To measure the association between antithrombotic (AT) medications (anticoagulant and antiplatelet) and risk for traumatic intracranial hemorrhage (ICH) in older adults with a mild traumatic brain injury (mTBI). We conducted a retrospective multicenter study across 103 emergency departments affiliated with a teleradiology company dedicated to emergency imaging between 2020 and 2022. Older adults (≥65 years old) with mTBI, with a head computed tomography scan, were included. Natural language processing models were used to label-free texts of emergency physician forms and radiology reports; and a multivariable logistic regression model to measure the association between AT medications and occurrence of ICH. A total of 5948 patients [median age 84.6 (74.3-89.1) years, 58.1% females] were included, of whom 781 (13.1%) had an ICH. Among them, 3177 (53.4%) patients were treated with at least one AT agent. No AT medication was associated with a higher risk for ICH: antiplatelet odds ratio 0.98 95% confidence interval (0.81-1.18), direct oral anticoagulant 0.82 (0.60-1.09), and vitamin K antagonist 0.66 (0.37-1.10). Conversely, a high-level fall [1.68 (1.15-2.4)], a Glasgow coma scale of 14 [1.83 (1.22-2.68)], a cutaneous head impact [1.5 (1.17-1.92)], vomiting [1.59 (1.18-2.14)], amnesia [1.35 (1.02-1.79)], a suspected skull vault fracture [9.3 (14.2-26.5)] or of facial bones fracture [1.34 (1.02-1.75)] were associated with a higher risk for ICH. This study found no association between AT medications and an increased risk of ICH among older patients with mTBI suggesting that routine neuroimaging in this population may offer limited benefit and that additional variables should be considered in the imaging decision.

The aim of this commentary review was to summarize the main research evidences on radiation exposure and to underline the best clinical and radiological practices to limit radiation exposure in ICU patients. Radiological imaging is essential for management of patients in the ICU despite the risk of ionizing radiation exposure in monitoring critically ill patients, especially in those with prolonged hospitalization. In optimizing radiation exposure reduction for ICU patients, multiple parties and professionals must be considered, including hospital management, clinicians, radiographers, and radiologists. Modified diagnostic reference levels for ICU patients, based on UK guidance, may be proposed, especially considering the frequent repetition of x-ray diagnostic procedures in ICU patients. Best practices may reduce radiation exposure in ICU patients with particular emphasis on justification and radiation exposure optimization in conventional radiology, interventional radiology and fluoroscopy, CT, and nuclear medicine. CT contributes most predominately to radiation exposure in ICU patients. Low-dose (<1 mSv in effective dose) or even ultra-low-dose CT protocols, iterative reconstruction algorithms, and artificial intelligence-based innovative dose-reduction strategies could reduce radiation exposure and related oncogenic risks.

Tuberculosis (TB) remains a significant global health challenge, as current diagnostic methods are often resource-intensive, time-consuming, and inaccessible in many high-burden communities, necessitating more efficient and accurate diagnostic methods to improve early detection and treatment outcomes. This study aimed to evaluate the performance of 6 convolutional neural network architectures-Visual Geometry Group-16 (VGG16), VGG19, Residual Network-50 (ResNet50), ResNet101, ResNet152, and Inception-ResNet-V2-in classifying chest x-ray (CXR) images as either normal or TB-positive. The impact of data augmentation on model performance, training times, and parameter counts was also assessed. The dataset of 4200 CXR images, comprising 700 labeled as TB-positive and 3500 as normal cases, was used to train and test the models. Evaluation metrics included accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve. The computational efficiency of each model was analyzed by comparing training times and parameter counts. VGG16 outperformed the other architectures, achieving an accuracy of 99.4%, precision of 97.9%, recall of 98.6%, F1-score of 98.3%, and area under the receiver operating characteristic curve of 98.25%. This superior performance is significant because it demonstrates that a simpler model can deliver exceptional diagnostic accuracy while requiring fewer computational resources. Surprisingly, data augmentation did not improve performance, suggesting that the original dataset's diversity was sufficient. Models with large numbers of parameters, such as ResNet152 and Inception-ResNet-V2, required longer training times without yielding proportionally better performance. Simpler models like VGG16 offer a favorable balance between diagnostic accuracy and computational efficiency for TB detection in CXR images. These findings highlight the need to tailor model selection to task-specific requirements, providing valuable insights for future research and clinical implementations in medical image classification.

Diagnostic delay remains a critical challenge in axial spondyloarthritis (axSpA). This review highlights key clinical and imaging research from 2024 that addresses this persistent issue, with a focus on the evolving roles of MRI, artificial intelligence (AI), and updated Canadian management recommendations. Multiple studies published in 2024 emphasized the continued problem of diagnostic delay in axSpA. Studies support the continued use of sacroiliac joint MRI as a central diagnostic tool for axSpA, particularly in patients with chronic back pain and associated conditions like uveitis, psoriasis (PsO), or inflammatory bowel disease. AI-based tools for interpreting sacroiliac joint MRIs demonstrated moderate agreement with expert assessments, offering a potential solution to variability and limited access to expert musculoskeletal radiology. These innovations may support earlier diagnosis and reduce misclassification. Innovative models of care, including patient-initiated telemedicine visits, reduced in-person visit frequency without compromising clinical outcomes in patients with stable axSpA. Updated Canadian treatment guidelines introduced more robust data on Janus kinase (JAK) inhibitors and offered stronger support for tapering biologics in patients with sustained low disease activity or remission, while advising against abrupt discontinuation. This clinical and imaging year in review covers challenges and innovations in axSpA, emphasizing the need for early access to care and the development of tools to support prompt diagnosis and sustained continuity of care.

MR neurography sequences provide excellent nerve-to-background soft tissue contrast, whereas a zero echo time (ZTE) MRI sequence provides cortical bone contrast. By demonstrating the spatial relationship between nerves and bones, a combination of rendered three-dimensional (3D) MR neurography and ZTE sequences provides a roadmap for clinical decision-making, particularly for surgical intervention. In this article, the authors describe the method for fused rendering of peripheral nerve and bone by combining nerve and bone structures from 3D MR neurography and 3D ZTE MRI, respectively. The described method includes scanning acquisition, postprocessing that entails deep learning-based reconstruction techniques, and rendering techniques. Representative case examples demonstrate the steps and clinical use of these techniques. Challenges in nerve and bone rendering are also discussed.

Interstitial lung disease (ILD) diagnosis is complex, continuously evolving, and increasingly reliant on thin-section chest CT. Multidisciplinary discussion aided by a thorough radiologic review can achieve a high-confidence diagnosis of ILD in the majority of patients and is currently the reference standard for ILD diagnosis. CT also allows the early recognition of interstitial lung abnormalities, possibly reflective of unsuspected ILD and progressive in a substantial proportion of patients. Beyond diagnosis, CT has also become essential for ILD prognostication and follow-up, aiding the identification of fibrotic and progressive forms. The presence of fibrosis is a critical determinant of prognosis, particularly when typical features of usual interstitial pneumonia (UIP) are identified. The UIP-centric imaging approach emphasized in this review is justified by the prognostic significance of UIP, the prevalence of UIP in idiopathic pulmonary fibrosis, and its strong radiologic-pathologic correlation. In nonidiopathic pulmonary fibrosis ILD, progressive pulmonary fibrosis carries clinically significant prognostic and therapeutic implications. With growing evidence and the emergence of novel ILD-related concepts, recent updates of several imaging guidelines aim to optimize the approach to ILD. Artificial intelligence tools are promising adjuncts to the qualitative CT assessment and will likely augment the role of CT in the ILD realm.

Lung diseases represent one of the most prevalent health challenges globally, necessitating accurate diagnosis to improve patient outcomes. This work presents a novel deep learning-aided lung disease classification framework comprising three key phases: image acquisition, segmentation, and classification. Initially, chest X-ray images are taken from standard datasets. The lung regions are segmented using an Adaptive Recurrent Residual U-Net (AR2-UNet), whose parameters are optimised using Enhanced Pufferfish Optimisation Algorithm (EPOA) to enhance segmentation accuracy. The segmented images are processed using "Attention-based Densenet with Long Short Term Memory(ADNet-LSTM)" for robust categorisation. Investigational results demonstrate that the proposed model achieves the highest classification accuracy of 93.92%, significantly outperforming several baseline models including ResNet with 90.77%, Inception with 89.55%, DenseNet with 89.66%, and "Long Short Term Memory (LSTM)" with 91.79%. Thus, the proposed framework offers a dependable and efficient solution for lung disease detection, supporting clinicians in early and accurate diagnosis.

AI-driven scan time reduction is rapidly transforming medical imaging with benefits such as improved patient comfort and enhanced efficiency. A Dual Contrastive Learning Generative Adversarial Network (DCLGAN) was developed to predict full-time PET scans from shorter, noisier scans, improving challenges in imaging patients with movement disorders. 18F FP-CIT PET/CT data from 391 patients with suspected Parkinsonism were used [250 training/validation, 141 testing (hospital A)]. Ground truth (GT) images were reconstructed from 15-minute scans, while denoised images (DIs) were generated from 1-, 3-, 5-, and 10-minute scans. Image quality was assessed using normalized root mean square error (NRMSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), visual analysis, and clinical metrics like BPND and ISR for diagnosis of non-neurodegenerative Parkinson disease (NPD), idiopathic PD (IPD), and atypical PD (APD). External validation used data from 2 hospitals with different scanners (hospital B: 1-, 3-, 5-, and 10-min; hospital C: 1-, 3-, and 5-min). In addition, motion artifact reduction was evaluated using the Dice similarity coefficient (DSC). In hospital A, NRMSE, PSNR, and SSIM values improved with scan duration, with the 5-minute DIs achieving optimal quality (NRMSE 0.008, PSNR 42.13, SSIM 0.98). Visual analysis rated DIs from scans ≥3 minutes as adequate or higher. The mean BPND differences (95% CI) for each DIs were 0.19 (-0.01, 0.40), 0.11 (-0.02, 0.24), 0.08 (-0.03, 0.18), and 0.01 (-0.06, 0.07), with the CIs significantly decreasing. ISRs with the highest effect sizes for differentiating NPD, IPD, and APD (PP/AP, PP/VS, PC/VP) remained stable post-denoising. External validation showed 10-minute DIs (hospital B) and 1-minute DIs (hospital C) reached benchmarks of hospital A's image quality metrics, with similar trends in visual analysis and BPND CIs. Furthermore, motion artifact correction in 9 patients yielded DSC improvements from 0.89 to 0.95 in striatal regions. The DL-model is capable of generating high-quality 18F FP-CIT PET images from shorter scans to enhance patient comfort, minimize motion artifacts, and maintain diagnostic precision. Furthermore, our study plays an important role in providing insights into how imaging quality assessment metrics can be used to determine the appropriate scan duration for different scanners with varying sensitivities.

Advancements in coronary computed tomography angiography (CCTA) facilitated the transition from traditional histological approaches to comprehensive plaque burden assessment. Recent updates in the European Society of Cardiology (ESC) guidelines emphasize CCTA's role in managing chronic coronary syndrome by enabling detailed monitoring of atherosclerotic plaque progression. Limitations of conventional CCTA, such as spatial resolution challenges in accurately characterizing plaque components like thin-cap fibroatheromas and necrotic lipid-rich cores, are addressed with photon-counting detector CT (PCD-CT) technology. PCD-CT offers enhanced spatial resolution and spectral imaging, improving the detection and characterization of high-risk plaque features while reducing artifacts. The integration of artificial intelligence (AI) in plaque analysis enhances diagnostic accuracy through automated plaque characterization and radiomics. These technological advancements support a comprehensive approach to plaque assessment, incorporating hemodynamic evaluations, morphological metrics, and AI-driven analysis, thereby enabling personalized patient care and improved prediction of acute clinical events.