Magnetic resonance fingerprinting (MRF) enables quantitative MRI by allowing the simultaneous mapping of multiple tissue properties through innovative acquisition and computational methods. This review focuses on the application of MRF techniques to cerebral physiology, emphasizing advancements in vascular imaging and the integration of biophysical modeling. We discuss the principles of MRF, its adaptation to quantify hemodynamic and vascular parameters, and its potential to overcome challenges in mapping vascular-related parameters. The review categorizes MRF-based imaging approaches, including MRF-arterial spin labeling (MRF-ASL), MR vascular fingerprinting (MRvF), and vascular fluid dynamics-MRF (VFD-MRF), highlighting their technical implementations, accuracy, and clinical applications in conditions such as stroke, brain tumors, and cerebrovascular diseases. We also explore the role of machine learning in enhancing dictionary matching and reducing computational time for more accurate and reliable real-time parameter estimation. The challenges such as low signal-to-noise ratios and computational demands are addressed through tailored sequence designs, noise-resilient dictionaries, and deep learning approaches. This comprehensive review provides a detailed technical framework for advancing the role of MRF in assessing cerebral physiology and its clinical translation.

To compare proprietary (GPT-4o, Gemini 1.5 Pro) and open-source (Llama 3.1 70B, Llama 3.1 405B) large language models (LLMs) for extracting clinically relevant variables from transarterial chemoembolization (TACE) reports in patients with hepatocellular carcinoma (HCC). Retrospective analysis of 556 anonymized longitudinal TACE-related reports (radiology, interventional procedure, and clinical follow-up) from 50 patients with HCC treated between 2012 and 2024 at a single tertiary center was carried out. Models extracted predefined binary variables (e.g., modified Response Evaluation Criteria in Solid Tumors [mRECIST] tumor response, alpha-fetoprotein [AFP] dynamics, Barcelona Clinic Liver Cancer [BCLC] stage) and ordinal variables (e.g., liver segment involvement, vascular invasion, follow-up assessment) using a standardized system prompt and output template. Model performance was assessed by accuracy, ordinal scores, and longitudinal error rates using mixed-effects regression with patient-level random intercepts. Proprietary models outperformed open-source models. GPT-4o and Gemini achieved the highest mean accuracies for binary variables (0.87 ± 0.21 and 0.85 ± 0.16) and ordinal variables (4.15/5 and 4.10/5), significantly exceeding both Llama models (p < 0.05). GPT-4o showed the lowest longitudinal error rate for binary variables (0.01 vs 0.09-0.21 for the other models), indicating greater robustness over time. All models showed poor performance in vascular invasion detection and follow-up assessment. Proprietary LLMs can accurately extract most key TACE-related variables from routine clinical reports and may support decision-making in interventional oncology; however, all models showed poor performance in vascular invasion detection and follow-up assessment, so expert human oversight remains essential.

PD-1 blockade therapy has emerged as a valuable treatment option for advanced hepatocellular carcinoma (HCC), but its therapeutic response and overall efficacy vary among patients. This study develops an automated framework for predicting response to PD-1 blockade with enhanced accuracy. A comprehensive two-phase investigation was conducted, comprising a retrospective multicenter cohort (n = 793) for model development and a prospective cohort (n = 60) for validation. We established an integrated predictive framework combining ultrasound radiomics with clinical indicators. Model performance was evaluated by ROC analyses, focusing on the area under the curve (AUC). Molecular analyses of liver tissues were performed to explore mechanisms underlying treatment response. The ultrasound radiomics model achieved AUCs of 0.714 (training) and 0.617 (validation). The ensemble model, integrating both modalities, demonstrated superior predictive capability, with AUCs of 0.743 (training) and 0.641 (validation). The ensemble learning model, integrating both imaging and clinical modalities, exhibited superior predictive capability, attaining an AUC of 0.743 in the training cohort and 0.641 in the validation cohort. The ensemble model demonstrated exceptional clinical utility in predicting pathological necrosis following PD-1 blockade before hepatectomy, achieving an AUC of 0.692. Notably, it exhibited strong clinical utility in predicting pathological necrosis post-therapy, achieving an AUC of 0.692. Subsequent KEGG/GO analyses implicated key genes in necroptosis and programmed cell death pathways. The proposed ultrasound-based ensemble model offers a non-invasive, reproducible method to predict PD-1 blockade response in HCC, effectively integrating imaging and clinical data to enhance predictive accuracy and reveal potential molecular mediators of therapeutic efficacy. We developed an advanced automated predictive model that synergistically integrates ultrasound imaging with clinical indicators through ensemble learning methodology. This innovative model employs state-of-the-art deep learning architectures, specifically optimized convolutional neural networks, to accurately predict therapeutic response to PD-1 blockade in patients with unresectable hepatocellular carcinoma.

Hematoma expansion (HE) is a critical therapeutic target in spontaneous intracerebral hemorrhage (sICH), yet its reliable early identification remains challenging. We developed an automated pipeline for HE prediction using non-contrast computed tomography from 2020 patients across five centers. The modular framework comprised automated segmentation, synthetic data augmentation, and Vision Transformer (ViT)-based classification. High-quality hematoma masks were generated by the full-scale U-Mamba model, identified as the optimal architecture through comprehensive benchmarking. Two augmented training sets were constructed using synthetic HE images from the Diffusion-UKAN model: UKAN-Balanced (HE: NHE = 1:1) and UKAN-Semibalanced (HE: NHE = 1:2). The ViT-1:2 classifier, trained on the UKAN-Semibalanced dataset, achieved a training set AUC of 0.815 and demonstrated robust cross-institutional generalization with external validation AUCs of 0.793 and 0.781 on two independent datasets. These findings suggest that the proposed modular approach provides a promising front-line tool for rapid HE risk stratification in acute care settings, with potentially improving clinical decision-making in sICH management.

Slice-based models have been widely applied in Alzheimer's disease (AD) identification tasks due to their reduced parameter count and fast inference speed. However, existing slice-based models require additional slice extraction steps and cannot achieve an end-to-end process from MRI to diagnostic results. Moreover, they often rely on Transformer architectures to model inter-slice dependencies, which suffer from quadratic computational complexity. To address these limitations, we propose ViViMZheimer, a slice-based end-to-end model that directly processes 3D MRI data and generates diagnostic predictions. ViViMZheimer integrates a ViViT-inspired spatial encoder with a Mamba-based temporal modeling mechanism, maintaining linear computational complexity while effectively capturing inter-slice dependencies along three spatial orientations. Additionally, a lightweight spatial attention module emphasizes lesion-relevant brain regions, and a gated bottleneck convolution refines key features in later stages of the model. We evaluated ViViMZheimer on the ADNI dataset, where it achieved accuracies of 98.17%, 82.21%, and 83.15% in distinguishing AD vs. cognitively normal (CN), AD vs. mild cognitive impairment (MCI), and CN vs. MCI, respectively. These results demonstrate that ViViMZheimer provides an effective and computationally efficient solution for automated Alzheimer's disease diagnosis from 3D MRI scans.

To evaluate the effect of combining real-time deep learning (DL)-based guiding and automated measurements of left ventricular (LV) volumetric measurements and strain. Patients (n=47) with mixed cardiac pathology were examined by two sonographers and one reference cardiologist. A real-time DL guiding tool to avoid LV foreshortening was used by one sonographer only per patient. Automated DL-based measurements from the sonographer using the guiding tool were paired with automated measurements from the reference cardiologist (artificial intelligence (AI)-assisted echocardiography), while manual measurements from the sonographer not using the guiding tool were paired with manual measurements from the reference cardiologist (standard echocardiography). The variability of LV EDV, LV ESV, ejection fraction (LV EF) and global longitudinal strain (LV GLS) was compared for standard echocardiography versus AI-assisted echocardiography. Coefficients of variation were lower for AI-assisted echocardiography compared with standard echocardiography (6% vs 15% for LV EDV (p<0.001), 10% vs 19% for ESV (p<0.001) and 7% vs 11% for GLS (p=0.047), respectively). For LV EF, the coefficients of variation were similar across groups (8% vs 9%, p=0.503, respectively). In exploratory analyses, automated measurements alone (all p≤0.002) but not the guiding tool (all ≥0.199) explained the improved variability for LV EDV, ESV and GLS. AI-assisted echocardiography combining DL-based real-time guiding and automated measurements significantly reduced the variability of LV EDV, ESV and GLS when compared to standard echocardiography. Among experienced operators, automated measurements were more beneficial than real-time guiding. ClinicalTrials.gov, ID: NCT04580095.

To evaluate the impact of an artificial intelligence (AI)-assisted computer-aided detection (CAD) system on the diagnostic accuracy and confidence in chest radiograph interpretation among nonthoracic radiologists and radiology residents with varying levels of experience. In this retrospective multiple-reader, multiple-case (MRMC) study, 400 chest radiographs (100 each for pulmonary nodules, pleural effusion, pneumothorax, and controls) were independently interpreted by 12 readers (two nonthoracic radiologists, four senior residents, and six junior residents). Readings were conducted under CAD-assisted and unassisted conditions, with a 30-day washout period. Readers assigned confidence scores (0-100) to their diagnosis. Diagnostic performance was evaluated using the area under the curve (AUC), sensitivity, and specificity, while reader confidence was assessed by the proportion of high-confidence ratings among correctly interpreted cases. The AI-assisted CAD system improved diagnostic performance across all abnormalities, with significant gains for pulmonary nodules (AUC: 0.781 → 0.854; P < 0.001) and pleural effusion (0.896 → 0.948; P < 0.001). The sensitivity increased by 7.2% for effusion, while the specificity for nodules improved markedly by 15.7%. Among all the readers, junior residents showed the greatest gains, especially for nodules, where the CAD closed their baseline AUC gap (originally -7.3%, P = 0.006) relative to nonthoracic radiologists. Reader confidence also increased significantly with the CAD, particularly for nodules (+15.2 %; P < 0.001). The AI-assisted CAD system significantly enhanced diagnostic accuracy and reader confidence in chest radiograph interpretation, especially for junior radiology residents. This approach may bridge experience-related diagnostic gaps and support clinical decision-making, particularly in institutions lacking thoracic radiologists.

To develop predictive models for fetal growth restriction (FGR) with and without the inclusion of OxNNet-derived first-trimester placental volume (FTPV), thereby evaluating the contribution of FTPV to these models and the extent to which FTPV percentile is associated with subsequent FGR. This study utilized data from the First-trimester Placental Ultrasound (FirstPLUS) study, a longitudinal observational cohort study conducted at King's College Hospital NHS Foundation Trust, London, UK, between March and November 2022. Participants underwent routine ultrasound assessment between 11 + 2 and 14 + 1 weeks' gestation, in addition to three-dimensional placental sonography. The OxNNet toolkit was used for automated placental segmentation and volume calculation. Multivariable logistic regression models were developed to predict FGR, incorporating maternal factors, first-trimester biomarkers (serum pregnancy-associated plasma protein-A, mean arterial blood pressure and uterine artery pulsatility index) and FTPV. The final cohort comprised 3500 pregnancies, of which 250 (7.1%) developed FGR. Low FTPV was found to be a risk factor for FGR, with an odds ratio of 1.736 (95% CI, 1.499-2.015) per unit decrease in FTPV Z-score. Incorporating FTPV into the predictive model based on maternal factors and biomarkers significantly increased the area under the receiver-operating-characteristics curve (AUC) for predicting all cases of FGR, from 0.78 (95% CI, 0.75-0.81) to 0.79 (95% CI, 0.76-0.82) (P = 0.005). Subgroup analysis of normotensive and hypertensive cases demonstrated a statistically significant effect size for the prediction of FGR by FTPV Z-score in both groups. The addition of FTPV to the model based on maternal factors and biomarkers for the prediction of normotensive FGR increased the AUC from 0.77 (95% CI, 0.74-0.80) to 0.78 (95% CI, 0.75-0.81) (P = 0.01). For preterm FGR, the AUC was 0.85 (95% CI, 0.78-0.92) with FTPV and 0.85 (95% CI, 0.79-0.92) without (P = 0.93); the absence of a significant difference may be due to a lack of power. FTPV Z-score is a predictor of FGR. Integrating FTPV into predictive models significantly enhanced the discriminative ability for all cases of FGR, as well as for the subgroup of normotensive FGR. © 2025 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

To address Croatia's high lung cancer mortality and late-stage diagnoses, the Ministry of Health initiated a multidisciplinary effort to design a national lung cancer screening program. Lung cancer remains one of the leading causes of cancer-related mortality both globally and in Croatia. In 2021 alone, Croatia recorded over 3300 new cases of lung cancer and more than 2800 associated deaths, indicating a high mortality burden. In response to this public health concern, the Ministry of Health has established a multidisciplinary Lung Cancer Screening Working Group, tasked with developing a national screening approach. The Program incorporates several innovative elements, including the application of modified International Early Lung Cancer Action Program (I-ELCAP) criteria for nodule management, volumetric analysis assessed by artificial intelligence, complete digitalization, smoking cessation, and nationwide deployment to ensure equitable access. From October 2020 to August 2025, over 50,000 participants were screened, resulting in more than 70,000 LDCT scans performed. The cohort includes 54% male and 46% female participants, with an average age of 62 years. Among these participants, 4.5% had positive results, which required further follow-up. The Croatian National Lung Cancer Screening Program offers unique features as it has been comprehensively incorporated into the existing healthcare infrastructure and is fully reimbursed. A key aspect of the program is the important role assigned to general practitioners (GPs), who are responsible for identifying and referring individuals at high risk for lung cancer. Question No European Union country has implemented a national lung cancer screening program despite evidence from previous trials showing significant mortality reduction. Findings Croatia successfully launched a fully integrated national lung cancer screening program using LDCT, AI-assisted volumetric analysis, modified I-ELCAP criteria, and GP-centered recruitment. Clinical relevance The Croatian model demonstrates the feasibility of national lung cancer screening within a European public healthcare system with full reimbursement, providing a replicable framework for other EU countries implementing lung cancer screening programs.

Pediatric cardiovascular imaging plays an important role in the diagnosis, monitoring, and management of congenital and acquired heart diseases. Although echocardiography remains the most widely used modality in pediatric cardiology, cross-sectional imaging techniques such as cardiac magnetic resonance imaging (MRI) and computed tomography (CT) provide complementary anatomic and functional information. However, time-consuming diagnostic processes and patient-specific characteristics remain major limitations to early and precise diagnosis, as well as optimal clinical outcomes. With technological advancements, artificial intelligence (AI) has been increasingly integrated into cardiovascular magnetic resonance imaging (MRI) and computed tomography (CT) to enhance image acquisition, segmentation, interpretation, and diagnosis, and to facilitate predictive modeling of clinical outcomes. This review summarizes current and emerging AI applications in pediatric cardiovascular MRI and CT, emphasizing workflow optimization, diagnostic automation, and quantitative analysis. Emerging frontiers include multimodal data integration for risk stratification, clinical decision-making support, digital twin models, three-dimensional virtual modeling, and the application of computational fluid dynamics, as well as the potential of AI to improve access to care in low-resource settings.