ObjectiveTo determine if combining PET-derived beta-cell mass (BCM) estimates with MRI- based morphology metrics improves the prediction of beta-cell functional mass in type 2 diabetes (T2D). MethodsWe performed a retrospective analysis of 40 participants; 19 T2D, 16 healthy obese volunteers (HOV), 5 prediabetes, who underwent [18F]FP-(+)-DTBZ PET to quantify vesicular monoamine transporter type 2 (VMAT2) density (SUVR-1), T1-weighted MRI for 3D morphology metric analysis, and an arginine stimulus test to measure acute (AIRarg) and maximum (AIRargMAX) insulin responses. Lasso regression models identified the optimal combination of PET, MRI, and clinical variables to predict beta-cell function for the whole pancreas and its subregions. ResultsCompared to HOV, individuals with T2D exhibited significantly reduced AIRarg and AIRargMAX. Only pancreas body volume was significantly smaller in the T2D cohort. For the whole pancreas, a model including PET-derived SUVR-1 and a subset of clinical covariates best predicted acute beta-cell function (AIRarg). However, predicting maximum functional reserve (AIRargMAX) required the addition of MRI-based morphology metrics in combination with SUVR-1 and a subset of clinical covariates. ConclusionWe combined PET imaging of BCM and MRI morphology metrics with a robust machine learning-based variable selection method to extract useful PET- and MRI-based metrics for predicting functional and not-fully functional BCM. This synergistic approach offers a novel combination of biomarkers for staging disease and evaluating therapeutic interventions.

Despite advances in deep learning (DL) and computer vision, breast cancer (BC) detection via ultra-sound remains challenging. Existing methods often focus on single tasks using complex pipelines and publicly available datasets, limiting clinical applicability. To address this, we propose BreastUS-Net-a novel architecture for hierarchical BC classification using diverse datasets. Our approach uses a dual-branch MobileNet architecture with fine-tuned and frozen layers to capture both task-specific and general features, eliminating manual feature extraction. These features are then fused to create a comprehensive representation, which is subsequently aggregated and refined. The aggregation step merges the outputs from both branches, while the refinement module reduces complexity, highlights relevant patterns, and mitigates overfitting to improve generalization. Additionally, we integrate a multihead self-attention (MHSA) block to highlight diagnostically significant regions in ultrasound images, enhancing both accuracy and robustness. Finally, the orthogonal softmax layer (OSL) boosts discriminative power by enforcing orthogonality among weight vectors, reducing parameter coadaptation and enabling more effective optimization. We used six diverse datasets from multiple centers, including: a large Zhejiang Cancer Hospital set (2,171 images), public BUSI dataset (780 images), external test sets from Yunnan Cancer Hospital (351 images) and Sir Run Run Shaw Hospitals (365 images), fibroadenoma (FA) vs. phyllodes tumor (PT) classification, and a PT grading dataset. We use explainable AI (XAI) techniques-Grad-CAM, SHAP, and saliency maps-to enhance trust in breast ultrasound predictions. Our model achieves state-of-the-art performance, with accuracies of 94.48% on a clinical dataset and 94.23% on the BUSI dataset, highlighting its potential to improve BC diagnosis and personalized treatment.

Sudden cardiac death (SCD) remains a feared and difficult-to-predict outcome in patients with congenital heart disease (CHD). This review examines the latest evidence in risk stratification, with a focus on limitations of existing models and the mechanistic and statistical complexities that hinder individualized decision-making. New multivariable risk scores for repaired tetralogy of Fallot and systemic right ventricle have improved prognostic resolution. Artificial intelligence-enabled ECG algorithms have shown promise in early identification of high-risk individuals with repaired tetralogy of Fallot. In parallel, three-dimensional cardiac magnetic resonance imaging has been leveraged to delineate arrhythmogenic isthmuses, enhancing substrate-guided interventions. While these tools enhance risk estimation, they require validation specific to the prediction of shockable terminal rhythms, improved interpretability, and integration into individualized decision frameworks. SCD risk prediction in CHD is evolving toward a multimodal, individualized approach that emphasizes probabilistic reasoning, shared decision-making, and epistemic humility. Although new models and technologies offer incremental gains, they do not eliminate the uncertainty inherent in predicting rare events. The application of population-based tools to individual patients must be interpreted cautiously, recognizing that SCD represents a final common pathway for diverse pathophysiological processes, and that decisions about ICD implantation entail complex trade-offs.

Deformable image registration aims to achieve nonlinear alignment of image spaces by estimating dense displacement fields. It is widely used in clinical tasks such as surgical planning, assisted diagnosis, and surgical navigation. While efficient, deep learning registration methods often struggle with large, complex displacements. Pyramid-based approaches address this with a coarse-to-fine strategy, but their single-feature processing can lead to error accumulation. In this paper, we introduce a dense Mixture of Experts (MoE) pyramid registration model, using routing schemes and multiple heterogeneous experts to increase the width and flexibility of feature processing within a single layer. The collaboration among heterogeneous experts enables the model to retain more precise details and maintain greater feature freedom when dealing with complex displacements. We use only deformation fields as the information transmission paradigm between different levels, with deformation field interactions between layers, which encourages the model to focus on the feature location matching process and perform registration in the correct direction. We do not utilize any complex mechanisms such as attention or ViT, keeping the model at its simplest form. The powerful deformable capability allows the model to perform volume registration directly and accurately without the need for affine registration. Experimental results show that the model achieves outstanding performance across four public datasets, including brain registration, lung registration, and abdominal multi-modal registration. The code will be published at https://github.com/Darlinglinlinlin/MOE_Morph.

Enlarged adenoids that obstruct nasal breathing can cause significant health complications, including cognitive deficits, cardiovascular risks, and developmental delays. Early and accurate diagnosis is critical for effective treatment planning, but current diagnostic methods-such as polysomnography and clinical visual inspection-are either time-consuming, expensive, or lack sufficient accuracy. As cone-beam computed tomography (CBCT) scans are frequently available for these patients and may complement diagnosis, we propose an open-source, automated deep learning tool for quantitative airway obstruction assessment. Our method leverages CBCT scans, which are automatically segmented and processed to extract 3D airway morphology. Our approach combines two advanced techniques for 3D shape analysis: multi-view and point cloud representations to capture both global and local airway features, enhancing classification and regression performance. Our model achieves an accuracy of 81.88% in classifying the presence or absence of adenoid hypertrophy and demonstrates improved performance in predicting the nasopharynx airway obstruction ratio. While the model performs well in detecting severe cases, further refinement is needed to improve classification and regression across all severity levels. This tool has the potential to enhance clinical workflows by providing rapid, quantitative, and reproducible assessments of airway obstruction, offering a promising solution for improving diagnostic efficiency and patient outcomes in clinical practice.

Breast cancer (BC) remains one of the most prevalent and challenging malignancies worldwide, affecting millions of women and shaping healthcare priorities across continents. Advances in early detection have significantly improved survival rates. In recent years, artificial intelligence (AI) has emerged as a powerful tool in this domain, transforming traditional diagnostic methods. Initially based on simple rule-based systems, AI has evolved into sophisticated deep learning models capable of analyzing complex medical data with remarkable accuracy. This bibliometric analysis examines the application of AI in the early diagnosis of breast cancer, aiming to understand not only the current state of the field but also its growth over the past decade. Publications indexed in Web of Science and Scopus from 2012 to March 2025 were systematically reviewed, while earlier literature (1994-2012) provided historical context. Tools such as Biblioshiny and VOSviewer were used to map research trends, collaboration patterns, and thematic evolution. Out of 1,436 initial documents, 1,293 high-quality studies were included. The results show a clear acceleration in AI-focused research after 2020, with increased global collaboration and a notable shift toward open-access publication. Recurring themes such as "machine learning," "diagnostic imaging," and "clinical decision support" highlight the field's direction. As AI becomes more integrated into clinical workflows, its potential to enhance diagnostic speed, consistency, and personalization is undeniable. However, key ethical issues such as bias, transparency, and patient data protection remain central to responsible implementation.

Magnetic Resonance Fingerprinting (MRF) is a technique that can provide rapid quantification of multiple tissue properties. Deep learning may potentially contribute to an accelerated acquisition of MRF. (1) To develop a deep learning method to accelerate the acquisition for kidney MRF; (2) to evaluate its performance in healthy subjects and patients with renal masses. Retrospective and based on internal reference data. Development set was 36 healthy subjects and 20 patients with renal masses. The testing set: 4 healthy subjects and 16 patients. 3T, Steady-State Free Precession (FISP)-based MRF. Quantification accuracy was evaluated in healthy kidneys and renal masses using quantitative metrics including normalized root-mean-square error (NRMSE) calculated based on reference maps generated using the standard template matching approach with all acquired MRF time frames. Paired Student's t-test. p < 0.05 was considered statistically significant. Accurate quantification in both T₁ (NRMSE = 0.025 ± 0.003) and T₂ (NRMSE = 0.053 ± 0.010) maps was obtained for healthy kidney tissues with a three-fold acceleration (576 time frames, 5 s of scan time), outperforming the template matching approach (T₁, NRMSE = 0.057 ± 0.015; T₂, NRMSE = 0.143 ± 0.080). For renal masses with T₁ and T₂ values in close range of healthy kidney tissues, similar performance was achieved with a three-fold acceleration. For renal masses presenting distinct T₁ or T₂ values, more MRF time frames were required to provide accurate tissue quantification. No significant difference was noticed in tissue/tumor quantification between neural networks trained using only healthy subjects versus a mixed dataset with healthy subjects and patients (p > 0.05). A deep learning-based method was developed to accelerate acquisition without compromising the accuracy of relaxation time mapping using kidney MRF. These results demonstrate reliable tissue quantification with at least a two-fold acceleration for both healthy kidneys and renal masses with various subtypes and histopathological grades. 4. Stage 1.

Papillary thyroid carcinoma (PTC) constitutes the predominant subtype among thyroid malignancies. Despite its generally favorable prognosis, certain aggressive subtypes, along with recurrent and metastatic manifestations, substantially affect patient survival outcomes. Recent advancements in the diagnostic and therapeutic strategies for PTC have ushered in a new era, characterized by the integration of molecular mechanisms and imaging-based evaluations. This review offers an integrated perspective of the clinicopathological features, molecular genetic characteristics, epigenetic regulation, and the contribution of the immune microenvironment to the aggressiveness of PTC. Primary investigation targets include BRAF/RAS/RET-related molecular mechanisms and the functional significance of non-coding RNAs [especially long non-coding RNAs (lncRNAs) and microRNAs (miRNAs)] in molecular regulation. Additionally, the impact of clinical factors such as age, sex, obesity, and comorbidity with Hashimoto's thyroiditis on the aggressiveness of PTC is thoroughly examined. Furthermore, this review systematically synthesizes the clinical advances in the early detection and risk assessment of aggressive PTC by emerging imaging modalities such as conventional ultrasound, interventional ultrasound, ultrasound elastography, contrast-enhanced ultrasound, and artificial intelligence-assisted analysis. Looking ahead, multidisciplinary collaborations integrating pathology, genomics, and imaging are anticipated to enhance the precise evaluation of PTC aggressiveness and facilitate the development of individualized treatment strategies. This review serves as a comprehensive reference for mechanistic exploration and clinical translation in the study of PTC aggressiveness, and provides guidance for the progression of precision medicine and management models for PTC patients.

Ultrasound (US) imaging is valued for its safety, affordability, and accessibility, but its low spatial resolution and operator dependence limit its diagnostic capabilities. Tomographic imaging modalities like computed tomography (CT) and magnetic resonance imaging (MRI) offer high-resolution 3D visualization but are cost-prohibitive and complex. Ultrasound-based tomographic imaging aims to combine the advantages of both modalities, potentially democratizing access to advanced imaging. A scoping review was conducted following PRISMA-SR guidelines. Articles were identified through searches in PubMed MEDLINE, Embase, Scopus, and arXiv from inception to July 2025. Eligibility criteria included full-text original studies focused on ultrasound-based tomographic imaging generation or reconstruction methods. Out of 8256 identified articles, 86 met the inclusion criteria. Studies examined four imaging modalities: photoacoustic tomography (36%), ultrasound computed tomography (36%), 3D reconstruction (20%), and synthetic imaging (7%). Deep learning algorithms (67%) were the most common, followed by iterative reconstruction algorithms (9%), and other methods. The breast (17%), brain (16%), and blood vessels (14%) were the most studied anatomical regions. This review highlights advancements in ultrasound-based tomographic imaging, driven by deep learning innovations. Despite progress, the field is still in its infancy, and challenges remain in clinical adoption, particularly in standardization and validating performance. Future research should focus on improving algorithm efficiency, generalizability, and validation.

Accurate detection of brain midline shift is critical for the diagnosis and monitoring of neurological conditions such as traumatic brain injuries, strokes, and tumors. This study aims to address the lack of dedicated datasets and tools for this task by introducing a novel dataset and a 3D Slicer extension, evaluating the effectiveness of multiple deep learning models for automatic detection of brain midline shift. We introduce the brain-midline-detection dataset, specifically designed for identifying three brain landmarks-Anterior Falx (AF), Posterior Falx (PF), and Septum Pellucidum (SP)-in MRI scans. A comprehensive performance evaluation was conducted using deep learning models including YOLOv5 (n, s, m, l), YOLOv8, and YOLOv9 (GELAN-C model). The best-performing model was integrated into the 3D Slicer platform as a custom extension, incorporating steps such as MRI preprocessing, filtering, skull stripping, registration, and midline shift computation. Among the evaluated models, YOLOv5l achieved the highest precision (0.9601) and recall (0.9489), while YOLOv5m delivered the best [email protected]:0.95 score (0.6087). YOLOv5n and YOLOv5s exhibited the lowest loss values, indicating high efficiency. Although YOLOv8s achieved a higher [email protected]:0.95 score (0.6382), its high loss values reduced its practical effectiveness. YOLOv9-GELAN-C performed the worst, with the highest losses and lowest overall accuracy. YOLOv5m was selected as the optimal model due to its balanced performance and was successfully integrated into 3D Slicer as an extension for automated midline shift detection. By offering a new annotated dataset, a validated detection pipeline, and open-source tools, this study contributes to more accurate, efficient, and accessible AI-assisted medical imaging for brain midline assessment.