Wrong-level spine surgery remains a significant concern in spine surgery, leading to devastating consequences for patients and healthcare systems alike. This comprehensive review aims to analyze the existing literature on wrong-level spine surgery in spine procedures, identifying key factors that contribute to these errors and exploring advanced strategies and technologies designed to prevent them. A systematic literature search was conducted across multiple databases, including PubMed, Scopus, EMBASE, and CINAHL. The selection criteria focused on preclinical and clinical studies that specifically addressed wrong site and wrong level surgeries in the context of spine surgery. The findings reveal a range of contributing factors to wrong-level spine surgeries, including communication failures, inadequate preoperative planning, and insufficient surgical protocols. The review emphasizes the critical role of innovative technologies-such as artificial intelligence, advanced imaging techniques, and surgical navigation systems-alongside established safety protocols like digital checklists and simulation training in enhancing surgical accuracy and preventing errors. In conclusion, integrating advanced technologies and systematic safety protocols is instrumental in reducing the incidence of wrong-level spine surgeries. This review underscores the importance of continuous education and the adoption of innovative solutions to foster a culture of safety and improve surgical outcomes. By addressing the multifaceted challenges associated with these errors, the field can work towards minimizing their occurrence and enhancing patient care.

<i>"Just Accepted" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i> Artificial Intelligence (AI) has demonstrated strong potential in automating medical imaging tasks, with potential applications across disease diagnosis, prognosis, treatment planning, and posttreatment surveillance. However, privacy concerns surrounding patient data remain a major barrier to the widespread adoption of AI in clinical practice, as large and diverse training datasets are essential for developing accurate, robust, and generalizable AI models. Federated Learning offers a privacy-preserving solution by enabling collaborative model training across institutions without sharing sensitive data. Instead, model parameters, such as model weights, are exchanged between participating sites. Despite its potential, federated learning is still in its early stages of development and faces several challenges. Notably, sensitive information can still be inferred from the shared model parameters. Additionally, postdeployment data distribution shifts can degrade model performance, making uncertainty quantification essential. In federated learning, this task is particularly challenging due to data heterogeneity across participating sites. This review provides a comprehensive overview of federated learning, privacy-preserving federated learning, and uncertainty quantification in federated learning. Key limitations in current methodologies are identified, and future research directions are proposed to enhance data privacy and trustworthiness in medical imaging applications. ©RSNA, 2025.

Stereotactic radiosurgery (SRS) is a non-invasive technique that is transforming the management of benign intracranial lesions through its precision and preservation of healthy tissues. It is effective for meningiomas, trigeminal neuralgia (TN), pituitary adenomas, vestibular schwannomas, and arteriovenous malformations. SRS ensures high tumor control rates, particularly for Grade I meningiomas and vestibular schwannomas. For refractory TN, it provides initial pain relief > 80 %. The advent of technologies such as PET-MRI, hypofractionation, and artificial intelligence is further improving treatment precision, but challenges remain, including the management of late side effects and standardization of practice.

Musculoskeletal ultrasound (MSUS) and magnetic resonance imaging (MRI) are advanced imaging techniques that are increasingly important in the diagnosis and management of rheumatoid arthritis (RA) and have significantly enhanced the rheumatologist's ability to assess RA disease activity and progression. This review serves as a five-year update to our previous publication on the contemporary role of imaging in RA, emphasizing the continued importance of MSUS and MRI in clinical practice and their expanding utility. The review examines the role of MSUS in diagnosing RA, differentiating RA from mimickers, scoring systems and quality control measures, novel longitudinal approaches to disease monitoring, and patient populations that may benefit most from MSUS. It also examines the role of MRI in diagnosing pre-clinical and early RA, disease activity monitoring, research and clinical trials, and development of alternative scoring approaches utilizing artificial intelligence. Finally, the role of MRI in RA diagnosis and management is summarized, and selected practice points offer key tips for integrating MSUS and MRI into clinical practice.

Urodynamic studies (UDS) are essential for evaluating lower urinary tract function but are limited by patient discomfort, lack of standardization and diagnostic variability. Advances in technology aim to address these challenges and improve diagnostic accuracy and patient comfort. AUM offers physiological assessment by allowing natural bladder filling and monitoring during daily activities. Compared to conventional UDS, AUM demonstrates higher sensitivity for detecting detrusor overactivity and underlying pathophysiology. However, it faces challenges like motion artifacts, catheter-related discomfort, and difficulty measuring continuous bladder volume. Emerging devices such as Urodynamics Monitor and UroSound offer more patient-friendly alternatives. These tools have the potential to improve diagnostic accuracy for bladder pressure and voiding metrics but remain limited and still require further validation and testing. Ultrasound-based modalities, including dynamic ultrasonography and shear wave elastography, provide real-time, noninvasive assessment of bladder structure and function. These modalities are promising but will require further development of standardized protocols. AI and machine learning models enhance diagnostic accuracy and reduce variability in UDS interpretation. Applications include detecting detrusor overactivity and distinguishing bladder outlet obstruction from detrusor underactivity. However, further validation is required for clinical adoption. Advances in AUM, wearable technologies, ultrasonography, and AI demonstrate potential for transforming UDS into a more accurate, patient-centered tool. Despite significant progress, challenges like technical complexity, standardization, and cost-effectiveness must be addressed to integrate these innovations into routine practice. Nonetheless, these technologies provide the possibility of a future of improved diagnosis and treatment of lower urinary tract dysfunction.

Pulmonary vascular intervention technology, with its minimally invasive and precise advantages, has been a groundbreaking advancement in the treatment of pulmonary vascular diseases. Techniques such as balloon pulmonary angioplasty (BPA), pulmonary artery stenting, and percutaneous pulmonary artery denervation (PADN) have significantly improved the prognoses for conditions such as chronic thromboembolic pulmonary hypertension (CTEPH), pulmonary artery stenosis, and pulmonary arterial hypertension (PAH). Although based on coronary intervention (PCI) techniques such as guidewire manipulation and balloon dilatation, pulmonary vascular interventions require specific modifications to address the unique characteristics of the pulmonary circulation, low pressure, thin-walled vessels, and complex branching, to mitigate risks of perforation and thrombosis. Future directions include the development of dedicated instruments, multi-modality imaging guidance, artificial intelligence-assisted procedures, and molecular interventional therapies. These innovations aim to establish an independent theoretical framework for pulmonary vascular interventions, facilitating their transition from "adjuvant therapies" to "core treatments" in clinical practice.

Radiomics and artificial intelligence (AI) are rapidly evolving, significantly transforming the field of medical imaging. Despite their growing adoption, these technologies remain challenging to approach due to their technical complexity. This review serves as a practical guide for early-career radiologists and researchers seeking to integrate radiomics into their studies. It provides practical insights for clinical and research applications, addressing common challenges, limitations, and future directions in the field. This work offers a structured overview of the essential steps in the radiomics workflow, focusing on concrete aspects of each step, including indicative and practical examples. It covers the main steps such as dataset definition, image acquisition and preprocessing, segmentation, feature extraction and selection, and AI model training and validation. Different methods to be considered are discussed, accompanied by summary diagrams. This review equips readers with the knowledge necessary to approach radiomics and AI in medical imaging from a hands-on research perspective.

Coronary artery disease is the main cause of mortality worldwide mandating early detection, appropriate treatment, and follow-up. Noninvasive cardiac imaging techniques allow detection of obstructive coronary heart disease by direct visualization of the arteries or myocardial blood flow reduction. These techniques have made remarkable progress since their introduction, achieving high diagnostic precision. This review aims at evaluating these noninvasive cardiac imaging techniques, rendering a thorough overview of diagnostic decision-making for detection of ischemia. We discuss the latest advances in the field such as computed tomography angiography, single-photon emission tomography, positron emission tomography, and cardiac magnetic resonance; their main advantages and disadvantages, their most appropriate use and prospects. For the review, we analyzed the literature from 2009 to 2024 on noninvasive cardiac imaging in the diagnosis of coronary artery disease. The review included the 78 publications considered most relevant, including landmark trials, review articles and guidelines. The progress in cardiac imaging is anticipated to overcome various limitations such as high costs, radiation exposure, artifacts, and differences in interpretation among observers. It is expected to lead to more automated scanning processes, and with the assistance of artificial intelligence-driven post-processing software, higher accuracy and reproducibility may be attained.

While deep learning models have demonstrated remarkable success in numerous domains, their black-box nature remains a significant limitation, especially in critical fields such as medical image analysis and inference. Existing explainability methods, such as SHAP, LIME, and Grad-CAM, are typically applied post hoc, adding computational overhead and sometimes producing inconsistent or ambiguous results. In this paper, we present the Deeply Explainable Artificial Neural Network (DxANN), a novel deep learning architecture that embeds explainability ante hoc, directly into the training process. Unlike conventional models that require external interpretation methods, DxANN is designed to produce per-sample, per-feature explanations as part of the forward pass. Built on a flow-based framework, it enables both accurate predictions and transparent decision-making, and is particularly well-suited for image-based tasks. While our focus is on medical imaging, the DxANN architecture is readily adaptable to other data modalities, including tabular and sequential data. DxANN marks a step forward toward intrinsically interpretable deep learning, offering a practical solution for applications where trust and accountability are essential.

Automatic lymph node segmentation is the cornerstone for advances in computer vision tasks for early detection and staging of cancer. Traditional segmentation methods are constrained by manual delineation and variability in operator proficiency, limiting their ability to achieve high accuracy. The introduction of deep learning technologies offers new possibilities for improving the accuracy of lymph node image analysis. This study evaluates the application of deep learning in lymph node segmentation and discusses the methodologies of various deep learning architectures such as convolutional neural networks, encoder-decoder networks, and transformers in analyzing medical imaging data across different modalities. Despite the advancements, it still confronts challenges like the shape diversity of lymph nodes, the scarcity of accurately labeled datasets, and the inadequate development of methods that are robust and generalizable across different imaging modalities. To the best of our knowledge, this is the first study that provides a comprehensive overview of the application of deep learning techniques in lymph node segmentation task. Furthermore, this study also explores potential future research directions, including multimodal fusion techniques, transfer learning, and the use of large-scale pre-trained models to overcome current limitations while enhancing cancer diagnosis and treatment planning strategies.