To assess the image quality and radiation dose between reduced-dose CT with deep learning reconstruction (DLR) using SilverBeam filter and standard dose with iterative reconstruction (IR) in abdominopelvic CT. In total, 182 patients (mean age ± standard deviation, 63 ± 14 years; 100 men) were included. Standard-dose scanning was performed with a tube voltage of 100 kVp, automatic tube current modulation, and IR reconstruction, whereas reduced-dose scanning was performed with a tube voltage of 120 kVp, a SilverBeam filter, and DLR. Additionally, a contrast-enhanced (CE)-boost image was obtained for reduced-dose scanning. Radiation dose, objective, and subjective image analyses were performed in each body mass index (BMI) category. The radiation dose for SilverBeam with DLR was significantly lower than that of standard dose with IR, with an average reduction in the effective dose of 59.0% (1.87 vs. 4.57 mSv). Standard dose with IR (10.59 ± 1.75) and SilverBeam with DLR (10.60 ± 1.08) showed no significant difference in image noise (p = 0.99). In the obese group (BMI > 25 kg/m²), there were no significant differences in SNRs of the liver, pancreas, and spleen between standard dose with IR and SilverBeam with DLR. SilverBeam with DLR + CE-boost demonstrated significantly better SNRs and CNRs, compared with standard dose with IR and SilverBeam with DLR. DLR combined with silver filter is effective for routine abdominopelvic CT, achieving a clearly reduced radiation dose while providing image quality that is non-inferior to standard dose with IR.

Diabetes mellitus (DM) is a serious global health concern that poses a significant threat to human life. Beyond its direct impact, diabetes substantially increases the risk of developing severe complications such as hypertension, cardiovascular disease, and musculoskeletal disorders like arthritis and osteoporosis. The field of diabetes classification has advanced significantly with the use of diverse data modalities and sophisticated tools to identify individuals or groups as diabetic. But the task of predicting diabetes prior to its onset, particularly through the use of longitudinal multi-modal data, remains relatively underexplored. To better understand the risk factors associated with diabetes development among Qatari adults, this longitudinal research aims to investigate dual-energy X-ray absorptiometry (DXA)-derived whole-body and regional bone composition measures as potential predictors of diabetes onset. We proposed a case-control retrospective study, with a total of 1,382 participants contains 725 male participants (cases: 146, control: 579) and 657 female participants (case: 133, control: 524). We excluded participants with incomplete data points. To handle class imbalance, we augmented our data using Synthetic Minority Over-sampling Technique (SMOTE) and SMOTEENN (SMOTE with Edited Nearest Neighbors), and to further investigated the association between bones data features and diabetes status, we employed ANOVA analytical method. For diabetes onset prediction, we employed both conventional and deep learning (DL) models to predict risk factors associated with diabetes in Qatari adults. We used SHAP and probabilistic methods to investigate the association of identified risk factors with diabetes. During experimental analysis, we found that bone mineral density (BMD), bone mineral contents (BMC) in the hip, femoral neck, troch area, and lumbar spine showed an upward trend in diabetic patients with [Formula: see text]. Meanwhile, we found that patients with abnormal glucose metabolism had increased wards BMD and BMC with low Z-score compared to healthy participants. Consequently, it shows that the diabetic group has superior bone health than the control group in the cohort, because they exhibit higher BMD, muscle mass, and bone area across most body regions. Moreover, in the age group distribution analysis, we found that the diabetes prediction rate was higher among healthy participants in the younger age group 20-40 years. But as the age range increased, the model predictions became more accurate for diabetic participants, especially in the older age group 56-69 years. It is also observed that male participants demonstrated a higher susceptibility to diabetes onset compared to female participants. Shallow models outperformed the DL models by presenting improved accuracy (91.08%), AUROC (96%), and recall values (91%). This pivotal approach utilizing DXA scans highlights significant potential for the rapid and minimally invasive early detection of diabetes.

Accurate segmentation of the liver parenchyma, portal veins, hepatic veins, and lesions from MRI is important for hepatic disease monitoring and treatment. Multi-phase contrast enhanced imaging is superior in distinguishing hepatic structures compared to single-phase approaches, but automated approaches for detailed segmentation of hepatic structures are lacking. This study evaluates deep learning architectures for segmenting liver structures from multi-phase Gd-EOB-DTPA-enhanced T1-weighted VIBE MRI scans. We utilized 458 T1-weighted VIBE scans of pathological livers, with 78 manually labeled for liver parenchyma, hepatic and portal veins, aorta, lesions, and ascites. An additional dataset of 47 labeled subjects was used for cross-scanner evaluation. Three models were evaluated using nested cross-validation: the conventional nnU-Net, the ResEnc nnU-Net, and the Swin UNETR. The late arterial phase was identified as the optimal fixed phase for co-registration. Both nnU-Net variants outperformed Swin UNETR across most tasks. The conventional nnU-Net achieved the highest segmentation performance for liver parenchyma (DSC: 0.97; 95% CI 0.97, 0.98), portal vein (DSC: 0.83; 95% CI 0.80, 0.87), and hepatic vein (DSC: 0.78; 95% CI 0.77, 0.80). Lesion and ascites segmentation proved challenging for all models, with the conventional nnU-Net performing best. This study demonstrates the effectiveness of deep learning, particularly nnU-Net variants, for detailed liver structure segmentation from multi-phase MRI. The developed models and preprocessing pipeline offer potential for improved liver disease assessment and surgical planning in clinical practice.

Accurate fetal growth evaluation is crucial for monitoring fetal health, with crown-rump length (CRL) being the gold standard for estimating gestational age and assessing growth during the first trimester. To enhance CRL evaluation accuracy and efficiency, we developed an artificial intelligence (AI)-based model (3DCRL-Net) using the 3D U-Net architecture for automatic landmark detection to achieve CRL plane localization and measurement in 3D ultrasound. We then compared its performance to that of experienced radiologists using both 2D and 3D ultrasound for fetal growth assessment. This prospective consecutive study collected fetal data from 1,326 ultrasound screenings conducted at 11-14 weeks of gestation (June 2021 to June 2023). Three experienced radiologists performed fetal screening using 2D video (2D-RAD) and 3D volume (3D-RAD) to obtain the CRL plane and measurement. The 3DCRL-Net model automatically outputs the landmark position, CRL plane localization and measurement. Three specialists audited the planes achieved by radiologists and 3DCRL-Net as standard or non-standard. The performance of CRL landmark detection, plane localization, measurement and time efficiency was evaluated in the internal testing dataset, comparing results with 3D-RAD. In the external dataset, CRL plane localization, measurement accuracy, and time efficiency were compared among the three groups. The internal dataset consisted of 126 cases in the testing set (training: validation: testing = 8:1:1), and the external dataset included 245 cases. On the internal testing set, 3DCRL-Net achieved a mean absolute distance error of 1.81 mm for the nine landmarks, higher accuracy in standard plane localization compared to 3D-RAD (91.27% vs. 80.16%), and strong consistency in CRL measurements (mean absolute error (MAE): 1.26 mm; mean difference: 0.37 mm, P = 0.70). The average time required per fetal case was 2.02 s for 3DCRL-Net versus 2 min for 3D-RAD (P < 0.001). On the external testing dataset, 3DCRL-Net demonstrated high performance in standard plane localization, achieving results comparable to 2D-RAD and 3D-RAD (accuracy: 91.43% vs. 93.06% vs. 86.12%), with strong consistency in CRL measurements, compared to 2D-RAD, which showed an MAE of 1.58 mm and a mean difference of 1.12 mm (P = 0.25). For 2D-RAD vs. 3DCRL-Net, the Pearson correlation and R² were 0.96 and 0.93, respectively, with an MAE of 0.11 ± 0.12 weeks. The average time required per fetal case was 5 s for 3DCRL-Net, compared to 2 min for 3D-RAD and 35 s for 2D-RAD (P < 0.001). The 3DCRL-Net model provides a rapid, accurate, and fully automated solution for CRL measurement in 3D ultrasound, achieving expert-level performance and significantly improving the efficiency and reliability of first-trimester fetal growth assessment.

The accurate early-stage diagnosis of gallbladder cancer (GBC) is regarded as one of the major challenges in the field of oncology. However, few studies have focused on the comprehensive classification of GBC based on multiple modalities. This study aims to develop a comprehensive diagnostic framework for GBC based on both imaging and non-imaging medical data. This retrospective study reviewed 298 clinical patients with gallbladder disease or volunteers from two devices. A novel end-to-end interpretable diagnostic framework for GBC is proposed to handle multiple medical modalities, including CT imaging, demographics, tumor markers, coagulation function tests, and routine blood tests. To achieve better feature extraction and fusion of the imaging modality, a novel global-hybrid-local network, namely GHL-Net, has also been developed. The ensemble learning strategy is employed to fuse multi-modality data and obtain the final classification result. In addition, two interpretable methods are applied to help clinicians understand the model-based decisions. Model performance was evaluated through accuracy, precision, specificity, sensitivity, F1-score, area under the curve (AUC), and matthews correlation coefficient (MCC). In both binary and multi-class classification scenarios, the proposed method showed better performance compared to other comparison methods in both datasets. Especially in the binary classification scenario, the proposed method achieved the highest accuracy, sensitivity, specificity, precision, F1-score, ROC-AUC, PR-AUC, and MCC of 95.24%, 93.55%, 96.87%, 96.67%, 95.08%, 0.9591, 0.9636, and 0.9051, respectively. The visualization results obtained based on the interpretable methods also demonstrated a high clinical relevance of the intermediate decision-making processes. Ablation studies then provided an in-depth understanding of our methodology. The machine learning-based framework can effectively improve the accuracy of GBC diagnosis and is expected to have a more significant impact in other cancer diagnosis scenarios.

In this research, we predict the output signal generated by iron oxide-based nanoparticles in Magnetic Resonance Imaging (MRI) using the physical properties of the nanoparticles and the MRI machine. The parameters considered include the size of the magnetic core of the nanoparticles, their magnetic saturation (Ms), the concentration of the nanoparticles (C), and the magnetic field (MF) strength of the MRI device. These parameters serve as input variables for the model, while the relaxation rate R₂ (s^-1) is taken as the output variable. To develop this model, we employed a machine learning approach based on a neural network known as SA-LOOCV-GRBF (SLG). In this study, we compared two different random selection patterns: SLG disperse random selection (DSLG) and SLG parallel random selection (PSLG). The sensitivity to neuron number in the hidden layers for DSLG was more pronounced compared to the PSLG pattern, and the mean square error (MSE) was calculated for this evaluation. It appears that the PSLG method demonstrated strong performance while maintaining less sensitivity to increasing neuron numbers. Consequently, the new pattern, PSLG, was selected for predicting MRI behavior.

Endovascular aortic aneurysm repair (EVAR) requires long-term surveillance to detect and treat postoperative complications. However, prediction models to optimise follow-up strategies are still lacking. The primary objective of this study is to develop predictive models of post-operative outcomes following elective EVAR using Artificial Intelligence (AI)-driven analysis. The secondary objective is to investigate morphological aortic changes following EVAR. This international, multicentre, observational study will retrospectively include 500 patients who underwent elective EVAR. Primary outcomes are EVAR postoperative complications including deaths, re-interventions, endoleaks, limb occlusion and stent-graft migration occurring within 1 year and at mid-term follow-up (1 to 3 years). Secondary outcomes are aortic anatomical changes. Morphological changes following EVAR will be analysed and compared based on preoperative and postoperative CT angiography (CTA) images (within 1 to 12 months, and at the last follow-up) using the AI-based software PRAEVAorta 2 (Nurea). Deep learning algorithms will be applied to stratify the risk of postoperative outcomes into low or high-risk categories. The training and testing dataset will be respectively composed of 70% and 30% of the cohort. The study protocol is designed to ensure that the sponsor and the investigators comply with the principles of the Declaration of Helsinki and the ICH E6 good clinical practice guideline. The study has been approved by the ethics committee of the University Hospital of Patras (Patras, Greece) under the number 492/05.12.2024. The results of the study will be presented at relevant national and international conferences and submitted for publication to peer-review journals.

Large language models (LLMs) hold great promise for optimizing and supporting radiology workflows amidst rising workloads. This review examines potential applications in daily radiology practice, as well as remaining challenges and potential solutions.Presentation of potential applications and challenges, illustrated with practical examples and concrete optimization suggestions.LLM-based assistance systems have potential applications in almost all language-based process steps of the radiological workflow. Significant progress has been made in areas such as report generation, particularly with retrieval-augmented generation (RAG) and multi-step reasoning approaches. However, challenges related to hallucinations, reproducibility, and data protection, as well as ethical concerns, need to be addressed before widespread implementation.LLMs have immense potential in radiology, particularly for supporting language-based process steps, with technological advances such as RAG and cloud-based approaches potentially accelerating clinical implementation. · LLMs can optimize reporting and other language-based processes in radiology with technologies such as RAG and multi-step reasoning approaches.. · Challenges such as hallucinations, reproducibility, privacy, and ethical concerns must be addressed before widespread adoption.. · RAG and cloud-based approaches could help overcome these challenges and advance the clinical implementation of LLMs.. · Fink A, Rau S, Kästingschäfer K et al. From Referral to Reporting: The Potential of Large Language Models in the Radiological Workflow. Rofo 2025; DOI 10.1055/a-2641-3059.

Bronchopulmonary dysplasia (BPD) is a chronic lung disease affecting 35% of extremely low birth weight infants. Defined by oxygen dependence at 36 weeks postmenstrual age, it causes lifelong respiratory complications. However, preventive interventions carry severe risks, including neurodevelopmental impairment, ventilator-induced lung injury, and systemic complications. Therefore, early BPD prognosis and prediction of BPD outcome is crucial to avoid unnecessary toxicity in low risk infants. Admission radiographs of extremely preterm infants are routinely acquired within 24h of life and could serve as a non-invasive prognostic tool. In this work, we developed and investigated a deep learning approach using chest X-rays from 163 extremely low-birth-weight infants ($\leq$32 weeks gestation, 401-999g) obtained within 24 hours of birth. We fine-tuned a ResNet-50 pretrained specifically on adult chest radiographs, employing progressive layer freezing with discriminative learning rates to prevent overfitting and evaluated a CutMix augmentation and linear probing. For moderate/severe BPD outcome prediction, our best performing model with progressive freezing, linear probing and CutMix achieved an AUROC of 0.78 $\pm$ 0.10, balanced accuracy of 0.69 $\pm$ 0.10, and an F1-score of 0.67 $\pm$ 0.11. In-domain pre-training significantly outperformed ImageNet initialization (p = 0.031) which confirms domain-specific pretraining to be important for BPD outcome prediction. Routine IRDS grades showed limited prognostic value (AUROC 0.57 $\pm$ 0.11), confirming the need of learned markers. Our approach demonstrates that domain-specific pretraining enables accurate BPD prediction from routine day-1 radiographs. Through progressive freezing and linear probing, the method remains computationally feasible for site-level implementation and future federated learning deployments.

Understanding radiologists' eye movement during Computed Tomography (CT) reading is crucial for developing effective interpretable computer-aided diagnosis systems. However, CT research in this area has been limited by the lack of publicly available eye-tracking datasets and the three-dimensional complexity of CT volumes. To address these challenges, we present the first publicly available eye gaze dataset on CT, called CT-ScanGaze. Then, we introduce CT-Searcher, a novel 3D scanpath predictor designed specifically to process CT volumes and generate radiologist-like 3D fixation sequences, overcoming the limitations of current scanpath predictors that only handle 2D inputs. Since deep learning models benefit from a pretraining step, we develop a pipeline that converts existing 2D gaze datasets into 3D gaze data to pretrain CT-Searcher. Through both qualitative and quantitative evaluations on CT-ScanGaze, we demonstrate the effectiveness of our approach and provide a comprehensive assessment framework for 3D scanpath prediction in medical imaging.