To develop and validate a non-invasive deep learning model that integrates voxel-level radiomics with multi-sequence MRI to predict microsatellite instability (MSI) status in patients with endometrial carcinoma (EC). This two-center retrospective study included 375 patients with pathologically confirmed EC from two medical centers. Patients underwent preoperative multiparametric MRI (T2WI, DWI, CE-T1WI), and MSI status was determined by immunohistochemistry. Tumor regions were manually segmented, and voxel-level radiomics features were extracted following IBSI guidelines. A dual-channel 3D deep neural network based on the Vision-Mamba architecture was constructed to jointly process voxel-wise radiomics feature maps and MR images. The model was trained and internally validated on cohorts from Center I and tested on an external cohort from Center II. Performance was compared with Vision Transformer, 3D-ResNet, and traditional radiomics models. Interpretability was assessed with feature importance ranking and SHAP value visualization. The Vision-Mamba model achieved strong predictive performance across all datasets. In the external test cohort, it yielded an AUC of 0.866, accuracy of 0.875, sensitivity of 0.833, and specificity of 0.900, outperforming other models. Integrating voxel-level radiomics features with MRI enabled the model to better capture both local and global tumor heterogeneity compared to traditional approaches. Interpretability analysis identified glszm_SizeZoneNonUniformityNormalized, ngtdm_Busyness, and glcm_Correlation as top features, with SHAP analysis revealing that tumor parenchyma, regions of enhancement, and diffusion restriction were pivotal for MSI prediction. The proposed voxel-level radiomics and deep learning model provides a robust, non-invasive tool for predicting MSI status in endometrial carcinoma, potentially supporting personalized treatment decision-making.

Computed tomography (CT) remains the primary modality for assessing renal tumors; however, tumor identification and segmentation rely heavily on manual interpretation by clinicians, which is time-consuming and subject to inter-observer variability. The heterogeneity of tumor appearance and indistinct margins further complicate accurate delineation, impacting histopathological classification, treatment planning, and prognostic assessment. There is a pressing clinical need for an automated segmentation tool to enhance diagnostic workflows and support clinical decision-making with results that are reliable, accurate, and reproducible. This study developed a fully automated pipeline based on the DeepMedic 3D convolutional neural network for the segmentation of kidneys and renal tumors through multi-scale feature extraction. The model was trained and evaluated using 5-fold cross-validation on a dataset of 382 contrast-enhanced CT scans manually annotated by experienced physicians. Image preprocessing included Hounsfield unit conversion, windowing, 3D reconstruction, and voxel resampling. Post-processing was also employed to refine output masks and improve model generalizability. The proposed model achieved high performance in kidney segmentation, with an average Dice coefficient of 93.82 ± 1.38%, precision of 94.86 ± 1.59%, and recall of 93.66 ± 1.77%. In renal tumor segmentation, the model attained a Dice coefficient of 88.19 ± 1.24%, precision of 90.36 ± 1.90%, and recall of 88.23 ± 2.02%. Visual comparisons with ground truth annotations confirmed the clinical relevance and accuracy of the predictions. The proposed DeepMedic-based framework demonstrates robust, accurate segmentation of kidneys and renal tumors on CT images. With its potential for real-time application, this model could enhance diagnostic efficiency and treatment planning in renal oncology.

Major depressive disorder (MDD) has been increasingly understood as a disorder of network-level functional dysconnectivity. However, previous brain connectome studies have primarily relied on node-centric approaches, neglecting critical edge-edge interactions that may capture essential features of network dysfunction. This study included resting-state functional MRI data from 838 MDD patients and 881 healthy controls (HC) across 23 sites. We applied a novel edge-centric connectome model to estimate edge functional connectivity and identify overlapping network communities. Regional functional diversity was quantified via normalized entropy based on community overlap patterns. Neurobiological decoding was performed to map brain-wide relationships between functional diversity alterations and patterns of gene expression and neurotransmitter distribution. Comparative machine learning analyses further evaluated the diagnostic utility of edge-centric versus node-centric connectome representations. Compared with HC, MDD patients exhibited significantly increased functional diversity within the prefrontal-striatal-thalamic reward circuit. Neurobiological decoding analysis revealed that functional diversity alterations in MDD were spatially associated with transcriptional patterns enriched for inflammatory processes, as well as distribution of 5-HT1B receptors. Machine learning analyses demonstrated superior classification performance of edge-centric models over traditional node-centric approaches in distinguishing MDD patients from HC at the individual level. Our findings highlighted that abnormal functional diversity within the reward processing system might underlie multi-level neurobiological mechanisms of MDD. The edge-centric connectome approach offers a valuable tool for identifying disease biomarkers, characterizing individual variation and advancing current understanding of complex network configuration in psychiatric disorders.

Precise implant placement in the anterior and posterior maxilla often presents challenges due to variable bone and soft tissue anatomy. Many clinicians elect a freehand surgical approach because conventional surgical guides may not always be easy to design, fabricate, or utilize. Guided surgery has been proven to have advantages over freehand surgical protocols and therefore, the present study proposed utilizing the nasopalatine canal (NPC) as an anatomical reference and point of fixation for a novel rotational path surgical template during computer-aided implant surgery (CAIS). The present digital workflow combined artificial intelligence (AI) facilitated cone beam computed tomography (CBCT) software bone segmentation of the maxillary arch to assess the NPC and surrounding hard tissues, to design and fabricate static surgical guides to precisely place implants. After rotational engagement of the maxillary buccal undercuts, each novel surgical guide incorporated the NPC for fixation with a single pin to achieve initial stability. 22 consecutive patients requiring maxillary reconstruction received 123 implants (7 fully and 15 partially edentulous) utilizing a fully-guided surgical protocol to complete 4 overdenture and 18 full-arch fixed restorations. 12 patients required extensive maxillary bone augmentation before implant placement. 13 patients required delayed loading based on bone density and 9 patients were restoratively loaded within 24 to 96 hours post-surgery, accomplished with the use of photogrammetry for the fabrication of 3D-printed restorations. The initial implant success rate was 98.37% and 100% initial prosthetic success. The use of the NPC for fixation of surgical guides did not result in any neurovascular post-operative complications. The novel template concept can improve surgical outcomes using a bone-borne template design for implant-supported rehabilitation of the partial and fully edentulous maxillary arch. Preliminary case series confirmed controlled placement accuracy with limited risk of neurovascular complications for full-arch overdenture and fixed restorations. NPC is a vital maxillary anatomic landmark for implant planning, with an expanded role for the stabilization of novel surgical guide designs due to advancements in AI bone segmentation.

BackgroundCarotid plaque presence is associated with cardiovascular risk, even among asymptomatic individuals. While deep learning has shown promise for carotid plaque phenotyping in patients with advanced atherosclerosis, its application in population-based settings of asymptomatic individuals remains unexplored. MethodsWe developed a YOLOv8-based model for plaque detection using carotid ultrasound images from 19,499 participants of the population-based UK Biobank (UKB) and fine-tuned it for external validation in the BiDirect study (N = 2,105). Cox regression was used to estimate the impact of plaque presence and count on major cardiovascular events. To explore the genetic architecture of carotid atherosclerosis, we conducted a genome-wide association study (GWAS) meta-analysis of the UKB and CHARGE cohorts. Mendelian randomization (MR) assessed the effect of genetic predisposition to vascular risk factors on carotid atherosclerosis. ResultsOur model demonstrated high performance with accuracy, sensitivity, and specificity exceeding 85%, enabling identification of carotid plaques in 45% of the UKB population (aged 47-83 years). In the external BiDirect cohort, a fine-tuned model achieved 86% accuracy, 78% sensitivity, and 90% specificity. Plaque presence and count were associated with risk of major adverse cardiovascular events (MACE) over a follow-up of up to seven years, improving risk reclassification beyond the Pooled Cohort Equations. A GWAS meta-analysis of carotid plaques uncovered two novel genomic loci, with downstream analyses implicating targets of investigational drugs in advanced clinical development. Observational and MR analyses showed associations between smoking, LDL cholesterol, hypertension, and odds of carotid atherosclerosis. ConclusionsOur model offers a scalable solution for early carotid plaque detection, potentially enabling automated screening in asymptomatic individuals and improving plaque phenotyping in population-based cohorts. This approach could advance large-scale atherosclerosis research. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=131 SRC="FIGDIR/small/24315675v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): [email protected]@27a04corg.highwire.dtl.DTLVardef@18cef18org.highwire.dtl.DTLVardef@1a53d8f_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGRAPHICAL ABSTRACT.C_FLOATNO ASCVD - Atherosclerotic Cardiovascular Disease, CVD - Cardiovascular disease, PCE - Pooled Cohort Equations, TP- true positive, FN - False Negative, FP - False Positive, TN - True Negative, GWAS - Genome-Wide Association Study. C_FIG CLINICAL PERSPECTIVECarotid ultrasound is a well-established method for assessing subclinical atherosclerosis with potential to improve cardiovascular risk assessment in asymptomatic individuals. Deep learning could automate plaque screening and enable processing of large imaging datasets, reducing the need for manual annotation. Integrating such large-scale carotid ultrasound datasets with clinical, genetic, and other relevant data can advance cardiovascular research. Prior studies applying deep learning to carotid ultrasound have focused on technical tasks-plaque classification, segmentation, and characterization-in small sample sizes of patients with advanced atherosclerosis. However, they did not assess the potential of deep learning in detecting plaques in asymptomatic individuals at the population level. We developed an efficient deep learning model for the automated detection and quantification of early carotid plaques in ultrasound imaging, primarily in asymptomatic individuals. The model demonstrated high accuracy and external validity across population-based cohort studies. Predicted plaque prevalence aligned with known cardiovascular risk factors. Importantly, predicted plaque presence and count were associated with future cardiovascular events and improved reclassification of asymptomatic individuals into clinically meaningful risk categories. Integrating our model predictions with genetic data identified two novel loci associated with carotid plaque presence--both previously linked to cardiovascular disease--highlighting the models potential for population-scale atherosclerosis research. Our model provides a scalable solution for automated carotid plaque phenotyping in ultrasound images at the population level. These findings support its use for automated screening in asymptomatic individuals and for streamlining plaque phenotyping in large cohorts, thereby advancing research on subclinical atherosclerosis in the general population.

Homogeneous AI assessment is required for CT-T staging of gastric cancer. To construct an End-to-End CT-based Deep Learning (DL) model for tumor T-staging in advanced gastric cancer. A retrospective study was conducted on 460 cases of presurgical CT patients with advanced gastric cancer between 2011 and 2024. A Three-dimensional (3D)-Convolution (Conv)-UNet based automatic segmentation model was employed to segment tumors, and a SmallFocusNet-based ternary classification model was built for CT-T staging. Finally, these models were integrated to create an end-to-end DL model. The segmentation model's performance was assessed using the Dice similarity coefficient (DSC), Intersection over Union (IoU) and 95 % Hausdorff Distance (HD_95), while the classification model's performance was measured with thearea under the Receiver Operating Characteristic curve (AUC), sensitivity, specificity, and F1-score.Eventually, the end-to-end DL model was compared with the radiologist using the McNemar test. The data were divided into Dataset 1(423 cases for training and test set, mean age, 65.0 years ± 9.46 [SD]) and Dataset 2(37 cases for independent validation set, mean age, 68.8 years ± 9.28 [SD]). For segmentation task, the model achieved a DSC of 0.860 ± 0.065, an IoU of 0.760 ± 0.096 in test set of Dataset 1, and a DSC of 0.870 ± 0.164, an IoU of 0.793 ± 0.168 in Dataset 2. For classification task,the model demonstrated a macro-average AUC of 0.882(95 % CI 0.812-0.926), an average sensitivity of 76.9 % (95 % CI 67.6 %-85.3 %) in test set of Dataset 1 and a macro-average AUC of 0.862(95 % CI 0.723-0.942), an average sensitivity of 76.3 % (95 % CI 59.8 %-90.0 %) in Dataset 2. Meanwhile, the DL model's performance was better than that of radiologist (Accuracy was 91.9 %vs82.1 %, P = 0.007). The end-to-end DL model for CT-T staging is highly accurate and consistent in pre-treatment staging of advanced gastric cancer.

Coronary computed tomography angiography (CTA)-derived plaque burden is associated with the risk of cardiovascular events and is expected to be used in clinical practice. Understanding the normative values of computed tomography-based quantitative plaque volume in the general population is clinically important for determining patient management. This study aimed to investigate the distribution of plaque volume in the general population and to develop nomograms using MiHEART (Miami Heart Study) at Baptist Health South Florida, a large community-based cohort study. The study included 2,301 asymptomatic subjects without cardiovascular disease enrolled in MiHEART. Quantitative assessment of plaque volume was performed by using artificial intelligence-guided quantitative coronary computed tomography angiography (AI-QCT) analysis. The percentiles of the plaque distribution were estimated with nonparametric techniques. Mean age of the participants was 53.5 years, and 50.4% were male. The median total plaque volume was 54 mm³ (Q1-Q3: 16-126 mm³) and increased with age. Male subjects had greater median total plaque volume than female subjects (80 mm³ [Q1-Q3: 31-181 mm³] vs 34 mm³ [Q1-Q3: 9-85 mm³]; P < 0.001); there was no difference according to race/ethnicity (Hispanic 53 mm³ [Q1-Q3: 14-119 mm³] vs non-Hispanic 54 mm³ [Q1-Q3: 17-127 mm³]; P = 0.756). The prevalence of subjects with total plaque volume ≥20 mm³ was 81.5% in male subjects and 61.9% in female subjects. Younger individuals had a greater percentage of noncalcified plaque. The large majority of study subjects had plaque detected by using AI-QCT. Furthermore, age- and sex-specific nomograms provided information on the plaque volume distribution in an asymptomatic population. (Miami Heart Study [MiHEART] at Baptist Health South Florida; NCT02508454).

The aim of this study was to develop, test and validate automated interpretable deep learning algorithms for the assessment and classification of the spheno-occipital synchondrosis (SOS) fusion stages from a cone beam computed tomography (CBCT). The sample consisted of 723 CBCT scans of orthodontic patients from private practices in the midwestern United States. The SOS fusion stages were classified by two orthodontists and an oral and maxillofacial radiologist. The advanced deep learning models employed consisted of ResNet, EfficientNet and ConvNeXt. Additionally, a new attention-based model, ConvNeXt + Conv Attention, was developed to enhance classification accuracy by integrating attention mechanisms for capturing subtle medical imaging features. Laslty, YOLOv11 was integrated for fully-automated region detection and segmentation. ConvNeXt + Conv Attention outperformed the other models and achieved a 88.94% accuracy with manual cropping and 82.49% accuracy in a fully automated workflow. This study introduces a novel artificial intelligence-based pipeline that reliably automates the classification of the SOS fusion stages using advanced deep learning models, with the highest accuracy achieved by ConvNext + Conv Attention. These models enhance the efficiency, scalability and consistency of SOS staging while minimising manual intervention from the clinician, underscoring the potential for AI-driven solutions in orthodontics and clinical workflows.

Quantitative susceptibility mapping (QSM) is a useful magnetic resonance imaging technique. We aim to propose a deep learning (DL)-based method for QSM reconstruction that is robust to data perturbations. We developed Diffusion-QSM, a diffusion model-based method with a time-travel and resampling refinement module for high-quality QSM reconstruction. First, the diffusion prior is trained unconditionally on high-quality QSM images, without requiring explicit information about the measured tissue phase, thereby enhancing generalization performance. Subsequently, during inference, the physical constraints from the QSM forward model and measurement are integrated into the output of the diffusion model to guide the sampling process toward realistic image representations. In addition, a time-travel and resampling module is employed during the later sampling stage to refine the image quality, resulting in an improved reconstruction without significantly prolonging the time. Experimental results show that Diffusion-QSM outperforms traditional and unsupervised DL methods for QSM reconstruction using simulation, in vivo and ex vivo data and shows better generalization capability than supervised DL methods when processing out-of-distribution data. Diffusion-QSM successfully unifies data-driven diffusion priors and subjectspecific physics constraints, enabling generalizable, high-quality QSM reconstruction under diverse perturbations, including image contrast, resolution and scan direction. This work advances QSM reconstruction by bridging the generalization gap in deep learning. The excellent quality and generalization capability underscore its potential for various realistic applications.

This study aimed to develop a deep learning (DL) framework using registration-guided generative adversarial networks (RegGAN) to synthesize contrast-enhanced CT (Syn-CECT) from non-contrast CT (NCCT), enabling iodine-free esophageal cancer (EC) T-staging. A retrospective multicenter analysis included 1,092 EC patients (2013-2024) divided into training (N = 313), internal (N = 117), and external test cohorts (N = 116 and N = 546). RegGAN synthesized Syn-CECT by integrating registration and adversarial training to address NCCT-CECT misalignment. Tumor segmentation used CSSNet with hierarchical feature fusion, while T-staging employed a dual-path DL model combining radiomic features (from NCCT/Syn-CECT) and Vision Transformer-derived deep features. Performance was validated via quantitative metrics (NMAE, PSNR, SSIM), Dice scores, AUC, and reader studies comparing six clinicians with/without model assistance. RegGAN achieved Syn-CECT quality comparable to real CECT (NMAE = 0.1903, SSIM = 0.7723; visual scores: p ≥ 0.12). CSSNet produced accurate tumor segmentation (Dice = 0.89, 95% HD = 2.27 in external tests). The DL staging model outperformed machine learning (AUC = 0.7893-0.8360 vs. ≤ 0.8323), surpassing early-career clinicians (AUC = 0.641-0.757) and matching experts (AUC = 0.840). Syn-CECT-assisted clinicians improved diagnostic accuracy (AUC increase: ~ 0.1, p < 0.01), with decision curve analysis confirming clinical utility at > 35% risk threshold. The RegGAN-based framework eliminates contrast agents while maintaining diagnostic accuracy for EC segmentation (Dice > 0.88) and T-staging (AUC > 0.78). It offers a safe, cost-effective alternative for patients with iodine allergies or renal impairment and enhances diagnostic consistency across clinician experience levels. This approach addresses limitations of invasive staging and repeated contrast exposure, demonstrating transformative potential for resource-limited settings.