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® 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.

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.

A comparative study of an on premise AutoML solution for medical image classification.

Automated machine learning (AutoML) allows for the simplified application of machine learning to real-world problems, by the implicit handling of necessary steps such as data pre-processing, feature engineering, model selection and hyperparameter optimization. This has encouraged its use in medical applications such as imaging. However, the impact of common parameter choices such as the number of trials allowed, and the resolution of the input images, has not been comprehensively explored in existing literature. We therefore benchmark AutoKeras (AK), an open-source AutoML framework, against several bespoke deep learning architectures, on five public medical datasets representing a wide range of imaging modalities. It was found that AK could outperform the bespoke models in general, although at the cost of increased training time. Moreover, our experiments suggest that a large number of trials and higher resolutions may not be necessary for optimal performance to be achieved.

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.

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.

[Contribution of free software to medical imaging]. / Apport du logiciel libre en imagerie médicale.

Medical research uses increasingly massive, complex and interdependent data, the analysis of which requires the use of specialized algorithms. In order to independently reproduce and validate the results of a scientific study, it is no longer sufficient to share the text of the article as an open-access document, together with the raw research data according to the open-data approach. It is now also needed to share the algorithms used to analyze the data with other research teams. Free and open-source software precisely responds to this need to disseminate technical knowledge at a large scale. In this paper, we present several examples of free software used in medicine, with a particular focus on medical imaging.

La recherche médicale recourt à des données de plus en plus massives, complexes et interdépendantes dont l'analyse nécessite l'usage d'algorithmes spécialisés. Afin de reproduire et valider les résultats d'une étude scientifique de manière indépendante, il n'est, dès lors, plus suffisant de partager le texte de l'article en «open-access¼ complété avec les données brutes en «open-data¼. Il convient désormais d'également partager les algorithmes qui ont servi à l'analyse des données avec d'autres équipes de chercheurs. Le logiciel libre et «open-source¼ répond précisément à ce besoin de diffuser les connaissances techniques à grande échelle. Dans cet article, nous présentons plusieurs exemples de logiciels libres utilisés en médecine, avec une attention particulière portée à l'imagerie médicale.

Research on Medical Image Segmentation Method Based on Improved U-Net3.

Computer assisted diagnostic technology has been widely used in clinical practice, specifically focusing on medical image segmentation. Its purpose is to segment targets with certain special meanings in medical images and extract relevant features, providing reliable basis for subsequent clinical diagnosis and research. However, because of different shapes and complex structures of segmentation targets in different medical images, some imaging techniques have similar characteristics, such as intensity, color, or texture, for imaging different organs and tissues. The localization and segmentation of targets in medical images remains an urgent technical challenge to be solved. As such, an improved full scale skip connection network structure for the CT liver image segmentation task is proposed. This structure includes a biomimetic attention module between the shallow encoder and the deep decoder, and the feature fusion proportion coefficient between the two is learned to enhance the attention of the overall network to the segmented target area. In addition, based on the traditional point sampling mechanism, an improved point sampling strategy is proposed for characterizing medical images to further enhance the edge segmentation effect of CT liver targets. The experimental results on the commonly used combined (CT-MR) health absolute organ segmentation (CHAOS) dataset show that the average dice similarity coefficient (DSC) can reach 0.9467, the average intersection over union (IOU) can reach 0.9623, and the average F1 score can reach 0.9351. This indicates that the model can effectively learn image detail features and global structural features, leading to improved segmentation of liver images.

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.

AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes.

Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for precision health strategies and early detection initiatives. Leveraging artificial intelligence, particularly eXtreme Gradient Boosting (XGBoost), we devise robust risk assessment models for T2D. Drawing upon comprehensive genetic and medical imaging datasets from 68,911 individuals in the Taiwan Biobank, our models integrate Polygenic Risk Scores (PRS), Multi-image Risk Scores (MRS), and demographic variables, such as age, sex, and T2D family history. Here, we show that our model achieves an Area Under the Receiver Operating Curve (AUC) of 0.94, effectively identifying high-risk T2D subgroups. A streamlined model featuring eight key variables also maintains a high AUC of 0.939. This high accuracy for T2D risk assessment promises to catalyze early detection and preventive strategies. Moreover, we introduce an accessible online risk assessment tool for T2D, facilitating broader applicability and dissemination of our findings.

Compressed Sensing for Biomedical Photoacoustic Imaging: A Review.

Photoacoustic imaging (PAI) is a rapidly developing emerging non-invasive biomedical imaging technique that combines the strong contrast from optical absorption imaging and the high resolution from acoustic imaging. Abnormal biological tissues (such as tumors and inflammation) generate different levels of thermal expansion after absorbing optical energy, producing distinct acoustic signals from normal tissues. This technique can detect small tissue lesions in biological tissues and has demonstrated significant potential for applications in tumor research, melanoma detection, and cardiovascular disease diagnosis. During the process of collecting photoacoustic signals in a PAI system, various factors can influence the signals, such as absorption, scattering, and attenuation in biological tissues. A single ultrasound transducer cannot provide sufficient information to reconstruct high-precision photoacoustic images. To obtain more accurate and clear image reconstruction results, PAI systems typically use a large number of ultrasound transducers to collect multi-channel signals from different angles and positions, thereby acquiring more information about the photoacoustic signals. Therefore, to reconstruct high-quality photoacoustic images, PAI systems require a significant number of measurement signals, which can result in substantial hardware and time costs. Compressed sensing is an algorithm that breaks through the Nyquist sampling theorem and can reconstruct the original signal with a small number of measurement signals. PAI based on compressed sensing has made breakthroughs over the past decade, enabling the reconstruction of low artifacts and high-quality images with a small number of photoacoustic measurement signals, improving time efficiency, and reducing hardware costs. This article provides a detailed introduction to PAI based on compressed sensing, such as the physical transmission model-based compressed sensing method, two-stage reconstruction-based compressed sensing method, and single-pixel camera-based compressed sensing method. Challenges and future perspectives of compressed sensing-based PAI are also discussed.

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.

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.

Bacterial imaging in tumour diagnosis.

Some bacteria, such as Escherichia coli (E. coli) and Salmonella typhimurium (S. typhimurium), have an inherent ability to locate solid tumours, making them a versatile platform that can be combined with other tools to improve the tumour diagnosis and treatment. In anti-cancer therapy, bacteria function by carrying drugs directly or expressing exogenous therapeutic genes. The application of bacterial imaging in tumour diagnosis, a novel and promising research area, can indeed provide dynamic and real-time monitoring in both pre-treatment assessment and post-treatment detection. Different imaging techniques, including optical technology, acoustic imaging, magnetic resonance imaging (MRI) and nuclear medicine imaging, allow us to observe and track tumour-associated bacteria. Optical imaging, including bioluminescence and fluorescence, provides high-sensitivity and high-resolution imaging. Acoustic imaging is a real-time and non-invasive imaging technique with good penetration depth and spatial resolution. MRI provides high spatial resolution and radiation-free imaging. Nuclear medicine imaging, including positron emission tomography (PET) and single photon emission computed tomography (SPECT) can provide information on the distribution and dynamics of bacterial population. Moreover, strategies of synthetic biology modification and nanomaterial engineering modification can improve the viability and localization ability of bacteria while maintaining their autonomy and vitality, thus aiding the visualization of gut bacteria. However, there are some challenges, such as the relatively low bacterial abundance and heterogeneously distribution within the tumour, the high dimensionality of spatial datasets and the limitations of imaging labeling tools. In summary, with the continuous development of imaging technology and nanotechnology, it is expected to further make in-depth study on tumour-associated bacteria and develop new bacterial imaging methods for tumour diagnosis.

The realization of medical devices for precision surgery - development and implementation of 'stop-and-go' imaging technologies.

INTRODUCTION: Surgery and biomedical imaging encompass a big share of the medical-device market. The ever-mounting demand for precision surgery has driven the integration of these two into the field of image-guided surgery. A key-question herein is how imaging modalities can guide the surgical decision-making process. Through performance-based design, chemists, engineers, and doctors need to build a bridge between imaging technologies and surgical challenges. AREAS-COVERED: This perspective article highlights the complementary nature between the technological design of an image-guidance modality and the type of procedure performed. The specific roles of the involved professionals, imaging technologies, and surgical indications are addressed. EXPERT-OPINION: Molecular-image-guided surgery has the potential to advance pre-, intra- and post-operative tissue characterization. To achieve this, surgeons need the access to well-designed indication-specific chemical-agents and detection modalities. Hereby, some technologies stimulate exploration ('go'), while others stimulate caution ('stop'). However, failing to adequately address the indication-specific needs rises the risk of incorrect tool employment and sub-optimal surgical performance. Therefore, besides the availability of new technologies, market growth is highly dependent on the practical nature and impact on real-life clinical care. While urology currently takes the lead in the widespread implementation of image-guidance technologies, the topic is generic and its popularity spreads rapidly within surgical oncology.