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In electrocardiograms (ECGs), multiple forms of encryption and preservation formats create difficulties for data sharing and retrospective disease analysis. Additionally, photography and storage using mobile devices are convenient, but the images acquired contain different noise interferences. To address this problem, a suite of novel methodologies was proposed for converting paper-recorded ECGs into digital data. Firstly, this study ingeniously removed gridlines by utilizing the Hue Saturation Value (HSV) spatial properties of ECGs. Moreover, this study introduced an innovative adaptive local thresholding method with high robustness for foreground–background separation. Subsequently, an algorithm for the automatic recognition of calibration square waves was proposed to ensure consistency in amplitude, rather than solely in shape, for digital signals. The original signal reconstruction algorithm was validated with the MIT–BIH and PTB databases by comparing the difference between the reconstructed and the original signals. Moreover, the mean of the Pearson correlation coefficient was 0.97 and 0.98, respectively, while the mean absolute errors were 0.324 and 0.241, respectively. The method proposed in this study converts paper-recorded ECGs into a digital format, enabling direct analysis using software. Automated techniques for acquiring and restoring ECG reference voltages enhance the reconstruction accuracy. This innovative approach facilitates data storage, medical communication, and remote ECG analysis, and minimizes errors in remote diagnosis. [ABSTRACT FROM AUTHOR]

Background: The lack of a clear answer regarding the efficacy of physiotherapy in the treatment of cubital tunnel syndrome (CuTS) has led to attempts to critically assess the scientific studies conducted to date. Materials and Methods: Two databases (MEDLINE via PubMed and PEDro) and Google Scholar were used to search for papers. The inclusion criteria were randomized controlled trials, case series, and case reports that evaluate the effects of physiotherapy in the treatment of patients with CuTS. Results: A total of 18 studies met the eligibility criteria, capturing a total of 425 participants. Seven papers were randomized controlled trials, three more described prospective studies without a control group, and eight papers contained case reports. An analysis of the literature evaluating the effectiveness of various forms of broadly defined physiotherapy indicates that their use can have a beneficial effect in reducing many subjective and objective symptoms and improving function. In the majority of papers included in this review, their authors indicated positive therapeutic effects. Only one randomized controlled trial reported no change following therapy. It can therefore be stated that the results of the research conducted so far are optimistic. However, only 7 of the 18 papers were randomized controlled trials, while 3 were prospective studies, and 8 papers were case studies, in which 23 people with CuTS were studied. Conclusions: The small number of randomized clinical trials and their considerable heterogeneity do not allow firm conclusions to be drawn about the effectiveness of physiotherapy in the conservative treatment of CuTS. [ABSTRACT FROM AUTHOR]

Background Detecting total cholesterol in dried blood spots could aid in identifying individuals with a high likelihood of familial hypercholesterolemia and could be used as a screening measure. This study aims to assess the diagnostic accuracy of dried blood spots on Whatman 903 paper cards using a manual enzymatic technique. Methods: A total of 394 samples were collected as serum and dried blood spots were compared. Cholesterol was determined in serum using the automated reference method, while cholesterol on paper was measured using a manual enzymatic method. Within- and between-day diagnostic variability were analyzed. The correlation between both methods was assessed using Passing–Bablok regression and Bland–Altman plot. Internal validation of our correlation formula was performed on 149 samples, along with external validation of the formula proposed by Corso et al. Results: The within- and between-day coefficient of variation was found to be lower than 10.14% and 14.09%, respectively. Passing–Bablok regression indicated a precision of 0.803 and an accuracy of 0.96. Internal validation precision was measured at 0.716. The resulting positive and negative predicted values were 0.77 and 0.92, respectively, vs. 0.46 and 0.96 from the external formula. Conclusions: Total cholesterol analysis in dried blood spots demonstrates high precision and reproducibility. This method reliably enables the incorporation of this biological marker into neonatal screening for familial hypercholesterolemia detection. [ABSTRACT FROM AUTHOR]

Objectives: This systematic review aimed to analyse the published evidence for the use of non-invasive methods for the early detection of oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMDs). Methods: The literature was systematically searched through several databases: PubMed, Cochrane Library, and Web of Science. Additional exploration was performed through cross-checks on the bibliographies of selected reviews. The inclusion criteria involved studies assessing the application of non-invasive tests on humans in the screening, diagnosis, or surveillance of OSCC or OPMDs and reporting sensitivity (SE) and specificity (SP). The Newcastle–Ottawa scale (NOS) was applied to assess the quality of the studies included. Results: The search strategy resulted in 8012 preliminary records. After a duplicate check, 116 titles remained. After abstract analysis, 70 papers remained. After full text analysis, only 54 of the 70 papers fit the inclusion criteria (28 were original articles and 26 were reviews). Those 26 reviews were used to manually search for further original articles. From this last search, 33 original articles were found. Thus, a total of 61 original studies were included and investigated. Findings from this systematic review indicate useful information, such as a description of the mechanisms, ease of use, limitations, and SE and SP values, to drive the choice of the optimal minimally invasive method to be utilized as an adjunctive tool to examine the suspicious lesions. Conclusions: Each of the analysed tools can be improved or implemented, considering their high SE and low SP. Despite advancements, incisional biopsy continues to be the gold standard for the definitive diagnosis of oral cancer and precancerous lesions. Further research and development are essential to improving the sensitivity, specificity, and reliability of non-invasive tools for widespread clinical application. [ABSTRACT FROM AUTHOR]

Diagnostic biomarkers are key components of diagnostics. In this paper, we consider diagnostic biomarkers taking continuous values that are associated with a dichotomous disease status, called malignant or benign. The performance of such a biomarker is evaluated by the area under the curve (AUC) of its receiver operating characteristic curve. We assume that, together with the disease status, one control and multiple experimental biomarkers are collected from each subject to test if any of the experimental biomarkers have a larger AUC than the control. In this case, each experimental biomarker will be compared with the control so that a multiple testing issue is involved in the comparisons. In this paper, we propose a simple non-parametric statistical testing procedure to compare K (≥ 2) experimental biomarkers with a control, adjusting for the multiplicity and its sample size calculation method. Our sample size formula requires the specification of the AUC values (or the standardized effect size of each biomarker between the benign and malignant groups) together with the correlation coefficients between the biomarkers, the prevalence of the malignant group in the study population, the type I error rate, and the power. Through simulations, we show that the statistical test controls the overall type I error rate accurately and the proposed sample size closely maintains the specified statistical power. [ABSTRACT FROM AUTHOR]

The rapid development of deep learning in medical imaging has significantly enhanced the capabilities of artificial intelligence while simultaneously introducing challenges, including the need for vast amounts of training data and the labor-intensive tasks of labeling and segmentation. Generative adversarial networks (GANs) have emerged as a solution, offering synthetic image generation for data augmentation and streamlining medical image processing tasks through models such as cGAN, CycleGAN, and StyleGAN. These innovations not only improve the efficiency of image augmentation, reconstruction, and segmentation, but also pave the way for unsupervised anomaly detection, markedly reducing the reliance on labeled datasets. Our investigation into GANs in medical imaging addresses their varied architectures, the considerations for selecting appropriate GAN models, and the nuances of model training and performance evaluation. This paper aims to provide radiologists who are new to GAN technology with a thorough understanding, guiding them through the practical application and evaluation of GANs in brain imaging with two illustrative examples using CycleGAN and pixel2style2pixel (pSp)-combined StyleGAN. It offers a comprehensive exploration of the transformative potential of GANs in medical imaging research. Ultimately, this paper strives to equip radiologists with the knowledge to effectively utilize GANs, encouraging further research and application within the field. [ABSTRACT FROM AUTHOR]

Globally, type 2 diabetes mellitus (T2DM) is a major threat to the public's health, particularly in low- and middle-income countries (LMICs). The production of short-chain fatty acids (SCFAs) by the gut microbiota has been reported to have the potential to reduce the prevalence of T2DM, particularly in LMICs where the disease is becoming more common. Dietary fibers are the primary source of SCFAs; they can be categorized as soluble (such as pectin and inulin) or insoluble (such as resistant starches). Increased consumption of processed carbohydrates, in conjunction with insufficient consumption of dietary fiber, has been identified as a significant risk factor for type 2 diabetes (T2DM). However, there are still controversies over the therapeutic advantages of SCFAs on human glucose homeostasis, due to a lack of studies in this area. Hence, a few questions need to be addressed to gain a better understanding of the beneficial link between SCFAs and glucose metabolism. These include the following: What are the biochemistry and biosynthesis of SCFAs? What role do SCFAs play in the pathology of T2DM? What is the most cost-effective strategy that can be employed by LMICs with limited laboratory resources to enhance their understanding of the beneficial function of SCFAs in patients with T2DM? To address the aforementioned questions, this paper aims to review the existing literature on the protective roles that SCFAs have in patients with T2DM. This paper further discusses possible cost-effective and accurate strategies to quantify SCFAs, which may be recommended for implementation by LMICs as preventive measures to lower the risk of T2DM. [ABSTRACT FROM AUTHOR]

Deep learning architectures like ResNet and Inception have produced accurate predictions for classifying benign and malignant tumors in the healthcare domain. This enables healthcare institutions to make data-driven decisions and potentially enable early detection of malignancy by employing computer-vision-based deep learning algorithms. These CNN algorithms, in addition to requiring huge amounts of data, can identify higher- and lower-level features that are significant while classifying tumors into benign or malignant. However, the existing literature is limited in terms of the explainability of the resultant classification, and identifying the exact features that are of importance, which is essential in the decision-making process for healthcare practitioners. Thus, the motivation of this work is to implement a custom classifier on the ovarian tumor dataset, which exhibits high classification performance and subsequently interpret the classification results qualitatively, using various Explainable AI methods, to identify which pixels or regions of interest are given highest importance by the model for classification. The dataset comprises CT scanned images of ovarian tumors taken from to the axial, saggital and coronal planes. State-of-the-art architectures, including a modified ResNet50 derived from the standard pre-trained ResNet50, are implemented in the paper. When compared to the existing state-of-the-art techniques, the proposed modified ResNet50 exhibited a classification accuracy of 97.5 % on the test dataset without increasing the the complexity of the architecture. The results then were carried for interpretation using several explainable AI techniques. The results show that the shape and localized nature of the tumors play important roles for qualitatively determining the ability of the tumor to metastasize and thereafter to be classified as benign or malignant. [ABSTRACT FROM AUTHOR]

Patient compliance in chronic illnesses is essential for disease management. This also applies to age-related macular degeneration (AMD), a chronic acquired retinal degeneration that needs constant monitoring and patient cooperation. Therefore, patients with AMD can benefit by being properly informed about their disease, regardless of the condition's stage. Information is essential in keeping them compliant with lifestyle changes, regular monitoring, and treatment. Large language models have shown potential in numerous fields, including medicine, with remarkable use cases. In this paper, we wanted to assess the capacity of two large language models (LLMs), ChatGPT4 and PaLM2, to offer advice to questions frequently asked by patients with AMD. After searching on AMD-patient-dedicated websites for frequently asked questions, we curated and selected a number of 143 questions. The questions were then transformed into scenarios that were answered by ChatGPT4, PaLM2, and three ophthalmologists. Afterwards, the answers provided by the two LLMs to a set of 133 questions were evaluated by two ophthalmologists, who graded each answer on a five-point Likert scale. The models were evaluated based on six qualitative criteria: ( C 1 ) reflects clinical and scientific consensus, ( C 2 ) likelihood of possible harm, ( C 3 ) evidence of correct reasoning, ( C 4 ) evidence of correct comprehension, ( C 5 ) evidence of correct retrieval, and ( C 6 ) missing content. Out of 133 questions, ChatGPT4 received a score of five from both reviewers to 118 questions (88.72%) for C 1 , to 130 (97.74%) for C 2 , to 131 (98.50%) for C 3 , to 133 (100%) for C 4 , to 132 (99.25%) for C 5 , and to 122 (91.73%) for C 6 , while PaLM2 to 81 questions (60.90%) for C 1 , to 114 (85.71%) for C 2 , to 115 (86.47%) for C 3 , to 124 (93.23%) for C 4 , to 113 (84.97%) for C 5 , and to 93 (69.92%) for C 6 . Despite the overall high performance, there were answers that are incomplete or inaccurate, and the paper explores the type of errors produced by these LLMs. Our study reveals that ChatGPT4 and PaLM2 are valuable instruments for patient information and education; however, since there are still some limitations to these models, for proper information, they should be used in addition to the advice provided by the physicians. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has been applied to predict the efficacy of biologic agents in ulcerative colitis (UC). ML can offer precision, personalization, efficiency, and automation. Moreover, it can improve decision support in predicting clinical outcomes. However, it faces challenges related to data quality and quantity, overfitting, generalization, and interpretability. This paper comments on two recent ML models that predict the efficacy of vedolizumab and ustekinumab in UC. Models that consider multiple pathways, multiple ethnicities, and combinations of real-world and clinical trial data are required for optimal shared decision-making and precision medicine. This paper also highlights the potential of combining ML with computational models to enhance clinical outcomes and personalized healthcare. Key Insights: (1) ML offers precision, personalization, efficiency, and decision support for predicting the efficacy of biologic agents in UC. (2) Challenging aspects in ML prediction include data quality, overfitting, and interpretability. (3) Multiple pathways, multiple ethnicities, and combinations of real-world and clinical trial data should be considered in predictive models for optimal decision-making. (4) Combining ML with computational models may improve clinical outcomes and personalized healthcare. [ABSTRACT FROM AUTHOR]

This paper introduces a novel one-dimensional convolutional neural network that utilizes clinical data to accurately detect choledocholithiasis, where gallstones obstruct the common bile duct. Swift and precise detection of this condition is critical to preventing severe complications, such as biliary colic, jaundice, and pancreatitis. This cutting-edge model was rigorously compared with other machine learning methods commonly used in similar problems, such as logistic regression, linear discriminant analysis, and a state-of-the-art random forest, using a dataset derived from endoscopic retrograde cholangiopancreatography scans performed at Olive View–University of California, Los Angeles Medical Center. The one-dimensional convolutional neural network model demonstrated exceptional performance, achieving 90.77% accuracy and 92.86% specificity, with an area under the curve of 0.9270. While the paper acknowledges potential areas for improvement, it emphasizes the effectiveness of the one-dimensional convolutional neural network architecture. The results suggest that this one-dimensional convolutional neural network approach could serve as a plausible alternative to endoscopic retrograde cholangiopancreatography, considering its disadvantages, such as the need for specialized equipment and skilled personnel and the risk of postoperative complications. The potential of the one-dimensional convolutional neural network model to significantly advance the clinical diagnosis of this gallstone-related condition is notable, offering a less invasive, potentially safer, and more accessible alternative. [ABSTRACT FROM AUTHOR]

Mesenteric ischemia diagnosis is challenging, with an overall mortality of up to 50% of cases despite advances in treatment. The main problem that affects the outcome is delayed diagnosis because of non-specific clinical presentation. Multi-Detector CT Angiography (MDCTA) is the first-line investigation for the suspected diagnosis of vascular abdominal pathologies and the diagnostic test of choice in suspected mesenteric bowel ischemia. MDCTA can accurately detect the presence of arterial and venous thrombosis, determine the extent and the gastrointestinal tract involved, and provide detailed information determining the subtype and the stage progression of the diseases, helping clinicians and surgeons with appropriate management. CT (Computed Tomography) can differentiate forms that are still susceptible to pharmacological or interventional treatment (NOM = non-operative management) from advanced disease with transmural necrosis in which a surgical approach is required. Knowledge of CT imaging patterns and corresponding vascular pathways is mandatory in emergency settings to reach a prompt and accurate diagnosis. The aims of this paper are 1. to provide technical information about the optimal CTA (CT Angiography) protocol; 2. to explain the CTA arterial and venous supply to the gastrointestinal tract and the relevant ischemic pattern; and 3. to describe vascular, bowel, and extraintestinal CT findings for the diagnosis of acute mesenteric ischemia. [ABSTRACT FROM AUTHOR]

Neck infections are often prone to being underestimated and can manifest insidiously. The spread of infection can lead to translocation into thoracic areas, causing descending necrotizing mediastinitis (DNM). However, the application of the post-mortem approach in such cases is not well-described in the literature. A literature review was carried out according to the PRISMA methods. Nine papers were included in the final review, revealing different levels of involvement of neck layers that can be linked to different causes. Expertise with respect to the anatomy of the fasciae and spaces of the neck enables an understanding of the pathogenesis of DNM. However, a clear autoptic description was not provided in any of the articles. Therefore, we also employed a practical post-mortem approach to cases of death due to DNM. It is fundamental for pathologists to identify the exact head and neck structures involved. Providing dissectors with support from an otolaryngologist could be useful. This paper could help address such difficult cases. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. Results: We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. Conclusions: The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology. [ABSTRACT FROM AUTHOR]

The paper presents a system for analyzing cardiac activity with the possibility of continuous and remote monitoring. The created sensor mobile device monitors heart activity by means of the convenient and imperceptible registration of cardiac signals. At the same time, the behavior of the human body is also monitored through the accelerometer and gyroscope built into the device, thanks to which it is possible to signal in the event of loss of consciousness or fall (in patients with syncope). Conducting real-time cardio monitoring and the analysis of recordings using various mathematical methods (linear, non-linear, and graphical) enables the research, accurate diagnosis, timely assistance, and correct treatment of cardiovascular diseases. The paper examines the recordings of patients diagnosed with arrhythmia and syncope recorded by electrocardiography (ECG) sensors in real conditions. The obtained results are subjected to statistical analysis to determine the accuracy and significance of the obtained results. The studies show significant deviations in the patients with arrhythmia and syncope regarding the obtained values of the studied parameters of heart rate variability (HRV) from the accepted normal values (for example, the root mean square of successive differences between normal heartbeats (RMSSD) in healthy individuals is 24.02 ms, while, in patients with arrhythmia (6.09 ms) and syncope (5.21 ms), it is much lower). The obtained quantitative and graphic results identify some possible abnormalities and demonstrate disorders regarding the activity of the autonomic nervous system, which is directly related to the work of the heart. [ABSTRACT FROM AUTHOR]

Background: In planning radiotherapy treatments, computed tomography (CT) has become a crucial tool. CT scans involve exposure to ionizing radiation, which can increase the risk of cancer and other adverse health effects in patients. Ionizing radiation doses for medical exposure must be kept "As Low As Reasonably Achievable". Very few articles on guidelines for radiotherapy-computed tomography scans are available. This paper reviews the current literature on radiation dose optimization based on the effective dose and diagnostic reference level (DRL) for head, neck, and pelvic CT procedures used in radiation therapy planning. This paper explores the strategies used to optimize radiation doses, and high-quality images for diagnosis and treatment planning. Methods: A cross-sectional study was conducted on 300 patients with head, neck, and pelvic region cancer in our institution. The DRL, effective dose, volumetric CT dose index (CTDIvol), and dose-length product (DLP) for the present and optimized protocol were calculated. DRLs were proposed for the DLP using the 75th percentile of the distribution. The DLP is a measure of the radiation dose received by a patient during a CT scan and is calculated by multiplying the CT dose index (CTDI) by the scan length. To calculate a DRL from a DLP, a large dataset of DLP values obtained from a specific imaging procedure must be collected and can be used to determine the median or 75th-percentile DLP value for each imaging procedure. Results: Significant variations were found in the DLP, CTDIvol, and effective dose when we compared both the standard protocol and the optimized protocol. Also, the optimized protocol was compared with other diagnostic and radiotherapy CT scan studies conducted by other centers. As a result, we found that our institution's DRL was significantly low. The optimized dose protocol showed a reduction in the CTDIvol (70% and 63%), DLP (60% and 61%), and effective dose (67% and 62%) for both head, neck, and pelvic scans. Conclusions: Optimized protocol DRLs were proposed for comparison purposes. [ABSTRACT FROM AUTHOR]

(1) Background: In digital-technology-assisted nasal defect reconstruction methods, a crucial step involves utilizing computer-aided design to virtually reconstruct the nasal defect's complete morphology. However, current digital methods for virtual nasal defect reconstruction have yet to achieve efficient, precise, and personalized outcomes. In this research paper, we propose a novel approach for reconstructing external nasal defects based on the Facial Mesh Generation Network (FMGen-Net), aiming to enhance the levels of automation and personalization in virtual reconstruction. (2) Methods: We collected data from 400 3D scans of faces with normal morphology and combined the structured 3D face template and the Meshmonk non-rigid registration algorithm to construct a structured 3D facial dataset for training FMGen-Net. Guided by defective facial data, the trained FMGen-Net automatically generated an intact 3D face that was similar to the defective face, and maintained a consistent spatial position. This intact 3D face served as the 3D target reference face (3D-TRF) for nasal defect reconstruction. The reconstructed nasal data were extracted from the 3D-TRF based on the defective area using reverse engineering software. The '3D surface deviation' between the reconstructed nose and the original nose was calculated to evaluate the effect of 3D morphological restoration of the nasal defects. (3) Results: In the simulation experiment of 20 cases involving full nasal defect reconstruction, the '3D surface deviation' between the reconstructed nasal data and the original nasal data was 1.45 ± 0.24 mm. The reconstructed nasal data, constructed from the personalized 3D-TRF, accurately reconstructed the anatomical morphology of nasal defects. (4) Conclusions: This paper proposes a novel method for the virtual reconstruction of external nasal defects based on the FMGen-Net model, achieving the automated and personalized construction of the 3D-TRF and preliminarily demonstrating promising clinical application potential. [ABSTRACT FROM AUTHOR]

This research paper outlines a method for automatically classifying wakefulness and deep sleep stage (N3) based on the American Academy of Sleep Medicine (AASM) standards. The study employed a single-channel EEG signal, leveraging the Wigner–Ville Distribution (WVD) for time–frequency analysis to determine EEG energy per second in specific frequency bands (δ , θ , α , and entire band). Particle Swarm Optimization (PSO) was used to optimize thresholds for distinguishing between wakefulness and stage N3. This process aims to mimic a sleep technician's visual scoring but in an automated fashion, with features and thresholds extracted to classify epochs into correct sleep stages. The study's methodology was validated using overnight PSG recordings from 20 subjects, which were evaluated by a technician. The PSG setup followed the 10–20 standard system with varying sampling rates from different hospitals. Two baselines, T1 for the wake stage and T2 for the N3 stage, were calculated using PSO to ascertain the best thresholds, which were then used to classify EEG epochs. The results showed high sensitivity, accuracy, and kappa coefficient, indicating the effectiveness of the classification algorithm. They suggest that the proposed method can reliably determine sleep stages, being aligned closely with the AASM standards and offering an intuitive approach. The paper highlights the strengths of the proposed method over traditional classifiers and expresses the intentions to extend the algorithm to classify all sleep stages in the future. [ABSTRACT FROM AUTHOR]

Given the pronounced impact COVID-19 continues to have on society—infecting 700 million reported individuals and causing 6.96 million deaths—many deep learning works have recently focused on the virus's diagnosis. However, assessing severity has remained an open and challenging problem due to a lack of large datasets, the large dimensionality of images for which to find weights, and the compute limitations of modern graphics processing units (GPUs). In this paper, a new, iterative application of transfer learning is demonstrated on the understudied field of 3D CT scans for COVID-19 severity analysis. This methodology allows for enhanced performance on the MosMed Dataset, which is a small and challenging dataset containing 1130 images of patients for five levels of COVID-19 severity (Zero, Mild, Moderate, Severe, and Critical). Specifically, given the large dimensionality of the input images, we create several custom shallow convolutional neural network (CNN) architectures and iteratively refine and optimize them, paying attention to learning rates, layer types, normalization types, filter sizes, dropout values, and more. After a preliminary architecture design, the models are systematically trained on a simplified version of the dataset-building models for two-class, then three-class, then four-class, and finally five-class classification. The simplified problem structure allows the model to start learning preliminary features, which can then be further modified for more difficult classification tasks. Our final model CoSev boosts classification accuracies from below 60% at first to 81.57% with the optimizations, reaching similar performance to the state-of-the-art on the dataset, with much simpler setup procedures. In addition to COVID-19 severity diagnosis, the explored methodology can be applied to general image-based disease detection. Overall, this work highlights innovative methodologies that advance current computer vision practices for high-dimension, low-sample data as well as the practicality of data-driven machine learning and the importance of feature design for training, which can then be implemented for improvements in clinical practices. [ABSTRACT FROM AUTHOR]

Background: The increasing popularity of laser- and light-based esthetic treatments for facial rejuvenation has raised concerns regarding ocular safety. Although these procedures are generally considered safe and effective, there is a growing body of evidence highlighting the potential for ocular complications. This review aims to systematically analyze the types and mechanisms of ocular injuries associated with such treatments, as well as to evaluate preventive measures and management strategies. Methods: A comprehensive literature search was conducted using databases including MEDLINE, PubMed and Ovid for relevant studies published on clinical trials, diagnosis and treatment. Some papers were further reviewed using a double-blinding approach, varying sample sizes, control usage, randomization usage and objective endpoint measurements. All studies were classified according to the Oxford Centre for evidence-based medicine evidence hierarchy. Result: Our review identified several types of ocular complications associated with facial laser or light treatments, including but not limited to conjunctival burns, corneal damage, retinal phototoxicity, and transient vision disturbances. The incidence of these complications varies significantly depending on the type of laser or light source employed, treatment parameters, and the anatomical proximity of the eyes to the treatment area. Factors such as inadequate protective measures, patient movement during the procedure, and the operator's experience were found to contribute to the risk of ocular injury. Strategies such as the use of appropriate eye protection, careful patient positioning, and thorough pre-treatment assessments were highlighted as essential preventive measures. Conclusion: Ocular complications, though rare, represent a significant risk in facial esthetic laser and light treatments. This review underscores the importance of awareness among practitioners regarding the potential ocular hazards and the implementation of robust safety protocols. Future research is needed to establish standardized guidelines to minimize risks and enhance patient safety in esthetic dermatological practices. Continued education and improved protective strategies will be essential in safeguarding ocular health as the field of esthetic treatments continues to evolve. This comprehensive review serves as an essential resource for practitioners, informing them of ocular risks, management options, and the need for vigilance to mitigate complications in clinical practice. [ABSTRACT FROM AUTHOR]

Melanocytic nevi, commonly known as moles, are benign skin lesions that often occur in children and adolescents. Overall, they are less common in children compared to adults. Understanding the diagnosis and management of melanocytic nevi and risk factors for melanoma development is crucial for their early detection and appropriate treatment. This paper presents children's most common melanocytic nevi, including their epidemiology, morphology, diagnostic methods, and treatment. [ABSTRACT FROM AUTHOR]

Cancer surgery is aimed at complete tumor resection and accurate lymph node detection. However, numerous blood vessels are distributed within the tumor, and the colors of the tumor, blood vessels, and lymph nodes are similar, making observations with the naked eye difficult. Therefore, tumors, blood vessels, and lymph nodes can be monitored via color classification using an operating microscope to induce fluorescence emission. However, as the beam width of the LED required to induce fluorescence emission is narrow and the power loss of the beam is significant at a certain working distance, there are limitations to inducing fluorescence emission, and light reflection occurs in the observation image, obstructing the view of the observation area. Therefore, the removal of reflected light is essential to avoid missing the diagnosis of the lesion under observation. This paper proposes the use of a beam mirror and polarizing filter to increase the beam width and beam intensity. The refraction and reflection effects of the beam were utilized using the beam mirror, and the rotation angle of the polarizing filter was adjusted to remove light reflection. Consequently, the minimum beam power using the beam mirror was 10.9 mW, the beam width was doubled to 40.2°, and more than 98% of light reflection was removed at 90° and 270°. With light reflection effectively eliminated, clear observation of lesions is possible. This method is expected to be used effectively in surgical, procedural, and diagnostic departments. [ABSTRACT FROM AUTHOR]

This paper investigates the feasibility of detecting and estimating the rate of internal hemorrhage based on continuous noninvasive hematocrit measurement. A unique challenge in hematocrit-based hemorrhage detection is that hematocrit decreases in response to hemorrhage and resuscitation with fluids, which makes hemorrhage detection during resuscitation challenging. We developed two sequential inference algorithms for detection of internal hemorrhage based on the Luenberger observer and the extended Kalman filter. The sequential inference algorithms use fluid resuscitation dose and hematocrit measurement as inputs to generate signatures to enable detection of internal hemorrhage. In the case of the extended Kalman filter, the signature is nothing but inferred hemorrhage rate, which allows it to also estimate internal hemorrhage rate. We evaluated the proof-of-concept of these algorithms based on in silico evaluation in 100 virtual patients subject to diverse hemorrhage and resuscitation rates. The results showed that the sequential inference algorithms outperformed naïve internal hemorrhage detection based on the decrease in hematocrit when hematocrit noise level was 1% (average F1 score: Luenberger observer 0.80; extended Kalman filter 0.76; hematocrit 0.59). Relative to the Luenberger observer, the extended Kalman filter demonstrated comparable internal hemorrhage detection performance and superior accuracy in estimating the hemorrhage rate. The analysis of the dependence of the sequential inference algorithms on measurement noise and plant parametric uncertainty showed that small (≤1%) hematocrit noise level and personalization of sequential inference algorithms may enable continuous noninvasive detection of internal hemorrhage and estimation of its rate. [ABSTRACT FROM AUTHOR]

This paper reports results obtained using white light diffraction phase microscopy (wDPM) on captured images of breast and colon tissue samples, marking a contribution to the advancement in biomedical imaging. Unlike conventional brightfield microscopy, wDPM offers the capability to capture intricate details of biological specimens with enhanced clarity and precision. It combines high resolution, enhanced contrast, and quantitative capabilities with non-invasive, label-free imaging. These features make it a useful tool for tissue imaging, providing detailed and accurate insights into tissue structure and dynamics without compromising the integrity of the samples. Our findings underscore the potential of quantitative phase imaging in histopathology, in the context of automating the process of tissue analysis and diagnosis. Of particular note are the insights gained from the reconstructed phase images, which provide physical data regarding peripheral glandular cell membranes. These observations serve to focus attention on pathologies involving the basal membrane, such as early invasive carcinoma. Through our analysis, we aim to contribute to catalyzing further advancements in tissue (breast and colon) imaging. [ABSTRACT FROM AUTHOR]

Immune checkpoint inhibitors (ICIs) activate T cells, causing immune-related adverse events (irAEs). Skin manifestations are common among irAEs, but ICI-associated bullous pemphigoid (BP) is rare. Inhibiting programmed death (PD)-1 signaling, in addition to causing epitope spreading, may disrupt B and T cell balance, causing excessive autoantibody production against the skin's basement membrane, leading to BP. A 70-year-old woman developed late-onset multi-organ irAEs, including diarrhea, thyroid dysfunction, and BP, while receiving pembrolizumab, a PD-1 inhibitor. This highlights the long-term risk of irAEs, which can occur 2–3 years after starting ICIs. In cases of multi-organ irAE, C-reactive protein levels and neutrophil/lymphocyte ratio are often low. These characteristics were observed in our case. Few papers address multiple organ involvement, highlighting the need to consider irAEs in a multi-organ context. While it is known that drug-induced skin reactions worsen as blood eosinophil counts increase, in our case, the eosinophil count remained normal, suggesting that ICI-associated BP might have been controlled without discontinuing the ICI and through tapering of low-dose oral prednisone treatment. Additionally, in this case, significant CD4-positive T cell infiltration was observed in the immunostaining examination of the blisters, indicating that severe CD4-positive T cell infiltration induced by the ICI might have led to multi-organ involvement, including severe diarrhea. Few reports focus on blood eosinophil counts in BP cases or discuss CD4 and CD8 immunostaining in BP cases. Therefore, future research should explore the relationship between blood eosinophil counts, immunostaining results, and the prognosis of irAEs, including BP, in treatment courses. [ABSTRACT FROM AUTHOR]

Salivary gland tumors are a rare and heterogeneous group of neoplasms of the head and neck region. The mixed category of these tumors include the following entities: pleomorphic adenoma (PA), carcinoma ex pleomorphic adenoma (CEPA), salivary carcinosarcoma (CS), and metastasizing PA (MPA). The most common benign tumor of the salivary glands is PA. Metastasis and malignant degeneration have been reported in cases of PA of a salivary gland origin. Judging by their behavior, MPA, CEPA, and CS can be considered aggressive tumors. Invasive CEPA has been identified in the parotid gland more frequently. MPA and CS cases reported in the current literature are rare. In this paper, we present, narratively, the clinico-morphological features of this group of mixed tumors. [ABSTRACT FROM AUTHOR]

The aim of this paper is to demonstrate the difference in usefulness of the coronary artery calcium score (CACS) and the full assessment of the severity of coronary artery disease in coronary computed tomography angiography (CCTA) studies. The difference between the population risk of coronary artery disease (CAD) assessed by the CACS and the severity of CAD was demonstrated in images from two CCTA studies. The first image is from a patient with a CACS of 0 and significant coronary artery stenosis. In the native phase of CCTA examination, no calcified changes were detected in the topography of the coronary arteries. In the middle section of the left descending artery (LAD), at the level of the second diagonal branch (Dg2), a large non-calcified atherosclerotic plaque was visible. Mid-LAD stenosis was estimated to be approximately 70%. The second image features a patient with a high CACS but no significant coronary artery stenosis. The calcium score of individual coronary arteries calculated using the Agatston method was as follows: left main (LM) 0, LAD 403, left circumflex (LCx) 207.7, right coronary artery (RCA) 12. CACS was 622.7, representing a significant population risk of significant CAD. In the proximal and middle sections of the LAD, numerous calcified and mixed atherosclerotic plaques with positive remodeling were visible, causing stenosis of 25–50%. Similarly, in the proximal and middle sections of the LCx, numerous calcified and mixed atherosclerotic plaques with positive remodeling were visualized, causing stenoses of 25–50%. Calcified atherosclerotic plaques were found in the RCA, causing stenosis <25%. The entire CCTA image met CAD-RADS 2 (coronary artery disease reporting and data system) criteria. In summary, CACS may be applicable in population-based studies to assess the risk of significant CAD. In the evaluation of individual patients, a comprehensive assessment of CAD severity based on the angiographic phase of the CCTA examination should be used. [ABSTRACT FROM AUTHOR]

Background: Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is an important extra-articular manifestation of rheumatoid arthritis (RA). Identifying patients at risk of progression and death is crucial for improving RA-ILD management and outcomes. This paper explores current evidence on prognostic factors in RA-ILD. Methods: We conducted a systematic literature review to examine the impact of clinical, radiological, and histological factors on lung function decline and the survival of RA-ILD patients. We searched electronic databases, including Medline and EMBASE, from inception to date. The incidence and prognosis of predictors were qualitatively analyzed, and univariate results were combined when feasible. Following the "Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)" guidelines, our systematic literature review involved a five-step algorithm. Out of 2217 records, 48 studies were eligible. These studies reported various prognostic factors, including demographic variables, clinical risk factors, serum markers, and preexisting treatments. Results: Lung function declined over time in 1225 subjects, with significant variability in smoking history and radiological/pathological UIP patterns. Severe lung fibrosis and abnormal pulmonary function tests (PFTs) were key univariate prognostic indicators, while age at initial presentation, RA disease activity, predicted DLCO percentage, and UIP pattern were the most reliable multivariate risk factors for ILD progression. Age, male gender, disease duration, RA activity, acute phase reactants, and specific serum biomarkers (Krebs vin den Lungen 6, surfactant protein D, and interleukin 6) were significantly associated with all-cause mortality. Conclusions: RA-ILD is a severe complication of RA characterized by significant prognostic variability. Key prognostic factors include extensive fibrosis observed on imaging, a marked decline in lung function, high RA disease activity, and specific biomarkers. These factors can guide treatment strategies and improve patient outcomes. [ABSTRACT FROM AUTHOR]

Brain cancer is a substantial factor in the mortality associated with cancer, presenting difficulties in the timely identification of the disease. The precision of diagnoses is significantly dependent on the proficiency of radiologists and neurologists. Although there is potential for early detection with computer-aided diagnosis (CAD) algorithms, the majority of current research is hindered by its modest sample sizes. This meta-analysis aims to comprehensively assess the diagnostic test accuracy (DTA) of computer-aided design (CAD) models specifically designed for the detection of brain cancer utilizing hyperspectral (HSI) technology. We employ Quadas-2 criteria to choose seven papers and classify the proposed methodologies according to the artificial intelligence method, cancer type, and publication year. In order to evaluate heterogeneity and diagnostic performance, we utilize Deeks' funnel plot, the forest plot, and accuracy charts. The results of our research suggest that there is no notable variation among the investigations. The CAD techniques that have been examined exhibit a notable level of precision in the automated detection of brain cancer. However, the absence of external validation hinders their potential implementation in real-time clinical settings. This highlights the necessity for additional studies in order to authenticate the CAD models for wider clinical applicability. [ABSTRACT FROM AUTHOR]

Selective RET inhibitors, such as selpercatinib and pralsetinib, have revolutionized the treatment of cancers with RET gene alterations. These inhibitors have shown remarkable clinical efficacy, particularly in RET-driven lung cancer, medullary thyroid cancer, and other solid tumors driven by RET gene fusions. The assessment of treatment response in oncology has been greatly enhanced by Fluorodeoxyglucose Positron Emission Tomography (FDG-PET), a valuable tool that measures tumor metabolism and provides early indicators of treatment effectiveness. This work explores the effectiveness of selective RET inhibitors in targeting RET-positive cancers and investigates the utility of FDG-PET in assessing treatment response. The paper includes insightful case studies that highlight the successful application of RET inhibitors in the treatment of RET-positive cancers. The findings suggest that FDG-PET has the potential to serve as a non-invasive biomarker for monitoring treatment response in patients with RET-positive cancers. However, further research is required to establish standardized criteria for interpreting FDG-PET scans in the context of selective RET inhibitors and to uncover the broader applications of FDG-PET in precision oncology. [ABSTRACT FROM AUTHOR]

Aortic arch anomalies encompass a diverse spectrum of conditions. Elucidating the prevalence of these anomalies, their impact on patient wellbeing, and the most effective diagnostic tools are crucial steps in ensuring optimal patient care. This paper aims to explore the various presentations of aortic arch anomalies, emphasizing the remarkable utility of computed tomography (CT) angiography in their definitive diagnosis and characterization. We conducted a retrospective study on patients who were submitted to the CT angiography of the thoracic aorta or supra-aortic trunks, or the contrast-enhanced CT scans of the thorax and/or cervical region between January 2021 and February 2024 in our Hospital. Out of the total of 2350 patients, 18 were diagnosed with aortic arch anomalies, with an average age of approximately 55 years. The aortic arch anomalies identified in the study were as follows: left aortic arch with the aberrant origin of the right subclavian artery, right aortic arch (types I and II), double aortic arch, aortic coarctation, aortic pseudocoarctation, and ductus diverticulum. Although often asymptomatic, aortic arch anomalies require recognition and CT using advanced post-processing techniques is the optimal diagnostic method with the ability to also identify other associated cardiac or vascular malformations. [ABSTRACT FROM AUTHOR]

Craniosynostoses (CRS) are caused by the premature fusion of one or more cranial sutures, with isolated nonsyndromic CRS accounting for most of the clinical manifestations. Such premature suture fusion impacts both skull and brain morphology and involves regions far beyond the immediate area of fusion. The combined use of different neuroimaging tools allows for an accurate depiction of the most prominent clinical–radiological features in nonsyndromic CRS but can also contribute to a deeper investigation of more subtle alterations in the underlying nervous tissue organization that may impact normal brain development. This review paper aims to provide a comprehensive framework for a better understanding of the present and future potential applications of neuroimaging techniques for evaluating nonsyndromic CRS, highlighting strategies for optimizing their use in clinical practice and offering an overview of the most relevant technological advancements in terms of diagnostic performance, radiation exposure, and cost-effectiveness. [ABSTRACT FROM AUTHOR]

Nail capillaroscopic examination is an inexpensive and easily applicable method to identify capillary morphological changes in patients with conditions such as systemic sclerosis and Raynaud's. The detection of changes in capillaries makes an important contribution to diagnosing these diseases. Capillary morphology is important in the symptoms of these diseases, and capillary diameter, visibility, distribution, length, microbleeds, blood flow, and density are important indicators in capillaroscopic evaluation. Manual examination to determine these parameters is subjective, causes inconsistent results, and is labor-intensive and time-consuming. To overcome these problems, a YOLOv8s-based system was proposed in this paper to detect the number, thickness, and density of capillaries in the nail bed. The system's components include database systems that store the analysis results, artificial intelligence-based software that runs on the SBC (Single-Board Computer), and recorded microscope images. mAP and F1_score parameters were used to evaluate the system's performance, and values of 0.882 and 0.83 were obtained. The proposed system is promising in improving the diagnosis process of diseases such as systemic sclerosis and Raynaud's by providing objective measurements and the early diagnosis and monitoring of diseases. [ABSTRACT FROM AUTHOR]

Background: Line-field confocal optical coherence tomography is a novel technology able to reproduce a "virtual biopsy" of the skin. The aim of this review is to explore the application of line-field confocal optical coherence tomography (LC-OCT) in various skin diseases, covering skin cancers, inflammatory and infectious skin diseases, genetic diseases, cosmetic procedures, and less common disorders. Methods: Study selection was conducted based on LC-OCT and using pertinent MeSh terms, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines from inception to March 2024; to evaluate the quality and risk of bias of studies, Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used. Results: the search retrieved 154 papers according to the selection criteria; after removing publications by one or more of the exclusion criteria, a total of 96 studies were found to be suitable for the analysis. Conclusions: Increasing evidence supports the use of LC-OCT as an adjunctive diagnostic tool for the in vivo diagnosis of a variety of skin tumors. As this device can be considered a "bridge" between dermoscopy and histopathology, widening applications in numerous fields of clinical dermatology, including inflammatory skin disease treatment, presurgical mapping, cosmetic procedures, and monitoring of non-invasive therapies, have been explored. [ABSTRACT FROM AUTHOR]

Malunion of thumb and finger fractures causes problems in the cosmetic and functional aspects of the hand. Malunion of phalangeal fractures usually manifests as a combination of rotational deformities in the coronal, sagittal, and transverse planes, and corrective osteotomy is performed on the planes that cause these problems. Quantification of the deformity is essential for precise osteotomy and is difficult to perform in the transverse plane, even with radiography or computed tomography. Thus, we developed a technique called linkage simulation for the quantitative measurement of rotational deformities for surgical planning. In this procedure, finger extension and flexion can be simulated based on the predicted rotational axis of the joint, which is useful for determining the appropriate correction. Furthermore, by performing a reduction simulation in the software, it is possible to simulate the surgery and predict the postoperative results. This paper reports the details of this technique. [ABSTRACT FROM AUTHOR]

Given the diversity of medical images, traditional image segmentation models face the issue of domain shift. Unsupervised domain adaptation (UDA) methods have emerged as a pivotal strategy for cross modality analysis. These methods typically utilize generative adversarial networks (GANs) for both image-level and feature-level domain adaptation through the transformation and reconstruction of images, assuming the features between domains are well-aligned. However, this assumption falters with significant gaps between different medical image modalities, such as MRI and CT. These gaps hinder the effective training of segmentation networks with cross-modality images and can lead to misleading training guidance and instability. To address these challenges, this paper introduces a novel approach comprising a cross-modality feature alignment sub-network and a cross pseudo supervised dual-stream segmentation sub-network. These components work together to bridge domain discrepancies more effectively and ensure a stable training environment. The feature alignment sub-network is designed for the bidirectional alignment of features between the source and target domains, incorporating a self-attention module to aid in learning structurally consistent and relevant information. The segmentation sub-network leverages an enhanced cross-pseudo-supervised loss to harmonize the output of the two segmentation networks, assessing pseudo-distances between domains to improve the pseudo-label quality and thus enhancing the overall learning efficiency of the framework. This method's success is demonstrated by notable advancements in segmentation precision across target domains for abdomen and brain tasks. [ABSTRACT FROM AUTHOR]

Mucopolysaccharidoses (MPS) comprise a group of 12 metabolic disorders where defects in specific enzyme activities lead to the accumulation of glycosaminoglycans (GAGs) within lysosomes. This classification expands to 13 when considering MPS IIIE. This type of MPS, associated with pathogenic variants in the ARSG gene, has thus far been described only in the context of animal models. However, pathogenic variants in this gene also occur in humans, but are linked to a different disorder, Usher syndrome (USH) type IV, which is sparking increasing debate. This paper gathers, discusses, and summarizes arguments both for and against classifying dysfunctions of arylsulfatase G (due to pathogenic variants in the ARSG gene) in humans as another subtype of MPS, called MPS IIIE. Specific difficulties in diagnostics and the classification of some inherited metabolic diseases are also highlighted and discussed. [ABSTRACT FROM AUTHOR]

Filler injections in the upper face pose significant challenges due to its complex anatomy and proximity to vascular structures. High-frequency Doppler ultrasound offers real-time visualization of facial anatomy, improving both safety and aesthetic outcomes. This paper presents a detailed overview of the ultrasonographic anatomy of the temples, forehead, and glabella, along with reproducible, ultrasound-guided filler injection techniques for these areas. We use two scanning techniques previously described: "scan before injecting" and "scan while injecting", applicable to subdermal, interfascial, and supraperiosteal planes in the temporal region, as well as the glabella, forehead, and supraorbital region. Ultrasound guidance for filler injections in the upper face can enhance procedural efficacy and safety. By integrating real-time imaging, practitioners can navigate the intricate vascular anatomy more effectively, thereby minimizing the risk of complications. This study highlights the need for ongoing research and continuous education to further refine these techniques and improve patient outcomes. [ABSTRACT FROM AUTHOR]

Background: The aim of this narrative review was to provide insights into the influence of the morphological characteristics of the anatomical structures of the upper jaw based on cone beam computed tomography (CBCT) analysis on the immediate implant placement in this region. Material and Methods:To conduct this research, we used many electronic databases, and the resulting papers were chosen and analyzed. From the clinical point of view, the region of the anterior maxilla is specific and can be difficult for immediate implant placement. Findings: Anatomical structures in the anterior maxilla, such as the nasopalatine canal and accessory canals, may limit and influence the implant therapy outcome. In addition to the aforementioned region, immediate implant placement in the posterior maxilla may be challenging for clinicians, especially in prosthetic-driven immediate implant placement procedures. Data presented within the recently published materials summarize the investigations performed in order to achieve more reliable indicators that may make more accurate decisions for clinicians. Conclusion: The possibility for immediate implant placement may be affected by the NPC shape in the anterior maxilla, while the presence of ACs may increase the incidence of immediate implant placement complications. The variations in IRS characteristics may be considered important criteria for choosing the implant properties required for successful immediate implant placement. [ABSTRACT FROM AUTHOR]

Despite the increased use of diagnostic imaging in Africa, the completion of the Radiology Request Form (RRF) remains suboptimal, often relying on paper-based communication. To examine the practices surrounding RRF completion in the African continent, on 25 March 2024, we conducted a systematic review of peer-reviewed articles describing the practice in African settings. Non-African studies, studies involving non-human subjects, and articles examining the practice of the RRF for interventional usage were excluded. Our search involves PubMed/MEDLINE, ScienceDirect, Scopus, Web of Science, Google Scholar, and African Journals Online. The included studies were 3004, of which 30 met the inclusion criteria. These studies span eight countries and highlighted several shortcomings, including the usage of informal forms, unconventional abbreviations, illegibility, inaccuracy, and the lack of important fields from institutional forms, commonly the last menstrual period and the referrer's contact details. We also found widespread non-compliance in all RRF fields; half of the studies did not have an adequately filled form. Incomplete RRFs lead to delayed imaging, increased workloads for radiographers and radiologists, and potential misdiagnoses due to insufficient information. It will also impede the application of radiation protection principles. To address these challenges, empowering radiographers and radiologists and encouraging best practices is essential. Regular audits and educational initiatives aimed at clinicians are recommended. While transitioning to a paperless communication system might help, implementing nationwide quality improvement projects to standardise radiology request forms is currently more feasible. [ABSTRACT FROM AUTHOR]

Simple Summary: Twenty ancient crania dated to the V–VI centuries from the necropolis of Opi, Abruzzi, Italy, were analysed. A 3D analysis of the maxillary sinuses of these ancient crania was performed. The aims of this paper were two: to compare the linear and volumetric measurements of the left and right maxillary sinuses and to determine the facial measurements in the same sample (the upper facial height, the bizygomatic width, and the palatal length). Cone beam computed tomography (CBCT) can provide precise information about complex anatomical structures as it is characterised by rapid volumetric image acquisition with high resolution. The aim of this study was to provide measurements for 20 ancient skulls of the Samnite people found in the necropolis of Opi, a small and isolated mountain village in Abruzzo, a region in central Italy. All the images (left and right) of the 20 ancient skulls from Opi were acquired. All the data are the property of the Department of Innovative Technologies in Medicine and Dentistry of G. D'Annunzio University, Chieti, and different parameters (area and volume) were measured and evaluated. The mean and standard deviation of the facial measurements were also calculated. All the data were subjected to statistical analysis. CBCT scan data of 20 fossil skulls did not show significant values regarding the MS area and the volume between the right and left sides. In the ancient skulls, no difference was found between the right and left sides. [ABSTRACT FROM AUTHOR]

Skin cancer is the predominant form of cancer worldwide, including 75% of all cancer cases. This study aims to evaluate the effectiveness of the spectrum-aided visual enhancer (SAVE) in detecting skin cancer. This paper presents the development of a novel algorithm for snapshot hyperspectral conversion, capable of converting RGB images into hyperspectral images (HSI). The integration of band selection with HSI has facilitated the identification of a set of narrow band images (NBI) from the RGB images. This study utilizes various iterations of the You Only Look Once (YOLO) machine learning (ML) framework to assess the precision, recall, and mean average precision in the detection of skin cancer. YOLO is commonly preferred in medical diagnostics due to its real-time processing speed and accuracy, which are essential for delivering effective and efficient patient care. The precision, recall, and mean average precision (mAP) of the SAVE images show a notable enhancement in comparison to the RGB images. This work has the potential to greatly enhance the efficiency of skin cancer detection, as well as improve early detection rates and diagnostic accuracy. Consequently, it may lead to a reduction in both morbidity and mortality rates. [ABSTRACT FROM AUTHOR]

The use of 3D-printed models in simulation-based training and planning for vascular surgery is gaining interest. This study aims to provide an overview of the current applications of 3D-printing technologies in vascular surgery. We performed a systematic review by searching four databases: PubMed, Web of Science, Scopus, and Cochrane Library (last search: 1 March 2024). We included studies considering the treatment of vascular stenotic/occlusive or aneurysmal diseases. We included papers that reported the outcome of applications of 3D-printed models, excluding case reports or very limited case series (≤5 printed models or tests/simulations). Finally, 22 studies were included and analyzed. Computed tomography angiography (CTA) was the primary diagnostic method used to obtain the images serving as the basis for generating the 3D-printed models. Processing the CTA data involved the use of medical imaging software; 3DSlicer (Brigham and Women's Hospital, Harvard University, Boston, MA), ITK-Snap, and Mimics (Materialise NV, Leuven, Belgium) were the most frequently used. Autodesk Meshmixer (San Francisco, CA, USA) and 3-matic (Materialise NV, Leuven, Belgium) were the most frequently employed mesh-editing software during the post-processing phase. PolyJet™, fused deposition modeling (FDM), and stereolithography (SLA) were the most frequently employed 3D-printing technologies. Planning and training with 3D-printed models seem to enhance physicians' confidence and performance levels by up to 40% and lead to a reduction in the procedure time and contrast volume usage to varying extents. [ABSTRACT FROM AUTHOR]

Psoriatic arthritis (PsA) is a chronic inflammatory condition affecting about one-third of individuals with psoriasis. Defining axial involvement in PsA (axPsA) remains debated. While rheumatologists guide clinical practice, consensus on axPsA is still lacking. This paper explores historical and upcoming definitions from the Axial Involvement in Psoriatic Arthritis (AXIS) study, which aims to establish a validated axPsA definition. Epidemiological data reveal diverse axPsA prevalence rates, emphasizing its complex relationship with peripheral arthritis and enthesitis. Unique genetic, clinical, and radiological features differentiate axPsA from ankylosing spondylitis (AS), necessitating refined classification criteria. The recommendations from the Assessment of Spondylarthritis international Society (ASAS) provide valuable guidance due to the limited direct evidence. Emerging therapies, including interleukin-23 (IL-23) inhibitors or Janus kinase (JAK) inhibitors, are under investigation for axPsA. Currently, secukinumab, an interleukin-17 (IL-17) inhibitor, is an evidence-based option for axPsA management. However, given the variability in individual patient responses and disease manifestations, personalized, evidence-based treatment approaches remain essential for optimizing patient outcomes. In the final section, two real-life cases illustrate the challenges in managing axPsA, emphasizing the importance of tailored therapies. Achieving precision in defining axPsA remains a formidable task, making detailed criteria essential for effective strategies and improving patient outcomes. [ABSTRACT FROM AUTHOR]

The implementation of tumor grading tasks with image processing and machine learning techniques has progressed immensely over the past several years. Multispectral imaging enabled us to capture the sample as a set of image bands corresponding to different wavelengths in the visible and infrared spectrums. The higher dimensional image data can be well exploited to deliver a range of discriminative features to support the tumor grading application. This paper compares the classification accuracy of RGB and multispectral images, using a case study on colorectal tumor grading with the QU-Al Ahli Dataset (dataset I). Rotation-invariant local phase quantization (LPQ) features with an SVM classifier resulted in 80% accuracy for the RGB images compared to 86% accuracy with the multispectral images in dataset I. However, the higher dimensionality elevates the processing time. We propose a band-selection strategy using mutual information between image bands. This process eliminates redundant bands and increases classification accuracy. The results show that our band-selection method provides better results than normal RGB and multispectral methods. The band-selection algorithm was also tested on another colorectal tumor dataset, the Texas University Dataset (dataset II), to further validate the results. The proposed method demonstrates an accuracy of more than 94% with 10 bands, compared to using the whole set of 16 multispectral bands. Our research emphasizes the advantages of multispectral imaging over the RGB imaging approach and proposes a band-selection method to address the higher computational demands of multispectral imaging. [ABSTRACT FROM AUTHOR]

Type 2 diabetes mellitus (T2DM) is one of the most common metabolic diseases in the world and poses a significant public health challenge. Early detection and management of this metabolic disorder is crucial to prevent complications and improve outcomes. This paper aims to find core differences in male and female markers to detect T2DM by their clinic and anthropometric features, seeking out ranges in potential biomarkers identified to provide useful information as a pre-diagnostic tool whie excluding glucose-related biomarkers using machine learning (ML) models. We used a dataset containing clinical and anthropometric variables from patients diagnosed with T2DM and patients without TD2M as control. We applied feature selection with three different techniques to identify relevant biomarker models: an improved recursive feature elimination (RFE) evaluating each set from all the features to one feature with the Akaike information criterion (AIC) to find optimal outputs; Least Absolute Shrinkage and Selection Operator (LASSO) with glmnet; and Genetic Algorithms (GA) with GALGO and forward selection (FS) applied to GALGO output. We then used these for comparison with the AIC to measure the performance of each technique and collect the optimal set of global features. Then, an implementation and comparison of five different ML models was carried out to identify the most accurate and interpretable one, considering the following models: logistic regression (LR), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbors (KNN), and nearest centroid (Nearcent). The models were then combined in an ensemble to provide a more robust approximation. The results showed that potential biomarkers such as systolic blood pressure (SBP) and triglycerides are together significantly associated with T2DM. This approach also identified triglycerides, cholesterol, and diastolic blood pressure as biomarkers with differences between male and female actors that have not been previously reported in the literature. The most accurate ML model was selection with RFE and random forest (RF) as the estimator improved with the AIC, which achieved an accuracy of 0.8820. In conclusion, this study demonstrates the potential of ML models in identifying potential biomarkers for early detection of T2DM, excluding glucose-related biomarkers as well as differences between male and female anthropometric and clinic profiles. These findings may help to improve early detection and management of the T2DM by accounting for differences between male and female subjects in terms of anthropometric and clinic profiles, potentially reducing healthcare costs and improving personalized patient attention. Further research is needed to validate these potential biomarkers ranges in other populations and clinical settings. [ABSTRACT FROM AUTHOR]

The objective of this paper was to determine how different types of posterior staphyloma (PS) may affect the appearance and degree of myopic maculopathy. A cross-sectional study was conducted, in which 467 eyes from 246 highly myopic patients [axial length (AL) ≥ 26 mm] were studied. A complete ophthalmic exploration was carried out on all patients, including imaging tests. The presence of macular PS was established as the main comparison variable between groups (macular PS vs. non-macular PS vs. non-PS). The variables analyzed included age, AL, decimal best-corrected visual acuity (BCVA), Atrophy (A)/Traction (T)/Neovascularization (N) components according to the ATN grading system, and the presence of severe pathologic myopia (PM). Out of the total, 179 eyes (38.3%) presented macular PS, 146 eyes presented non-macular PS (31.2%), and 142 eyes showed no PS (30.4%). The group without PS was significantly younger than macular PS and non-macular PS groups (53.85 vs. 66.57 vs. 65.20 years; p < 0.001 each, respectively). There were no age differences between PS groups. Eyes with macular PS (31.47 ± 2.30 mm) were significantly longer than those with non-macular PS (28.68 ± 1.78 mm, p < 0.001) and those without PS (27.47 ± 1.34 mm, p < 0.001). BCVA was significantly better in the non-PS group (0.75 ± 0.27) compared to the non-macular PS (0.56 ± 0.31) and macular PS groups (0.43 ± 0.33), with p < 0.001 each. Eyes without PS showed significantly lower A and T components (1.31 ± 0.96 and 0.30 ± 0.53, respectively) than non-macular PS (2.21 ± 0.75 and 0.71 ± 0.99, respectively, p < 0.001 each) and macular PS eyes (2.83 ± 0.64 and 1.11 ± 1.10, respectively, p < 0.001 each). The N component was lower in non-PS eyes vs. non-macular PS eyes (0.20 ± 0.59 vs. 0.47 ± 0.83, p < 0.001) and as compared to the macular PS group (0.68 ± 0.90, p < 0.01). Additionally, the N component was significantly lower in the non-macular PS group than in the macular PS one (p < 0.05). The prevalence of severe PM was different between groups (p < 0.001). It was higher among macular PS eyes (138/179) when compared to other groups (p < 0.001, each), followed by the non-macular PS eyes (40/146) and being the lowest in the non-PS group (20/142). To conclude, macular PS is associated with a more advanced maculopathy, worse vision, and higher rates of severe PM. [ABSTRACT FROM AUTHOR]

Atrial septal defects (ASDs) present a significant healthcare challenge, demanding accurate and timely diagnosis and precise management to ensure optimal patient outcomes. Artificial intelligence (AI) applications in healthcare are rapidly evolving, offering promise for enhanced medical decision-making and patient care. In the context of cardiology, the integration of AI promises to provide more efficient and accurate diagnosis and personalized treatment strategies for ASD patients. In interventional cardiology, sometimes the lack of precise measurement of the cardiac rims evaluated by transthoracic echocardiography combined with the floppy aspect of the rims can mislead and result in complications. AI software can be created to generate responses for difficult tasks, like which device is the most suitable for different shapes and dimensions to prevent embolization or erosion. This paper reviews the current state of AI in healthcare and its applications in cardiology, emphasizing the specific opportunities and challenges in applying AI to ASD diagnosis and management. By exploring the capabilities and limitations of AI in ASD diagnosis and management. This paper highlights the evolution of medical practice towards a more AI-augmented future, demonstrating the capacity of AI to unlock new possibilities for healthcare professionals and patients alike. [ABSTRACT FROM AUTHOR]

Systemic steroids are frequently used in critically ill patients for their anti-inflammatory properties. Potential benefits of these agents should be balanced against their known side effects. In this paper, we review trials assessing the use of systemic steroids in common conditions requiring admission to the intensive care unit. These include septic shock, the acute respiratory distress syndrome, severe pneumonia, COVID-19, and hypercapnic respiratory failure due to chronic obstructive pulmonary disease. We will mainly focus on well-conducted randomized controlled trials to determine whether steroids should be administered to critically ill patients presenting with these conditions. [ABSTRACT FROM AUTHOR]

Pneumonia ranks among the most prevalent lung diseases and poses a significant concern since it is one of the diseases that may lead to death around the world. Diagnosing pneumonia necessitates a chest X-ray and substantial expertise to ensure accurate assessments. Despite the critical role of lateral X-rays in providing additional diagnostic information alongside frontal X-rays, they have not been widely used. Obtaining X-rays from multiple perspectives is crucial, significantly improving the precision of disease diagnosis. In this paper, we propose a multi-view multi-feature fusion model (MV-MFF) that integrates latent representations from a variational autoencoder and a β -variational autoencoder. Our model aims to classify pneumonia presence using multi-view X-rays. Experimental results demonstrate that the MV-MFF model achieves an accuracy of 80.4% and an area under the curve of 0.775, outperforming current state-of-the-art methods. These findings underscore the efficacy of our approach in improving pneumonia diagnosis through multi-view X-ray analysis. [ABSTRACT FROM AUTHOR]