This study aimed to develop and validate a deep learning (DL) model to detect obstructive coronary artery disease (CAD, ≥ 50% stenosis) in coronary CT angiography (CCTA) among patients presenting to the emergency department (ED) with acute chest pain. The training dataset included 378 patients with acute chest pain who underwent CCTA (10,060 curved multiplanar reconstruction [MPR] images) from a single-center ED between January 2015 and December 2022. The external validation dataset included 298 patients from 3 ED centers between January 2021 and December 2022. A DL model based on You Only Look Once v4, requires manual preprocessing for curved MPR extraction and was developed using 15 manually preprocessed MPR images per major coronary artery. Model performance was evaluated per artery and per patient. The training dataset included 378 patients (mean age 61.3 ± 12.2 years, 58.2% men); the external dataset included 298 patients (mean age 58.3 ± 13.8 years, 54.6% men). Obstructive CAD prevalence in the external dataset was 27.5% (82/298). The DL model achieved per-artery sensitivity, specificity, positive predictive value, negative predictive value (NPV), and area under the curve (AUC) of 92.7%, 89.9%, 62.6%, 98.5%, and 0.919, respectively; and per-patient values of 93.3%, 80.7%, 67.7%, 96.6%, and 0.871, respectively. The DL model demonstrated high sensitivity and NPV for identifying obstructive CAD in patients with acute chest pain undergoing CCTA, indicating its potential utility in aiding ED physicians in CAD detection.

Total kidney and liver volumes are key image-based biomarkers to predict the severity of kidney and liver phenotype in autosomal dominant polycystic kidney disease (ADPKD). However, MRI-based advanced biomarkers like total cyst number (TCN) and cyst parenchyma surface area (CPSA) have been shown to more accurately assess cyst burden and improve the prediction of disease progression. The main aim of this study is to extend the calculation of advanced biomarkers to other imaging modalities; thus, we propose a fully automated model to segment kidney and liver cysts in CT images. Abdominal CTs of ADPKD patients were gathered retrospectively between 2001-2018. A 3D deep-learning method using the nnU-Net architecture was trained to learn cyst edges-cores and the non-cystic kidney/liver parenchyma. Separate segmentation models were trained for kidney cysts in contrast-enhanced CTs and liver cysts in non-contrast CTs using an active learning approach. Two experienced research fellows manually generated the reference standard segmentation, which were reviewed by an expert radiologist for accuracy. Two-hundred CT scans from 148 patients (mean age, 51.2 ± 14.1 years; 48% male) were utilized for model training (80%) and testing (20%). In the test set, both models showed good agreement with the reference standard segmentations, similar to the agreement between two independent human readers (model vs reader: TCNkidney/liver r=0.96/0.97 and CPSAkidney r=0.98), inter-reader: TCNkidney/liver r=0.96/0.98 and CPSAkidney r=0.99). Our study demonstrates that automated models can segment kidney and liver cysts accurately in CT scans of patients with ADPKD.

Fractional flow reserve (FFR) - a physiological indicator of coronary stenosis significance - has now become a widely used parameter also in the guidance of percutaneous coronary intervention (PCI). Several studies have shown the superiority of FFR compared to visual assessment, contributing to the reduction in clinical endpoints. However, the current approach to FFR assessment requires coronary instrumentation with a dedicated pressure wire and thus increasing invasiveness, cost, and duration of the procedure. Alternative, noninvasive methods of FFR assessment based on computational fluid dynamics are being widely tested; these approaches are generally not fully automated and may sometimes require substantial computational power. Nowadays, one of the most rapidly expanding fields in medicine is the use of artificial intelligence (AI) in therapy optimization, diagnosis, treatment, and risk stratification. AI usage contributes to the development of more sophisticated methods of imaging analysis and allows for the derivation of clinically important parameters in a faster and more accurate way. Over the recent years, AI utility in deriving FFR in a noninvasive manner has been increasingly reported. In this review, we critically summarize current knowledge in the field of AI-derived FFR based on data from computed tomography angiography, invasive angiography, optical coherence tomography, and intravascular ultrasound. Available solutions, possible future directions in optimizing cathlab performance, including the use of mixed reality, as well as current limitations standing behind the wide adoption of these techniques, are overviewed.

Pituitary adenomas, ranging from subtle microadenomas to mass-effect macroadenomas, pose diagnostic challenges for radiologists due to increasing scan volumes and the complexity of dynamic contrast-enhanced MRI interpretation. A hybrid CNN-LSTM model was trained and validated on a multi-center dataset of 2,163 samples from Tehran and Babolsar, Iran. Transfer learning and preprocessing techniques (e.g., Wiener filters) were utilized to improve classification performance for microadenomas (< 10 mm) and macroadenomas (> 10 mm). The model achieved 90.5% accuracy, an area under the receiver operating characteristic curve (AUROC) of 0.92, and 89.6% sensitivity (93.5% for microadenomas, 88.3% for macroadenomas), outperforming standard CNNs by 5-18% across metrics. With a processing time of 0.17 s per scan, the model demonstrated robustness to variations in imaging conditions, including scanner differences and contrast variations, excelling in real-time detection and differentiation of adenoma subtypes. This dual-path approach, the first to synergize spatial and temporal MRI features for pituitary diagnostics, offers high precision and efficiency. Supported by comparisons with existing models, it provides a scalable, reproducible tool to improve patient outcomes, with potential adaptability to broader neuroimaging challenges.

Quantitative magnetic resonance imaging (qMRI) has attracted more and more attention in clinical diagnosis and medical sciences due to its capability to non-invasively characterize tissue properties. Nevertheless, most qMRI methods are time-consuming and sensitive to motion, making them inadequate for quantifying organs with physiological movement. In this context, single-shot multiple overlapping-echo detachment (MOLED) imaging technique has been presented, but its acquisition efficiency and image quality are limited when the field of view (FOV) is smaller than the object, especially for abdominal organs and myocardium. A novel single-shot reduced FOV qMRI method was developed based on MOLED (termed rFOV-MOLED). This method combines zonal oblique multislice (ZOOM) and outer volume suppression (OVS) techniques to reduce the FOV and suppress signals outside the FOV. A deep neural network was trained using synthetic data generated from Bloch simulations to achieve high-quality T₂ map reconstruction from rFOV-MOLED iamges. Numerical simulation, water phantom and in vivo abdominal and myocardial imaging experiments were performed to evaluate the method. The coefficient of variation and repeatability index were used to evaluate the reproducibility. Multiple statistical analyses were utilized to evaluate the accuracy and significance of the method, including linear regression, Bland-Altman analysis, Wilcoxon signed-rank test, and Mann-Whitney U test, with the p-value significance level of 0.05. Experimental results show that rFOV-MOLED achieved excellent performance in reducing aliasing signals due to FOV reduction. It provided T₂ maps closely resembling the reference maps. Moreover, it gave finer tissue details than MOLED and was quite repeatable. rFOV-MOLED can ultrafast and stably provide accurate T2 maps for myocardium and specific abdominal organs with improved acquisition efficiency and image quality.

This study aimed to develop and validate deep learning models using [¹⁸F]FDG PET/CT to predict PD-L1 status in esophageal cancer (EC) patients. Additionally, we assessed the potential of derived deep learning model scores (DLS) for survival stratification in immunotherapy. In this retrospective study, we included 331 EC patients from two centers, dividing them into training, internal validation, and external validation cohorts. Fifty patients who received immunotherapy were followed up. We developed four 3D ResNet10-based models-PET + CT + clinical factors (CPC), PET + CT (PC), PET (P), and CT (C)-using pre-treatment [¹⁸F]FDG PET/CT scans. For comparison, we also constructed a logistic model incorporating clinical factors (clinical model). The DLS were evaluated as radiological markers for survival stratification, and nomograms for predicting survival were constructed. The models demonstrated accurate prediction of PD-L1 status. The areas under the curve (AUCs) for predicting PD-L1 status were as follows: CPC (0.927), PC (0.904), P (0.886), C (0.934), and the clinical model (0.603) in the training cohort; CPC (0.882), PC (0.848), P (0.770), C (0.745), and the clinical model (0.524) in the internal validation cohort; and CPC (0.843), PC (0.806), P (0.759), C (0.667), and the clinical model (0.671) in the external validation cohort. The CPC and PC models exhibited superior predictive performance. Survival analysis revealed that the DLS from most models effectively stratified overall survival and progression-free survival at appropriate cut-off points (P < 0.05), outperforming stratification based on PD-L1 status (combined positive score ≥ 10). Furthermore, incorporating model scores with clinical factors in nomograms enhanced the predictive probability of survival after immunotherapy. Deep learning models based on [¹⁸F]FDG PET/CT can accurately predict PD-L1 status in esophageal cancer patients. The derived DLS can effectively stratify survival outcomes following immunotherapy, particularly when combined with clinical factors.

Magnetic susceptibility differences at the heart-lung interface introduce B₀-field inhomogeneities that challenge cardiac MRI at high field strengths (≥ 3 T). Although hardware-based shimming has advanced, conventional approaches often neglect dynamic variations in thoracic anatomy caused by cardiac and respiratory motion, leading to residual off-resonance artifacts. This study aims to characterize motion-induced B₀-field fluctuations in the heart and evaluate a deep learning-enabled motion-adaptive B₀ shimming pipeline to mitigate them. A motion-resolved B₀ mapping sequence was implemented at 3 T to quantify cardiac and respiratory-induced B₀ variations. A motion-adaptive shimming framework was then developed and validated through numerical simulations and human imaging studies. B₀-field homogeneity and T₂* mapping accuracy were assessed in multiple breath-hold positions using standard and motion-adaptive shimming. Respiratory motion significantly altered myocardial B₀ fields (p < 0.01), whereas cardiac motion had minimal impact (p = 0.49). Compared with conventional scanner shimming, motion-adaptive B₀ shimming yielded significantly improved field uniformity across both inspiratory (post-shim SD_ratio: 0.68 ± 0.10 vs. 0.89 ± 0.11; p < 0.05) and expiratory (0.65 ± 0.16 vs. 0.84 ± 0.20; p < 0.05) breath-hold states. Corresponding improvements in myocardial T₂* map homogeneity were observed, with reduced coefficient of variation (0.44 ± 0.19 vs. 0.39 ± 0.22; 0.59 ± 0.30 vs. 0.46 ± 0.21; both p < 0.01). The proposed motion-adaptive B₀ shimming approach effectively compensates for respiration-induced B₀ fluctuations, enhancing field homogeneity and reducing off-resonance artifacts. This strategy improves the robustness and reproducibility of T₂* mapping, enabling more reliable high-field cardiac MRI.

Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to assess physical properties of brain connections. Here, we present an integrative ecosystem of software that performs all steps of tractometry: post-processing of dMRI data, delineation of major white matter pathways, and modeling of the tissue properties within them. This ecosystem also provides a set of interoperable and extensible tools for visualization and interpretation of the results that extract insights from these measurements. These include novel machine learning and statistical analysis methods adapted to the characteristic structure of tract-based data. We benchmark the performance of these statistical analysis methods in different datasets and analysis tasks, including hypothesis testing on group differences and predictive analysis of subject age. We also demonstrate that computational advances implemented in the software offer orders of magnitude of acceleration. Taken together, these open-source software tools-freely available at https://tractometry.org-provide a transformative environment for the analysis of dMRI data.

AI-based neural decoding reconstructs visual perception by leveraging generative models to map brain activity measured through functional MRI (fMRI) into the observed visual stimulus. Traditionally, ridge linear models transform fMRI into a latent space, which is then decoded using variational autoencoders (VAE) or latent diffusion models (LDM). Owing to the complexity and noisiness of fMRI data, newer approaches split the reconstruction into two sequential stages, the first one providing a rough visual approximation using a VAE, the second one incorporating semantic information through the adoption of LDM guided by contrastive language-image pre-training (CLIP) embeddings. This work addressed some key scientific and technical gaps of the two-stage neural decoding by: 1) implementing a gated recurrent unit (GRU)-based architecture to establish a non-linear mapping between the fMRI signal and the VAE latent space, 2) optimizing the dimensionality of the VAE latent space, 3) systematically evaluating the contribution of the first reconstruction stage, and 4) analyzing the impact of different brain regions of interest (ROIs) on reconstruction quality. Experiments on the Natural Scenes Dataset, containing 73,000 unique natural images, along with fMRI of eight subjects, demonstrated that the proposed architecture maintained competitive performance while reducing the complexity of its first stage by 85%. The sensitivity analysis showcased that the first reconstruction stage is essential for preserving high structural similarity in the final reconstructions. Restricting analysis to semantic ROIs, while excluding early visual areas, diminished visual coherence, preserving semantics though. The inter-subject repeatability across ROIs was about 92 and 98% for visual and sematic metrics, respectively. This study represents a key step toward optimized neural decoding architectures leveraging non-linear models for stimulus prediction. Sensitivity analysis highlighted the interplay between the two reconstruction stages, while ROI-based analysis provided strong evidence that the two-stage AI model reflects the brain's hierarchical processing of visual information.

Thyroid cancer is one of the most common endocrine malignancies. Its incidence has steadily increased in recent years. Distinguishing between benign and malignant thyroid nodules (TNs) is challenging due to their overlapping imaging features. The rapid advancement of artificial intelligence (AI) in medical image analysis, particularly deep learning (DL) algorithms, has provided novel solutions for automated TN detection. However, existing studies exhibit substantial heterogeneity in diagnostic performance. Furthermore, no systematic evidence-based research comprehensively assesses the diagnostic performance of DL models in this field. This study aimed to execute a systematic review and meta-analysis to appraise the performance of DL algorithms in diagnosing TN malignancy, identify key factors influencing their diagnostic efficacy, and compare their accuracy with that of clinicians in image-based diagnosis. We systematically searched multiple databases, including PubMed, Cochrane, Embase, Web of Science, and IEEE, and identified 41 eligible studies for systematic review and meta-analysis. Based on the task type, studies were categorized into segmentation (n=14) and detection (n=27) tasks. The pooled sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) were calculated for each group. Subgroup analyses were performed to examine the impact of transfer learning and compare model performance against clinicians. For segmentation tasks, the pooled sensitivity, specificity, and AUC were 82% (95% CI 79%-84%), 95% (95% CI 92%-96%), and 0.91 (95% CI 0.89-0.94), respectively. For detection tasks, the pooled sensitivity, specificity, and AUC were 91% (95% CI 89%-93%), 89% (95% CI 86%-91%), and 0.96 (95% CI 0.93-0.97), respectively. Some studies demonstrated that DL models could achieve diagnostic performance comparable with, or even exceeding, that of clinicians in certain scenarios. The application of transfer learning contributed to improved model performance. DL algorithms exhibit promising diagnostic accuracy in TN imaging, highlighting their potential as auxiliary diagnostic tools. However, current studies are limited by suboptimal methodological design, inconsistent image quality across datasets, and insufficient external validation, which may introduce bias. Future research should enhance methodological standardization, improve model interpretability, and promote transparent reporting to facilitate the sustainable clinical translation of DL-based solutions.