulder, common etiologies of pain, and the significance of precise diagnosis. High-resolution imaging facilitates the identification of various shoulder pathologies and is crucial in treatment planning, surgical interventions, and long-term prognosis assessment. We examine emerging technologies, discuss challenges and limitations, and chart potential future developments, emphasizing the ongoing evolution of imaging in this critical healthcare domain. In conclusion, high-resolution imaging is an indispensable tool, continually advancing to meet the diagnostic and therapeutic needs of individuals grappling with shoulder joint pain.

Liability, risks, and recommendations for ultrasound use in the diagnosis of obstetrics diseases

This literature review will summarize the liability issues, risks, and ultrasound recommendations for diagnosing obstetrics diseases. One liability issue is related to misdiagnosis or failure to detect abnormalities during an ultrasound examination. Ultrasound images can be subjective interpretations, and errors may occur due to factors such as operator skill, equipment limitations, or fetal positioning. Another liability concern is related to the potential adverse effects of ultrasound exposure on both the mother and fetus. While extensive research has shown that diagnostic ultrasound is generally safe when used appropriately, there are still uncertainties regarding long-term effects. Some studies suggest a possible association between prolonged or excessive exposure to ultrasound waves and adverse outcomes such as low birth weight, developmental delays, or hearing impairment. Additionally, obtaining informed consent from patients is crucial in mitigating liability risks. Patients should be informed about the purpose of the ultrasound examination, its benefits, limitations, potential risks (even if minimal), and any alternative diagnostic options available. This ensures that patients know the procedure and can make informed decisions about their healthcare. Proper documentation helps establish a clear record of the care provided and can serve as evidence in any legal disputes.

From Prevention to Management: Understanding Postoperative Infections in Gynaecology

This narrative review examines the multifaceted realm of postoperative infections in gynaecology, addressing their significance, types, risk factors, prevention, management, and emerging trends. Postoperative infections, encompassing surgical site infections, urinary tract infections, and pelvic inflammatory disease, pose considerable challenges in patient care, warranting comprehensive exploration. Strategies for prevention include preoperative patient assessment, antimicrobial prophylaxis, and aseptic techniques. Intraoperative measures encompass infection control and instrument sterilization, while postoperative care involves wound management and early infection detection. Diagnostic tools, including blood tests, imaging, and microbiological cultures, aid in timely identification. Management strategies encompass antibiotic therapy, surgical interventions, supportive care, and addressing complications. The review underscores the necessity of personalized approaches, multidisciplinary collaboration, and innovative technologies in future infection management. It calls for ongoing research, heightened awareness, and meticulous care to minimize the impact of postoperative infections and optimize patient outcomes.

A Comprehensive Review of the Vascular Consequences of Diabetes in the Lower Extremities: Current Approaches to Management and Evaluation of Clinical Outcomes

Diabetes mellitus is a global health concern characterized by chronic hyperglycemia, and its vascular consequences in the lower extremities pose significant challenges for individuals living with the condition. This comprehensive review delves into the multifaceted landscape of diabetes-related vascular complications in the lower limbs, with a primary focus on current strategies for management and the evaluation of clinical outcomes. This review achieves several critical objectives by synthesizing existing knowledge and research findings. It elucidates the intricate pathophysiological mechanisms underpinning these complications, shedding light on the cellular and molecular processes involved. Additionally, it outlines clinical assessment and diagnostic strategies used to identify and stratify risk, ranging from cutting-edge imaging techniques to clinical examinations. The review comprehensively examines current management strategies, encompassing lifestyle modifications, pharmacological interventions, surgical procedures, and wound care practices. Moreover, it assesses and analyzes clinical outcomes, including limb salvage rates, amputation rates, and overall quality of life for individuals undergoing treatment. In addressing the challenges faced in managing these complications, this review aims to contribute to improved patient care. It proposes future research directions to enhance the management and outcomes of diabetes-related vascular consequences in the lower extremities.

Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstruction

Computed tomography (CT) and magnetic resonance imaging (MRI) are two widely used clinical imaging modalities for non-invasive diagnosis. However, both of these modalities come with certain problems. CT uses harmful ionising radiation, and MRI suffers from slow acquisition speed. Both problems can be tackled by undersampling, such as sparse sampling. However, such undersampled data leads to lower resolution and introduces artefacts. Several techniques, including deep learning based methods, have been proposed to reconstruct such data. However, the undersampled reconstruction problem for these two modalities was always considered as two different problems and tackled separately by different research works. This paper proposes a unified solution for both sparse CT and undersampled radial MRI reconstruction, achieved by applying Fourier transform-based pre-processing on the radial MRI and then finally reconstructing both modalities using sinogram upsampling combined with filtered back-projection. The Primal-Dual network is a deep learning based method for reconstructing sparsely-sampled CT data. This paper introduces Primal-Dual UNet, which improves the Primal-Dual network in terms of accuracy and reconstruction speed. The proposed method resulted in an average SSIM of 0.932±0.021 while performing sparse CT reconstruction for fan-beam geometry with a sparsity level of 16, achieving a statistically significant improvement over the previous model, which resulted in 0.919±0.016. Furthermore, the proposed model resulted in 0.903±0.019 and 0.957±0.023 average SSIM while reconstructing undersampled brain and abdominal MRI data with an acceleration factor of 16, respectively - statistically significant improvements over the original model, which resulted in 0.867±0.025 and 0.949±0.025. Finally, this paper shows that the proposed network not only improves the overall image quality, but also improves the image quality for the regions-of-interest: liver, kidneys, and spleen; as well as generalises better than the baselines in presence the of a needle.

Review of Development and Recent Advances in Biomedical X-ray Fluorescence Imaging

The use of X-rays for non-invasive imaging has a long history, which has resulted in several well-established methods in preclinical as well as clinical applications, such as tomographic imaging or computed tomography. While projection radiography provides anatomical information, X-ray fluorescence analysis allows quantitative mapping of different elements in samples of interest. Typical applications so far comprise the identification and quantification of different elements and are mostly located in material sciences, archeology and environmental sciences, whereas the use of the technique in life sciences has been strongly limited by intrinsic spectral background issues arising in larger objects, so far. This background arises from multiple Compton-scattering events in the objects of interest and strongly limits the achievable minimum detectable marker concentrations. Here, we review the history and report on the recent promising developments of X-ray fluorescence imaging (XFI) in preclinical applications, and provide an outlook on the clinical translation of the technique, which can be realized by reducing the above-mentioned intrinsic background with dedicated algorithms and by novel X-ray sources.

The diagnostic value of the computed tomography scan and ultrasonography in acute appendicitis

The most prevalent cause of emergency abdominal surgery is acute appendicitis. Ultrasonography is safe and widely available, although it's operator-dependent and difficult for people with massive bodies. Computed tomography (CT) scans are more accurate than ultrasonography, with a 93 to 98% accuracy rate. The goal of this investigation is to evaluate the diagnostic value of ultrasonography and CT scanning for acute appendicitis. This is a cross-sectional study that was performed on 231 patients with suspected with acute appendicitis. The Alvarado score was initially used to diagnose acute appendicitis. A radiologist performed abdominal ultrasonography on all patients. If the results of the ultrasonography were negative or unclear, a CT scan was performed using oral contrast. Finally, all ultrasonography and CT scan data were reevaluated by an experienced radiologist and compared to the patient's final diagnosis in the case of surgery and pathology results. Comparisons between the two groups were performed. The sensitivity, specificity, and positive and negative predictive value of ultrasonography according to pathology results in patients with low clinical suspicion were 74.9%, 63.4%, 94.3%, and 67.6%, respectively. The sensitivity, specificity, positive and negative predictive value of CT scans based on pathology results were 87.9%, 81.8%, 94.7%, and 79.3%, respectively, in patients with low clinical suspicion. The CT scan results in female patients suspected of appendicitis were completely consistent with the pathology results. The CT scan demonstrated greater specificity and sensitivity in diagnosing acute appendicitis compared to abdominal ultrasonography.