ACR Appropriateness Criteria® Hydronephrosis on Prior Imaging-Unknown Cause.

Initial imaging evaluation of hydronephrosis of unknown etiology is a complex subject and is dependent on clinical context. In asymptomatic patients, it is often best conducted via CT urography (CTU) without and with contrast, MR urography (MRU) without and with contrast, or scintigraphic evaluation with mercaptoacetyltriglycine (MAG3) imaging. For symptomatic patients, CTU without and with contrast, MRU without and with contrast, MAG3 scintigraphy, or ultrasound of the kidneys and bladder with Doppler imaging are all viable initial imaging studies. In asymptomatic pregnant patients, nonionizing imaging with US of the kidneys and bladder with Doppler imaging is preferred. Similarly, in symptomatic pregnant patients, US of the kidneys and bladder with Doppler imaging or MRU without contrast is the imaging study of choice, as both ionizing radiation and gadolinium contrast are avoided in pregnancy. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

ACR Appropriateness Criteria® Soft Tissue Vascular Anomalies: Vascular Malformations and Infantile Vascular Tumors (Non-CNS)-Child.

Soft tissue vascular anomalies may be composed of arterial, venous, and/or lymphatic elements, and diagnosed prenatally or later in childhood or adulthood. They are divided into categories of vascular malformations and vascular tumors. Vascular malformations are further divided into low-flow and fast-flow lesions. A low-flow lesion is most common, with a prevalence of 70%. Vascular tumors may behave in a benign, locally aggressive, borderline, or malignant manner. Infantile hemangioma is a vascular tumor that presents in the neonatal period and then regresses. The presence or multiple skin lesions in an infant can signal underlying visceral vascular anomalies, and complex anomalies may be associated with overgrowth syndromes. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

ACR Appropriateness Criteria® Sepsis.

Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection. A search for the underlying cause of infection typically includes radiological imaging as part of this investigation. This document focuses on thoracic and abdominopelvic causes of sepsis. In 2017, the global incidence of sepsis was estimated to be 48.9 million cases, with 11 million sepsis-related deaths (accounting for nearly 20% of all global deaths); therefore, understanding which imaging modalities and types of studies are acceptable or not acceptable is imperative. The 5 variants provided include the most commonly encountered scenarios in the setting of sepsis along with recommendations and data for each imaging study. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

ACR Appropriateness Criteria® Workup of Pleural Effusion or Pleural Disease.

Pleural effusions are categorized as transudative or exudative, with transudative effusions usually reflecting the sequala of a systemic etiology and exudative effusions usually resulting from a process localized to the pleura. Common causes of transudative pleural effusions include congestive heart failure, cirrhosis, and renal failure, whereas exudative effusions are typically due to infection, malignancy, or autoimmune disorders. This document summarizes appropriateness guidelines for imaging in four common clinical scenarios in patients with known or suspected pleural effusion or pleural disease. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

ACR Appropriateness Criteria® Orbital Imaging and Vision Loss-Child.

Orbital disorders in children consist of varied pathologies affecting the orbits, orbital contents, visual pathway, and innervation of the extraocular or intraocular muscles. The underlying etiology of these disorders may be traumatic or nontraumatic. Presumed location of the lesion along with the additional findings, such as eye pain, swelling, exophthalmos/enophthalmos, erythema, conjunctival vascular dilatation, intraocular pressure, etc, help in determining if imaging is needed, modality of choice, and extent of coverage (orbits and/or head). Occasionally, clinical signs and symptoms may be nonspecific, and, in these cases, diagnostic imaging studies play a key role in depicting the nature and extent of the injury or disease. In this document, various clinical scenarios are discussed by which a child may present with an orbital or vision abnormality. Imaging studies that might be most appropriate (based on the best available evidence or expert consensus) in these clinical scenarios are also discussed. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Non-gastrointestinal stromal tumor, mesenchymal neoplasms of the gastrointestinal tract: a review of tumor genetics, pathology, and cross-sectional imaging findings.

There is a diverse group of non-gastrointestinal stromal tumor (GIST), mesenchymal neoplasms of the gastrointestinal (GI) tract that demonstrate characteristic pathology and histogenesis as well as variable imaging findings and biological behavior. Recent advancements in tumor genetics have unveiled specific abnormalities associated with certain tumors, influencing their molecular pathogenesis, biology, response to treatment, and prognosis. Notably, giant fibrovascular polyps of the esophagus, identified through MDM2 gene amplifications, are now classified as liposarcomas. Some tumors exhibit distinctive patterns of disease distribution. Glomus tumors and plexiform fibromyxomas exhibit a pronounced affinity for the gastric antrum. In contrast, smooth muscle tumors within the GI tract are predominantly found in the esophagus and colorectum, surpassing the incidence of GISTs in these locations. Surgical resection suffices for symptomatic benign tumors; multimodality treatment may be necessary for frank sarcomas. This article aims to elucidate the cross-sectional imaging findings associated with a wide spectrum of these tumors, providing insights that align with their histopathological features.

Consumer electronics based smart technologies for enhanced terahertz healthcare having an integration of split learning with medical imaging.

The proposed work contains three major contribution, such as smart data collection, optimized training algorithm and integrating Bayesian approach with split learning to make privacy of the patent data. By integrating consumer electronics device such as wearable devices, and the Internet of Things (IoT) taking THz image, perform EM algorithm as training, used newly proposed slit learning method the technology promises enhanced imaging depth and improved tissue contrast, thereby enabling early and accurate disease detection the breast cancer disease. In our hybrid algorithm, the breast cancer model achieves an accuracy of 97.5 percent over 100 epochs, surpassing the less accurate old models which required a higher number of epochs, such as 165.

Imaging Assessment of Radiation Therapy-Related Normal Tissue Injury in Children: A PENTEC Visionary Statement.

The Pediatric Normal Tissue Effects in the Clinic (PENTEC) consortium has made significant contributions to understanding and mitigating the adverse effects of childhood cancer therapy. This review addresses the role of diagnostic imaging in detecting, screening, and comprehending radiation therapy-related late effects in children, drawing insights from individual organ-specific PENTEC reports. We further explore how the development of imaging biomarkers for key organ systems, alongside technical advancements and translational imaging approaches, may enhance the systematic application of imaging evaluations in childhood cancer survivors. Moreover, the review critically examines knowledge gaps and identifies technical and practical limitations of existing imaging modalities in the pediatric population. Addressing these challenges may expand access to, minimize the risk of, and optimize the real-world application of, new imaging techniques. The PENTEC team envisions this document as a roadmap for the future development of imaging strategies in childhood cancer survivors, with the overarching goal of improving long-term health outcomes and quality of life for this vulnerable population.

Shifting to machine supervision: annotation-efficient semi and self-supervised learning for automatic medical image segmentation and classification.

Advancements in clinical treatment are increasingly constrained by the limitations of supervised learning techniques, which depend heavily on large volumes of annotated data. The annotation process is not only costly but also demands substantial time from clinical specialists. Addressing this issue, we introduce the S4MI (Self-Supervision and Semi-Supervision for Medical Imaging) pipeline, a novel approach that leverages advancements in self-supervised and semi-supervised learning. These techniques engage in auxiliary tasks that do not require labeling, thus simplifying the scaling of machine supervision compared to fully-supervised methods. Our study benchmarks these techniques on three distinct medical imaging datasets to evaluate their effectiveness in classification and segmentation tasks. Notably, we observed that self-supervised learning significantly surpassed the performance of supervised methods in the classification of all evaluated datasets. Remarkably, the semi-supervised approach demonstrated superior outcomes in segmentation, outperforming fully-supervised methods while using 50% fewer labels across all datasets. In line with our commitment to contributing to the scientific community, we have made the S4MI code openly accessible, allowing for broader application and further development of these methods. The code can be accessed at https://github.com/pranavsinghps1/S4MI .

A novel deep learning technique for medical image analysis using improved optimizer.

Application of Convolutional neural network in spectrum of Medical image analysis are providing benchmark outputs which converges the interest of many researchers to explore it in depth. Latest preprocessing technique Real ESRGAN (Enhanced super resolution generative adversarial network) and GFPGAN (Generative facial prior GAN) are proving their efficacy in providing high resolution dataset. Objective: Optimizer plays a vital role in upgrading the functioning of CNN model. Different optimizers like Gradient descent, Stochastic Gradient descent, Adagrad, Adadelta and Adam etc. are used for classification and segmentation of Medical image but they suffer from slow processing due to their large memory requirement. Stochastic Gradient descent suffers from high variance and is computationally expensive. Dead neuron problem also proves to detrimental to the performance of most of the optimizers. A new optimization technique Gradient Centralization is providing the unparalleled result in terms of generalization and execution time. Method: Our paper explores the next factor which is the employment of new optimization technique, Gradient centralization (GC) to our integrated framework (Model with advanced preprocessing technique). Result and conclusion: Integrated Framework of Real ESRGAN and GFPGAN with Gradient centralization provides an optimal solution for deep learning models in terms of Execution time and Loss factor improvement.

Reliability of a Musculoskeletal Imaging Competency Examination for Physical Therapists.

INTRODUCTION: The Burley Readiness Examination (BRE) for Musculoskeletal (MSK) Imaging Competency assesses physical therapists' baseline MSK imaging competency. Establishing its reliability is essential to its value in determining MSK imaging competency. The purpose of this study was to test the reliability of the BRE for MSK Imaging Competency among physical therapists (PTs) with varying levels of training and education. REVIEW OF LITERATURE: Previous literature supports PTs' utility concerning diagnostic imaging; however, no studies directly measure their competency. With PTs expanding their practice scope and professional PT education programs, increasing their MSK imaging instruction, assessing competency becomes strategic in determining the future of MSK education and training. SUBJECTS: One hundred twenty-three United States licensed PTs completed the BRE. METHODS: Physical therapists completed the BRE through an online survey platform. Point biserial correlation (rpb) was calculated for each examination question. Final analyses were based on 140 examination questions. Examination scores were compared using independent sample t-test and one-way analysis of variance. Chi-square tests and odds ratios (ORs) assessed the relationship of a passing examination score (≥75%) and the type of training. Reliability of the BRE was assessed using Cronbach's alpha (α). RESULTS: Mean overall examination score was 75.89 ± 8.56%. Seventy PTs (56.9%) obtained a passing score. Physical therapists with additional MSK imaging training, board certification, and residency or fellowship training scored significantly higher (P < .001) compared with those with only entry-level PT program education. Physical therapists with additional MSK imaging training scored significantly higher (x̄ = 81.07% ± 8.93%) and were almost 5 times (OR = 4.74, 95% CI [1.95-11.50]) as likely to achieve a passing score than those without. The BRE demonstrated strong internal consistency (Cronbach's α = 0.874). DISCUSSION AND CONCLUSIONS: The BRE was reliable, consistently identifying higher examination scores among those with increased MSK imaging training. Training in MSK imaging influenced competency more than other factors. The BRE may be of analytical value to PT professional and postprofessional programs.

Investigating the impact of data heterogeneity on the performance of federated learning algorithm using medical imaging.

In recent years, Federated Learning (FL) has gained traction as a privacy-centric approach in medical imaging. This study explores the challenges posed by data heterogeneity on FL algorithms, using the COVIDx CXR-3 dataset as a case study. We contrast the performance of the Federated Averaging (FedAvg) algorithm on non-identically and independently distributed (non-IID) data against identically and independently distributed (IID) data. Our findings reveal a notable performance decline with increased data heterogeneity, emphasizing the need for innovative strategies to enhance FL in diverse environments. This research contributes to the practical implementation of FL, extending beyond theoretical concepts and addressing the nuances in medical imaging applications. This research uncovers the inherent challenges in FL due to data diversity. It sets the stage for future advancements in FL strategies to effectively manage data heterogeneity, especially in sensitive fields like healthcare.

Advanced Imaging of Gout and Other Inflammatory Diseases Around the Knee.

The knee is one of the most commonly affected joints in the course of inflammatory arthropathies, such as crystal-induced and autoimmune inflammatory arthritis. The latter group includes systemic connective tissue diseases and spondyloarthropathies. The different pathogenesis of these entities results in their varied radiologic images. Some lead quickly to joint destruction, others only after many years, and in the remaining, destruction will not be a distinguishing radiologic feature.Radiography, ultrasonography, and magnetic resonance imaging have traditionally been the primary modalities in the diagnosis of noninflammatory and inflammatory arthropathies. In the case of crystallopathies, dual-energy computed tomography has been introduced. Hybrid techniques also offer new diagnostic opportunities. In this article, we discuss the pathologic findings and imaging correlations for crystallopathies and inflammatory diseases of the knee, with an emphasis on recent advances in their imaging diagnosis.

Imaging in Lung Cancer Staging.

Lung cancer remains one of the leading causes of mortality worldwide, as well as in the United States. Clinical staging, primarily with imaging, is integral to stratify patients into groups that determine treatment options and predict survival. The eighth edition of the tumor, node, metastasis (TNM-8) staging system proposed in 2016 by the International Association for the Study of Lung Cancer remains the current standard for lung cancer staging. The system is used for all subtypes of lung cancer, including non-small cell lung cancer, small cell lung cancer, and bronchopulmonary carcinoid tumors.

Implementing vision transformer for classifying 2D biomedical images.

In recent years, the growth spurt of medical imaging data has led to the development of various machine learning algorithms for various healthcare applications. The MedMNISTv2 dataset, a comprehensive benchmark for 2D biomedical image classification, encompasses diverse medical imaging modalities such as Fundus Camera, Breast Ultrasound, Colon Pathology, Blood Cell Microscope etc. Highly accurate classifications performed on these datasets is crucial for identification of various diseases and determining the course of treatment. This research paper presents a comprehensive analysis of four subsets within the MedMNISTv2 dataset: BloodMNIST, BreastMNIST, PathMNIST and RetinaMNIST. Each of these selected datasets is of diverse data modalities and comes with various sample sizes, and have been selected to analyze the efficiency of the model against diverse data modalities. The study explores the idea of assessing the Vision Transformer Model's ability to capture intricate patterns and features crucial for these medical image classification and thereby transcend the benchmark metrics substantially. The methodology includes pre-processing the input images which is followed by training the ViT-base-patch16-224 model on the mentioned datasets. The performance of the model is assessed using key metrices and by comparing the classification accuracies achieved with the benchmark accuracies. With the assistance of ViT, the new benchmarks achieved for BloodMNIST, BreastMNIST, PathMNIST and RetinaMNIST are 97.90%, 90.38%, 94.62% and 57%, respectively. The study highlights the promise of Vision transformer models in medical image analysis, preparing the way for their adoption and further exploration in healthcare applications, aiming to enhance diagnostic accuracy and assist medical professionals in clinical decision-making.