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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]

This paper investigates the use of machine learning algorithms to aid medical professionals in the detection and risk assessment of diabetes. The research employed a dataset gathered from individuals with type 2 diabetes in Ninh Binh, Vietnam. A variety of classification algorithms, including Decision Tree Classifier, Logistic Regression, SVC, Ada Boost Classifier, Gradient Boosting Classifier, Random Forest Classifier, and K Neighbors Classifier, were utilized to identify the most suitable algorithm for the dataset. The results of the present study indicate that the Random Forest Classifier algorithm yielded the most promising results, exhibiting a cross-validation score of 0.998 and an accuracy rate of 100%. To further evaluate the effectiveness of the selected model, it was subjected to a testing phase involving a new dataset comprising 67 patients that had not been previously seen. The performance of the algorithm on this dataset resulted in an accuracy rate of 94%, especially the study's notable finding is the algorithm's accurate prediction of the probability of patients developing diabetes, as indicated by the class 1 (diabetes) probabilities. This innovative approach offers a meticulous and quantifiable method for diabetes detection and risk evaluation, showcasing the potential of machine learning algorithms in assisting clinicians with diagnosis and management. By communicating the diabetes score and probability estimates to patients, the comprehension of their disease status can be enhanced. This information empowers patients to make informed decisions and motivates them to adopt healthier lifestyle habits, ultimately playing a crucial role in impeding disease progression. The study underscores the significance of leveraging machine learning in healthcare to optimize patient care and improve long-term health outcomes. [ABSTRACT FROM AUTHOR]

Malignant melanoma is a cutaneous malignancy resulting from the uncontrolled proliferation of melanocytes and poses a challenge diagnostically because neoplastic lesions can mimic benign lesions, which are much more common in the population. Doctors, when they suspect the presence of melanoma, arrange for its removal and the performance of a histological examination to ascertain its diagnosis; in cases where the dermatoscopic examination is indicative of benignity, however, after the lesion is removed, histological examination is not always performed, a very dangerous occurrence and a harbinger of further medico-legal problems. The authors present a court litigation case of an "alleged" failure to diagnose malignant melanoma in a patient who died of brain metastases from melanoma in the absence of a certain location of the primary tumor: the physician who had removed a benign lesion a few months earlier was sued, and only thanks to the presence of photographic documentation was the health care provider able to prove his extraneousness. The aim of this paper is to formulate a proposal for a dermatological protocol to be followed in cases of excisions of benign skin lesions with a twofold purpose: on the one hand, to be able to prove, in a judicial context, the right action on the part of the sanitarians; on the other hand, to avoid the rise of so-called "defensive medicine". [ABSTRACT FROM AUTHOR]

The analysis of colonic contents is a valuable tool for the gastroenterologist and has multiple applications in clinical routine. When considering magnetic resonance imaging (MRI) modalities, T2 weighted images are capable of segmenting the colonic lumen, whereas fecal and gas contents can only be distinguished in T1 weighted images. In this paper, we present an end-to-end quasi-automatic framework that comprises all the steps needed to accurately segment the colon in T2 and T1 images and to extract colonic content and morphology data to provide the quantification of colonic content and morphology data. As a consequence, physicians have gained new insights into the effects of diets and the mechanisms of abdominal distension. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a degenerative condition of the brain that affects the memory and reasoning abilities of patients. Memory is steadily wiped out by this condition, which gradually affects the brain's ability to think, recall, and form intentions. In order to properly identify this disease, a variety of manual imaging modalities including CT, MRI, PET, etc. are being used. These methods, however, are time-consuming and troublesome in the context of early diagnostics. This is why deep learning models have been devised that are less time-intensive, require less high-tech hardware or human interaction, continue to improve in performance, and are useful for the prediction of AD, which can also be verified by experimental results obtained by doctors in medical institutions or health care facilities. In this paper, we propose a hybrid-based AI-based model that includes the combination of both transfer learning (TL) and permutation-based machine learning (ML) voting classifier in terms of two basic phases. In the first phase of implementation, it comprises two TL-based models: namely, DenseNet-121 and Densenet-201 for features extraction, whereas in the second phase of implementation, it carries out three different ML classifiers like SVM, Naïve base and XGBoost for classification purposes. The final classifier outcomes are evaluated by means of permutations of the voting mechanism. The proposed model achieved accuracy of 91.75%, specificity of 96.5%, and an F1-score of 90.25. The dataset used for training was obtained from Kaggle and contains 6200 photos, including 896 images classified as mildly demented, 64 images classified as moderately demented, 3200 images classified as non-demented, and 1966 images classified as extremely mildly demented. The results show that the suggested model outperforms current state-of-the-art models. These models could be used to generate therapeutically viable methods for detecting AD in MRI images based on these results for clinical prospective. [ABSTRACT FROM AUTHOR]

The complexity of brain tissue requires skillful technicians and expert medical doctors to manually analyze and diagnose Glioma brain tumors using multiple Magnetic Resonance (MR) images with multiple modalities. Unfortunately, manual diagnosis suffers from its lengthy process, as well as elevated cost. With this type of cancerous disease, early detection will increase the chances of suitable medical procedures leading to either a full recovery or the prolongation of the patient's life. This has increased the efforts to automate the detection and diagnosis process without human intervention, allowing the detection of multiple types of tumors from MR images. This research paper proposes a multi-class Glioma tumor classification technique using the proposed deep-learning-based features with the Support Vector Machine (SVM) classifier. A deep convolution neural network is used to extract features of the MR images, which are then fed to an SVM classifier. With the proposed technique, a 96.19% accuracy was achieved for the HGG Glioma type while considering the FLAIR modality and a 95.46% for the LGG Glioma tumor type while considering the T2 modality for the classification of four Glioma classes (Edema, Necrosis, Enhancing, and Non-enhancing). The accuracies achieved using the proposed method were higher than those reported by similar methods in the extant literature using the same BraTS dataset. In addition, the accuracy results obtained in this work are better than those achieved by the GoogleNet and LeNet pre-trained models on the same dataset. [ABSTRACT FROM AUTHOR]

Improving diagnosis has been one of the most critical issues in medicine for the last two decades. In the context of the rise of digital health and its augmentation and human diagnostic thinking, it has become necessary to integrate the concept of digital diagnosis into dual-process theory (DPT), which is the fundamental axis of the diagnostic thinking process physicians. Particularly, since the clinical decision support system (CDSS) corresponds to analytical thinking (system 2) in DPT, it is necessary to redefine system 2 to include the CDSS. However, to the best of my knowledge there has been no concrete conceptual model based on this need. The innovation and novelty of this paper are that it redefines system 2 to include new concepts and shows the relationship among the breakdown of system 2. In this definition, system 2 is divided into "inside" and "outside" brains, where "inside" includes symptomatologic, anatomical, biomechanical–physiological, and etiological thinking approaches, and "outside" includes CDSS. Moreover, this paper discusses the actual and possible future interplay between "inside" and "outside." The author envisions that this paper will serve as a cornerstone for the future development of system 2 diagnostic thinking strategy. [ABSTRACT FROM AUTHOR]

Histoplasma capsulatum (H. capsulatum) is considered to be one of the most extensively spread dysmorphic fungi worldwide. Histoplasmosis primarily impacts patients with weakened immune systems and can result in a diverse range of clinical manifestations. In immunocompetent patients, the disease may manifest as a self-limiting or asymptomatic infection; however, in immunocompromised individuals, it can occur as a debilitating, disseminated disease. Diagnosing histoplasmosis may be challenging. A medical professional that specializes in treating endemic fungal illnesses is better able to assist with an accurate and timely diagnosis since they have a deeper grasp of these illnesses. Consequently, the process of diagnosing histoplasmosis might be difficult for less experienced physicians. The case presented is an example of the myriad faces that histoplasmosis can take on, mimicking other common infectious or malignant conditions, leading to extensive work-up and invasive procedures in establishing the diagnosis of this otherwise benign condition. We hereby report the case of disseminated histoplasmosis in a young immunocompetent female patient. [ABSTRACT FROM AUTHOR]

Background: White blood cell (WBC) scintigraphy plays a major role in the diagnostic approach to periprosthetic infections. Although the procedure has been standardized by the publication of several guidelines, the interpretation of this technique may be susceptible to intra and inter-variability. We aimed to assess the reproducibility of interpretation between nuclear medicine physicians and by the same physician and to demonstrate that Cohen's coefficient is more unstable than Gwet's coefficient, as the latter is influenced by the prevalence rates. Methods: We enrolled 59 patients who performed a Technetium-99m WBC (99mTc-WBC) scintigraphy for suspected hip or knee prosthesis infection. Three physicians, blinded to all patient clinical data, performed two image readings. Each WBC study was assessed both visually and semi-quantitatively according to the guidelines of the European Association of Nuclear Medicine (EANM). For semi-quantitative analysis, readers drew an irregular Region of Interest (ROI) over the suspected infectious lesion and copied it to the normal contralateral bone. The mean counts per ROI were used to calculate lesion-to-reference tissue ( L R ) ratios for both late and delayed images. An increase in L R over time ( L R late > L R delayed ) of more than 20% was considered indicative of infection. Agreement between readers and between readings was assessed by the first-order agreement coefficient (Gwet's AC1). Reading time for each scan was compared between the three readers in both the first and the second reading, using the Generalized Linear Mixed Model. Results: An excellent agreement was found among all three readers: 0.90 for the first reading and 0.94 for the second reading. Both inter- and intra-variability showed values ≥0.86. Gwet's method demonstrated greater robustness than the Cohen coefficient when assessing the intra and inter-rater variability, since it is not influenced by the prevalence rate. Conclusions: These studies can contribute to improving the reliability of nuclear medicine imaging techniques and to evaluating the effectiveness of trainee preparation. [ABSTRACT FROM AUTHOR]

In recent years, the preferentially expressed antigen in melanoma (PRAME) has also been used in the histopathological diagnosis of melanocytic lesions, in order to understand if it could constitute a valid, inexpensive, and useful resource in dermatopathological fields. We performed a double-center study to evaluate whether the data on the usefulness and possible limitations of PRAME could also be confirmed by our group. From 1 December 2021 to 29 March 2022, we collected 275 cases of melanocytic lesions that were immunostained with PRAME (Ab219650) and rabbit monoclonal antibody (Abcam). To better correlate the PRAME expression with its nature (benign, uncertain potential for malignancy, or malignant), we categorized PRAME tumor cells' percentage positivity and intensity of immunostaining in a cumulative score obtained by adding the quartile of positive tumor cells (0, 1+, 2+, 3+, 4+) to the PRAME expression intensity in tumor cells (0, 1+, 2+, 3+). Of these 275 lesions, 136 were benign, 12 were of uncertain potential for malignancy (MELTUMP or SAMPUS or SPARK nevus), and 127 were malignant. The immunoexpression of PRAME was completely negative in 125/136 benign lesions (91.9%), with only a few positive melanocytes (1+) and intensity 1+ in the remaining 11 cases (8.1%). Of the 127 cases of melanoma (superficial spreading, lentigo maligna, and pagetoid histotypes), PRAME was strongly positive in 104/127 cases (81.8%) with intensity 4+ and 3+. In 17 cases (13.3%; melanoma spindle and nevoid cell histotypes), PRAME was positive in percentage 2+ and with intensity ranging from 2+ to 3+. In 7 cases (5.5%) of desmoplastic melanoma, PRAME was 1+ positive and/or completely negative. Of the 12 cases of lesions with uncertain potential for malignancy, the immunoexpression of PRAME was much more heterogeneous and irregularly distributed throughout the lesion. These data are perfectly in agreement with the current literature, and they demonstrate that the reliability of PRAME is quite high, but its use cannot cause physicians to disregard the morphological information and the execution of other ancillary immunohistochemical stains such as Melan-A, HMB-45, MiTF, and SOX-10. [ABSTRACT FROM AUTHOR]

Background: This study evaluates the potential of ChatGPT and Google Bard as educational tools for patients in orthopedics, focusing on sports medicine and pediatric orthopedics. The aim is to compare the quality of responses provided by these natural language processing (NLP) models, addressing concerns about the potential dissemination of incorrect medical information. Methods: Ten ACL- and flat foot-related questions from a Google search were presented to ChatGPT-3.5 and Google Bard. Expert orthopedic surgeons rated the responses using the Global Quality Score (GQS). The study minimized bias by clearing chat history before each question, maintaining respondent anonymity and employing statistical analysis to compare response quality. Results: ChatGPT-3.5 and Google Bard yielded good-quality responses, with average scores of 4.1 ± 0.7 and 4 ± 0.78, respectively, for sports medicine. For pediatric orthopedics, Google Bard scored 3.5 ± 1, while the average score for responses generated by ChatGPT was 3.8 ± 0.83. In both cases, no statistically significant difference was found between the platforms (p = 0.6787, p = 0.3092). Despite ChatGPT's responses being considered more readable, both platforms showed promise for AI-driven patient education, with no reported misinformation. Conclusions: ChatGPT and Google Bard demonstrate significant potential as supplementary patient education resources in orthopedics. However, improvements are needed for increased reliability. The study underscores the evolving role of AI in orthopedics and calls for continued research to ensure a conscientious integration of AI in healthcare education. [ABSTRACT FROM AUTHOR]

TB diagnosis has been simplified in India following advances in available diagnostic tools. This facilitates private doctors' "patient first" approach toward early diagnosis; however, costs remain high. India's NTEP established a TB diagnostic network, which is free for patients and incentivizes private doctors to participate. Drawing from this context led to the design and implementation of the One-Stop TB Diagnostic Solution model, which was conducted in the Hisar district, Haryana, allowing specimens from presumptive TB patients from private doctors to be collected and tested as per NTEPs diagnostic algorithm. A subset of data pertaining to private doctors was analyzed for the project period. Qualitative data were also collected by interviewing doctors using a snowball method to capture doctors' perception about the model. Out of 1159 specimens collected from 60 facilities, MTB was detected in 32% and rifampicin resistance was detected in 7% specimens. All specimens went through the diagnostic algorithm. Thirty doctors interviewed were satisfied with the services offered and were appreciative of the program that implements this "patient centric" model. Results from implementation indicate the need to strengthen private diagnostics through a certification process to ensure provision of quality TB diagnostic services. [ABSTRACT FROM AUTHOR]

Background: The Pulmonary Embolism Severity Index (PESI) is an extensively validated prognostic score, but impact analyses of the PESI on management strategies, outcomes and health care costs are lacking. Our aim was to assess whether the adoption of the PESI for patients admitted to an internal medicine ward has the potential to safely reduce the length of hospital stay (LOS). Methods: We carried out a multicenter randomized controlled trial, enrolling consecutive adult outpatients diagnosed with acute PE and admitted to an internal medicine ward. Within 48 h after diagnosis, the treating physicians were randomized, for every patient, to calculate and report the PESI in the clinical record form on top of the standard of care (experimental arm) or to continue routine clinical practice (standard of care). The ClinicalTrials.gov identifier is NCT03002467. Results: This study was prematurely stopped due to slow recruitment. A total of 118 patients were enrolled at six internal medicine units from 2016 to 2019. The treating physicians were randomized to the use of the PESI for 59 patients or to the standard of care for 59 patients. No difference in the median LOS was found between the experimental arm (8, IQR 6–12) and the standard-of-care arm (8, IQR 6–12) (p = 0.63). A pre-specified secondary analysis showed that the LOS was significantly shorter among the patients who were treated with DOACs (median of 8 days, IQR 5–11) compared to VKAs or heparin (median of 9 days, IQR 7–12) (p = 0.04). Conclusions: The formal calculation of the PESI in the patients already admitted to internal medicine units did not impact the length of hospital stay. [ABSTRACT FROM AUTHOR]

Dirofilariasis is an infectious disease caused by species of the Dirofilaria genus. It is manifested by the appearance of a subcutaneous swelling, especially in the eye region. We present the case of a 29-year-old patient who presented with facial asymmetry in the right genian region. Following clinical and paraclinical evaluations, the diagnosis of a parasitic cyst was established in the context of dirofilariasis with Dirofilaria repens (D. repens). Treatment consisted of surgical excision of the formation associated with prophylactic antibiotic medication. Macroscopic analysis of the excision piece revealed a structure that contained a cystic cavity and a filamentous form with a length of approximately 10 mm and a diameter of 1 mm. This is the first case of dirofilariasis located in the genian region reported in Romania. The overview of this pathology is important to raise awareness among physicians about its presence and clinical variations. Understanding such cases helps healthcare professionals enhance diagnostic skills, refine treatment strategies, and provide valuable insights into the prevalence and clinical presentation, fostering early detection and timely intervention. Detailed case reports contribute to the understanding of the disease's epidemiology, including risk factors and transmission patterns, which is essential for effective public health strategies. [ABSTRACT FROM AUTHOR]

Chronic otitis with cholesteatoma is a potentially dangerous disease that can lead to the development of intracranial abscesses. Although cerebellar abscess is half as common as cerebral abscess, it is known for its particularly difficult diagnosis, which requires the visualization of the pathological process continuity from the mastoid to the posterior fossa. In this article, we present an extremely rare case from the literature of cholesteatomatous otomastoiditis complicated with meningitis and cerebellar abscess, along with the description of technical surgical details for the plugging of the bony defect between the mastoid and posterior fossa with muscle and surgical glue. The particularity of this case lies in the late presentation to the doctor of an immunocompetent patient, through a dramatic symptomatology of life-threatening complications. We emphasize the importance of responsibly treating any episode of middle ear infection and considering the existence of underlying pathologies. In such cases, we recommend additional neuroimaging explorations, which can prevent potentially lethal complications. The treatment of such intracranial complications must be carried out promptly and requires collaboration between a neurosurgeon and an ENT surgeon. [ABSTRACT FROM AUTHOR]

Background: Foreign body ingestion (FBI) represents the most common cause of emergent gastrointestinal endoscopy in children. FBI's management can be quite challenging for physicians because of the variability of the clinical presentation, and the decision tree becomes even more intricate because of patient-specific variables that must be considered in the pediatric age range (e.g., age of patients and neuropsychiatric disorders) in addition to the mere characteristics of the foreign body. We present an application for smartphones designed for pediatricians and pediatric surgeons based on the latest guidelines from the official pediatric societies. The app aims to help physicians manage FBI quickly and properly in children. Materials and methods: The latest pediatric FBI management guidelines were reviewed and summarized. The flow chart we obtained guided the development of a smartphone application. A questionnaire was administered to all pediatric surgeon trainees at our institute to test the feasibility and helpfulness of the application. Results: An app for smartphones was obtained and shared for free on the Google Play Store and Apple Store. The app guides the physician step by step in the diagnostic process, analyzing all patient- and foreign body-specific characteristics. The app consultation ends with a suggestion of the most proper decision to make in terms of further radiological investigations and the indication and timing of endoscopy. A questionnaire administered to trainees proved the app to be useful and easy to use. Conclusion: We developed an app able to help pediatricians and pediatric surgeons manage FBI in children, providing standardized and updated recommendations in a smart and easily available way. [ABSTRACT FROM AUTHOR]

Background: Thyroid nodules are very common. In most cases, they are benign, but they can be malignant in a low percentage of cases. The accurate assessment of these nodules is critical to choosing the next diagnostic steps and potential treatment. Ultrasound (US) imaging, the primary modality for assessing these nodules, can lack objectivity due to varying expertise among physicians. This leads to observer variability, potentially affecting patient outcomes. Purpose: This study aims to assess the potential of a Decision Support System (DSS) in reducing these variabilities for thyroid nodule detection and region estimation using US images, particularly in lesser experienced physicians. Methods: Three physicians with varying levels of experience evaluated thyroid nodules on US images, focusing on nodule detection and estimating cystic and solid regions. The outcomes were compared to those obtained from a DSS for comparison. Metrics such as classification match percentage and variance percentage were used to quantify differences. Results: Notable disparities exist between physician evaluations and the DSS assessments: the overall classification match percentage was just 19.2%. Individually, Physicians 1, 2, and 3 had match percentages of 57.6%, 42.3%, and 46.1% with the DSS, respectively. Variances in assessments highlight the subjectivity and observer variability based on physician experience levels. Conclusions: The evident variability among physician evaluations underscores the need for supplementary decision-making tools. Given its consistency, the CAD offers potential as a reliable "second opinion" tool, minimizing human-induced variabilities in the critical diagnostic process of thyroid nodules using US images. Future integration of such systems could bolster diagnostic precision and improve patient outcomes. [ABSTRACT FROM AUTHOR]

For many years, we have faced the complications of viral hepatitis and alcohol-related liver diseases such as cirrhosis, decompensation, portal hypertension, and hepatocellular carcinoma (HCC). Recently, we have seen a dynamic change in the field of hepatology. With the significant achievements in eradicating the hepatitis C virus by direct-acting antiviral agents and the rising epidemic of obesity, diabetes mellitus, and metabolic syndrome, there is a paradigm shift in the leading cause of liver cirrhosis and cancer to nonalcoholic fatty liver disease (NAFLD). Current data highlight the rapidly rising incidence of NAFLD-related HCC worldwide and expose the unseen part of the iceberg. In this review, we aim to update knowledge about the pathogenesis of NAFLD-induced HCC, surveillance difficulties, and promising disease markers. Molecular biomarkers, for example, may become a promising cornerstone for risk-stratified surveillance, early detection, and treatment selection for NAFLD-related HCC. Physicians can offer personalized and tailor-made clinical decisions for this unique patient subgroup. [ABSTRACT FROM AUTHOR]

Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide fast and accurate medical diagnoses. However, the high complexity makes deep neural networks and their predictions difficult to understand. Providing model explanations can be a way of increasing the understanding of "black box" models and building trust. In this work, we applied transfer learning to develop a deep neural network to predict sex from electrocardiograms. Using the visual explanation method Grad-CAM, heat maps were generated from the model in order to understand how it makes predictions. To evaluate the usefulness of the heat maps and determine if the heat maps identified electrocardiogram features that could be recognized to discriminate sex, medical doctors provided feedback. Based on the feedback, we concluded that, in our setting, this mode of explainable artificial intelligence does not provide meaningful information to medical doctors and is not useful in the clinic. Our results indicate that improved explanation techniques that are tailored to medical data should be developed before deep neural networks can be applied in the clinic for diagnostic purposes. [ABSTRACT FROM AUTHOR]

Long COVID syndrome has emerged as a long-lasting consequence of acute SARS-CoV-2 infection in adults. In addition, children may be affected by Long COVID, with potential clinical issues in different fields, including problems in school performance and daily activities. Yet, the pathophysiologic bases of Long COVID in children are largely unknown, and it is difficult to predict who will develop the syndrome. In this multidisciplinary clinical review, we summarise the latest scientific data regarding Long COVID and its impact on children. Special attention is given to diagnostic tests, in order to help the physicians to find potential disease markers and quantify impairment. Specifically, we assess the respiratory, upper airways, cardiac, neurologic and motor and psychological aspects. Finally, we also propose a multidisciplinary clinical approach. [ABSTRACT FROM AUTHOR]

Doctors' diagnosis preferences are different, which makes them adopt different assumptions in medical decision making. Taking the diagnosis of thyroid nodules as an example, this study compares three assumptions, namely deletion, imputation based on the distribution (distribution), and benign by default (benign). For deletion, which is the most used assumption, the clinical reports with missing features would be deleted. For distribution, the missing features would be replaced with a distribution of features with respective probabilities. Besides the two assumptions, certain doctors have also stated that they leave benign features unrecorded because they think that such benign features are irrelevant to the final diagnosis. Under the benign assumption, the missing features would be replaced with benign features. The three assumptions are tested comparatively. Moreover, the belief rule base (BRB) is used to construct the diagnostic model under the three assumptions since it is essentially a white-box approach that can provide good interpretability and direct access to doctors and patients. A total of 3766 clinical reports on thyroid nodule diagnosis were collected from ten radiologists over a seven-year period. Case study results validate that the benign by default assumption has produced the optimal results, although different doctors could present varied tendencies towards different assumptions. Guidance and suggestions for doctors' practical work have been made based on the study results to improve work efficiency and diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) is a serious illness with important consequences for patients and health systems. Estimation of prevalence and incidence, especially in early stages, is difficult due to a lack of epidemiological studies and consolidated registries. In general, the disease awareness is low, and thus CKD is not timely diagnosed in most cases. Robust screening programs are not implemented in Eastern European countries. A panel consisting of Primary Care Physicians and Nephrologists from Bulgaria, Croatia, Serbia, and Slovenia virtually met in December 2021 to discuss current CKD awareness and diagnostic approaches in the Balkan area The meeting resulted in specific calls to action in the region to improve the number and quality of epidemiology studies and the level of awareness among patients and medical communities, as well as implementation of screening programs in high-risk populations. Collaboration between specialists was acknowledged as a crucial driver for optimal management of patients with CKD. Joint efforts are required to persuade healthcare authorities to establish specific policies for better care of kidney patients. [ABSTRACT FROM AUTHOR]

The pancreas is a glandular organ that is responsible for the proper functioning of the digestive and endocrine systems, and therefore, it affects the condition of the entire body. Consequently, it is important to effectively diagnose and treat diseases of this organ. According to clinicians, pancreatitis—a common disease affecting the pancreas—is one of the most complicated and demanding diseases of the abdomen. The classification of pancreatitis is based on clinical, morphologic, and histologic criteria. Medical doctors distinguish, inter alia, acute pancreatitis (AP), the most common causes of which are gallstone migration and alcohol abuse. Effective diagnostic methods and the correct assessment of the severity of acute pancreatitis determine the selection of an appropriate treatment strategy and the prediction of the clinical course of the disease, thus preventing life-threatening complications and organ dysfunction or failure. This review collects and organizes recommendations and guidelines for the management of patients suffering from acute pancreatitis. [ABSTRACT FROM AUTHOR]

Breast cancer is the leading cause of death for women globally. In clinical practice, pathologists visually scan over enormous amounts of gigapixel microscopic tissue slide images, which is a tedious and challenging task. In breast cancer diagnosis, micro-metastases and especially isolated tumor cells are extremely difficult to detect and are easily neglected because tiny metastatic foci might be missed in visual examinations by medical doctors. However, the literature poorly explores the detection of isolated tumor cells, which could be recognized as a viable marker to determine the prognosis for T1NoMo breast cancer patients. To address these issues, we present a deep learning-based framework for efficient and robust lymph node metastasis segmentation in routinely used histopathological hematoxylin–eosin-stained (H–E) whole-slide images (WSI) in minutes, and a quantitative evaluation is conducted using 188 WSIs, containing 94 pairs of H–E-stained WSIs and immunohistochemical CK(AE1/AE3)-stained WSIs, which are used to produce a reliable and objective reference standard. The quantitative results demonstrate that the proposed method achieves 89.6% precision, 83.8% recall, 84.4% F1-score, and 74.9% mIoU, and that it performs significantly better than eight deep learning approaches, including two recently published models (v3_DCNN and Xception-65), and three variants of Deeplabv3+ with three different backbones, namely, U-Net, SegNet, and FCN, in precision, recall, F1-score, and mIoU ( p < 0.001 ). Importantly, the proposed system is shown to be capable of identifying tiny metastatic foci in challenging cases, for which there are high probabilities of misdiagnosis in visual inspection, while the baseline approaches tend to fail in detecting tiny metastatic foci. For computational time comparison, the proposed method takes 2.4 min for processing a WSI utilizing four NVIDIA Geforce GTX 1080Ti GPU cards and 9.6 min using a single NVIDIA Geforce GTX 1080Ti GPU card, and is notably faster than the baseline methods (4-times faster than U-Net and SegNet, 5-times faster than FCN, 2-times faster than the 3 different variants of Deeplabv3+, 1.4-times faster than v3_DCNN, and 41-times faster than Xception-65). [ABSTRACT FROM AUTHOR]

Wireless Capsule Endoscopy (WCE) is a procedure to examine the human digestive system for potential mucosal polyps, tumours, or bleedings using an encapsulated camera. This work focuses on polyp detection within WCE videos through Machine Learning. When using Machine Learning in the medical field, scarce and unbalanced datasets often make it hard to receive a satisfying performance. We claim that using Sequential Models in order to take the temporal nature of the data into account improves the performance of previous approaches. Thus, we present a bidirectional Long Short-Term Memory Network (BLSTM), a sequential network that is particularly designed for temporal data. We find the BLSTM Network outperforms non-sequential architectures and other previous models, receiving a final Area under the Curve of 93.83 % . Experiments show that our method of extracting spatial and temporal features yields better performance and could be a possible method to decrease the time needed by physicians to analyse the video material. [ABSTRACT FROM AUTHOR]

Inborn errors of metabolism (IEM) constitute an important group of conditions characterized by an altered metabolic pathway. There are numerous guidelines for the diagnosis and management of IEMs in the pediatric population but not for adults. Given the increasing frequency of this group of conditions in adulthood, other clinicians in addition to pediatricians should be aware of them and learn to identify their characteristic manifestations. Early recognition and implementation of an appropriate therapeutic approach would improve the clinical outcome of many of these patients. This review presents when and how to investigate a metabolic disorder with the aim of encouraging physicians not to overlook a treatable disorder. [ABSTRACT FROM AUTHOR]

To evaluate the reader's diagnostic performance against the ground truth with and without the help of a novel content-based image retrieval system (CBIR) that retrieves images with similar CT patterns from a database of 79 different interstitial lung diseases. We evaluated three novice readers' and three resident physicians' (with at least three years of experience) diagnostic performance evaluating 50 different CTs featuring 10 different patterns (e.g., honeycombing, tree-in bud, ground glass, bronchiectasis, etc.) and 24 different diseases (sarcoidosis, UIP, NSIP, Aspergillosis, COVID-19 pneumonia etc.). The participants read the cases first without assistance (and without feedback regarding correctness), and with a 2-month interval in a random order with the assistance of the novel CBIR. To invoke the CBIR, a ROI is placed into the pathologic pattern by the reader and the system retrieves diseases with similar patterns. To further narrow the differential diagnosis, the readers can consult an integrated textbook and have the possibility of selecting high-level semantic features representing clinical information (chronic, infectious, smoking status, etc.). We analyzed readers' accuracy without and with CBIR assistance and further tested the hypothesis that the CBIR would help to improve diagnostic performance utilizing Wilcoxon signed rank test. The novice readers demonstrated an unassisted accuracy of 18/28/44%, and an assisted accuracy of 84/82/90%, respectively. The resident physicians demonstrated an unassisted accuracy of 56/56/70%, and an assisted accuracy of 94/90/96%, respectively. For each reader, as well as overall, Sign test demonstrated statistically significant (p < 0.01) difference between the unassisted and the assisted reads. For students and physicians, Chi²-test and Mann-Whitney-U test demonstrated statistically significant (p < 0.01) difference for unassisted reads and statistically insignificant (p > 0.01) difference for assisted reads. The evaluated CBIR relying on pattern analysis and featuring the option to filter the results of the CBIR by predominant characteristics of the diseases via selecting high-level semantic features helped to drastically improve novices' and resident physicians' accuracy in diagnosing interstitial lung diseases in CT. [ABSTRACT FROM AUTHOR]

Background: Structured reporting (SR) in radiology has been recognized recently by major scientific societies. This study aims to build structured computed tomography (CT) and magnetic resonance (MR)-based reports in pancreatic adenocarcinoma during the staging phase in order to improve communication between the radiologist and members of multidisciplinary teams. Materials and Methods: A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A modified Delphi process was used to develop the CT-SR and MRI-SR, assessing a level of agreement for all report sections. Cronbach's alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation. Results: The final CT-SR version was built by including n = 16 items in the "Patient Clinical Data" section, n = 11 items in the "Clinical Evaluation" section, n = 7 items in the "Imaging Protocol" section, and n = 18 items in the "Report" section. Overall, 52 items were included in the final version of the CT-SR. The final MRI-SR version was built by including n = 16 items in the "Patient Clinical Data" section, n = 11 items in the "Clinical Evaluation" section, n = 8 items in the "Imaging Protocol" section, and n = 14 items in the "Report" section. Overall, 49 items were included in the final version of the MRI-SR. In the first round for CT-SR, all sections received more than a good rating. The overall mean score of the experts was 4.85. The Cα correlation coefficient was 0.85. In the second round, the overall mean score of the experts was 4.87, and the Cα correlation coefficient was 0.94. In the first round, for MRI-SR, all sections received more than a good rating. The overall mean score of the experts was 4.73. The Cα correlation coefficient was 0.82. In the second round, the overall mean score of the experts was 4.91, and the Cα correlation coefficient was 0.93. Conclusions: The CT-SR and MRI-SR are based on a multi-round consensus-building Delphi exercise derived from the multidisciplinary agreement of expert radiologists in order to obtain more appropriate communication tools for referring physicians. [ABSTRACT FROM AUTHOR]

Among patients with peripheral artery disease, an altered estimation of walking ability reported to the physician may influence the choice of treatment. We compared claudication distance (CD) values reported by patients or assessed by validated protocols to elaborate a formula capable of estimating more reliable values. Three hundred fifty-nine patients with claudication were measured at the time of entry into a rehabilitation program. Walking performance was obtained by patients' reports (self-reported claudication distance, SR-CD) and was directly assessed to determine the claudication and maximal walking distance by the 6-min test (6-CD and 6-MWD) and an incremental treadmill test (T-CD and T-MWD). The degree of muscle deoxygenation was objectively determined at the calf by near-infrared spectroscopy (NIRS) during the treadmill test. Among the 289 subjects analyzed, SR-CD exceeded both 6-CD and T-CD (+155 and +182 m, respectively). SR-CD was moderately correlated with T-CD (r = 0.30), 6-CD (r = 0.32), and 6-MWD (r = 0.29) but not with muscle deoxygenation per meter walked, unlike T-CD and 6-CD. A formula adjusted for the presence of diabetes reduced patient overestimation by 92%. The patient's reported claudication distance was generally overestimated compared to objective measures, and it was made more reliable through a corrective factor for easy use in a clinical setting. [ABSTRACT FROM AUTHOR]