Stroke is one of the leading causes of death and disability worldwide, with a significantly elevated incidence among individuals with hypertension. Conventional risk assessment methods primarily rely on a limited set of clinical parameters and often exclude imaging-derived structural features, resulting in suboptimal predictive accuracy. This study aimed to develop a deep learning-based multimodal stroke risk prediction model by integrating carotid ultrasound imaging with multidimensional clinical data to enable precise identification of high-risk individuals among hypertensive patients. A total of 2,176 carotid artery ultrasound images from 1,088 hypertensive patients were collected. ResNet50 was employed to automatically segment the carotid intima-media and extract key structural features. These imaging features, along with clinical variables such as age, blood pressure, and smoking history, were fused using a Vision Transformer (ViT) and fed into a Radial Basis Probabilistic Neural Network (RBPNN) for risk stratification. The model's performance was systematically evaluated using metrics including AUC, Dice coefficient, IoU, and Precision-Recall curves. The proposed multimodal fusion model achieved outstanding performance on the test set, with an AUC of 0.97, a Dice coefficient of 0.90, and an IoU of 0.80. Ablation studies demonstrated that the inclusion of ViT and RBPNN modules significantly enhanced predictive accuracy. Subgroup analysis further confirmed the model's robust performance in high-risk populations, such as those with diabetes or smoking history. The deep learning-based multimodal fusion model effectively integrates carotid ultrasound imaging and clinical features, significantly improving the accuracy of stroke risk prediction in hypertensive patients. The model demonstrates strong generalizability and clinical application potential, offering a valuable tool for early screening and personalized intervention planning for stroke prevention. Not applicable.

Early diagnosis of Alzheimer's disease (AD) and its precursor, mild cognitive impairment (MCI), is critical for effective prevention and treatment. Computer-aided diagnosis using magnetic resonance imaging (MRI) provides a cost-effective and objective approach. However, existing methods often segment 3D MRI images into 2D slices, leading to spatial information loss and reduced diagnostic accuracy. To overcome this limitation, we propose TA-SSM Net, a deep learning model that leverages tri-directional attention and structured state-space model (SSM) for improved MRI-based diagnosis of AD and MCI. The tri-directional attention mechanism captures spatial and contextual information from forward, backward, and vertical directions in 3D MRI images, enabling effective feature fusion. Additionally, gradient checkpointing is applied within the SSM to enhance processing efficiency, allowing the model to handle whole-brain scans while preserving spatial correlations. To evaluate our method, we construct a dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI), consisting of 300 AD patients, 400 MCI patients, and 400 normal controls. TA-SSM Net achieved an accuracy of 90.24% for MCI detection and 95.83% for AD detection. The results demonstrate that our approach not only improves classification accuracy but also enhances processing efficiency and maintains spatial correlations, offering a promising solution for the diagnosis of Alzheimer's disease.

This study aimed to develop and assess an advanced Attention-Based Residual U-Net (ResUNet) model for accurately segmenting different types of brain hemorrhages from CT images. The goal was to overcome the limitations of manual segmentation and current automated methods regarding precision and generalizability. A dataset of 1,347 patient CT scans was collected retrospectively, covering six types of hemorrhages: subarachnoid hemorrhage (SAH, 231 cases), subdural hematoma (SDH, 198 cases), epidural hematoma (EDH, 236 cases), cerebral contusion (CC, 230 cases), intraventricular hemorrhage (IVH, 188 cases), and intracerebral hemorrhage (ICH, 264 cases). The dataset was divided into 80% for training using a 10-fold cross-validation approach and 20% for testing. All CT scans were standardized to a common anatomical space, and intensity normalization was applied for uniformity. The ResUNet model included attention mechanisms to enhance focus on important features and residual connections to support stable learning and efficient gradient flow. Model performance was assessed using the Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and directed Hausdorff distance (dHD). The ResUNet model showed excellent performance during both training and testing. On training data, the model achieved DSC scores of 95 ± 1.2 for SAH, 94 ± 1.4 for SDH, 93 ± 1.5 for EDH, 91 ± 1.4 for CC, 89 ± 1.6 for IVH, and 93 ± 2.4 for ICH. IoU values ranged from 88 to 93, with dHD between 2.1- and 2.7-mm. Testing results confirmed strong generalization, with DSC scores of 93 for SAH, 93 for SDH, 92 for EDH, 90 for CC, 88 for IVH, and 92 for ICH. IoU values were also high, indicating precise segmentation and minimal boundary errors. The ResUNet model outperformed standard U-Net variants, achieving higher multi-label segmentation accuracy. This makes it a valuable tool for clinical applications that require fast and reliable brain hemorrhage analysis. Future research could investigate semi-supervised techniques and 3D segmentation to further enhance clinical use. Not applicable.

Accurate identification and segmentation of brain tumors in Magnetic Resonance Imaging (MRI) images are critical for timely diagnosis and treatment. MRI is frequently used to diagnose these disorders; however medical professionals find it challenging to manually evaluate MRI pictures because of time restrictions and unpredictability. Computerized methods such as R-CNN, attention models and earlier YOLO variants face limitations due to high computational demands and suboptimal segmentation performance. To overcome these limitations, this study proposes a successive framework that evaluates YOLOv9, YOLOv10, and YOLOv11 for tumor detection and segmentation using the Figshare Brain Tumor dataset (2100 images) and BraTS2020 dataset (3170 MRI slices). Preprocessing involves log transformation for intensity normalization, histogram equalization for contrast enhancement, and edge-based ROI extraction. The models were trained on 80% of the combined dataset and evaluated on the remaining 20%. YOLOv11 demonstrated superior performance, achieving 96.22% classification accuracy on BraTS2020 and 96.41% on Figshare, with an F1-score of 0.990, recall of 0.984, [email protected] of 0.993, and mAP@ [0.5:0.95] of 0.801 during testing. With a fast inference time of 5.3 ms and a balanced precision-recall profile, YOLOv11 proves to be a robust, real-time solution for brain tumor detection in clinical applications.

This study aims to explore Deep Learning methods, namely Large Language Models (LLMs) and Computer Vision models to accurately predict neoadjuvant rectal (NAR) score for locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiation (NACRT). The NAR score is a validated surrogate endpoint for LARC. 160 CT scans of patients were used in this study, along with 4 different types of radiology reports, 2 generated from CT scans and other 2 from MRI scans, both before and after NACRT. For CT scans, two different approaches with convolutional neural network were utilized to tackle the 3D scan entirely or tackle it slice by slice. For radiology reports, an encoder architecture LLM was used. The performance of the approaches was quantified by the Area under the Receiver Operating Characteristic curve (AUC). The two different approaches for CT scans yielded [Formula: see text] and [Formula: see text] while the LLM trained on post NACRT MRI reports showed the most predictive potential at [Formula: see text] and a statistical improvement, p = 0.03, over the baseline clinical approach (from [Formula: see text] to [Formula: see text])). This study showcases the potential of Large Language Models and the inadequacies of CT scans in predicting NAR values. Clinical trial number Not applicable.

Predicting shorter life expectancy is crucial for prioritizing antifibrotic therapy in fibrotic lung diseases, where progression varies widely, from stability to rapid deterioration. This heterogeneity complicates treatment decisions, emphasizing the need for reliable baseline measures. This study focuses on leveraging artificial intelligence model to address heterogeneity in disease outcomes, focusing on mortality as the ultimate measure of disease trajectory. This retrospective study included 1744 anonymised patients who underwent high-resolution CT scanning. The AI model, SABRE (Smart Airway Biomarker Recognition Engine), was developed using data from patients with various lung diseases (n=460, including lung cancer, pneumonia, emphysema, and fibrosis). Then, 1284 high-resolution CT scans with evidence of diffuse FLD from the Australian IPF Registry and OSIC were used for clinical analyses. Airway branches were categorized and quantified by anatomic structures and volumes, followed by multivariable analysis to explore the associations between these categories and patients' progression and mortality, adjusting for disease severity or traditional measurements. Cox regression identified SABRE-based variables as independent predictors of mortality and progression, even adjusting for disease severity (fibrosis extent, traction bronchiectasis extent, and ILD extent), traditional measures (FVC%, DLCO%, and CPI), and previously reported deep learning algorithms for fibrosis quantification and morphological analysis. Combining SABRE with DLCO significantly improved prognosis utility, yielding an AUC of 0.852 at the first year and a C-index of 0.752. SABRE-based variables capture prognostic signals beyond that provided by traditional measurements, disease severity scores, and established AI-based methods, reflecting the progressiveness and pathogenesis of the disease.

Pancreatic ductal adenocarcinoma (PDAC) and mass-forming pancreatitis (MFP) share similar clinical, laboratory, and imaging features, making accurate diagnosis challenging. Nevertheless, PDAC is highly malignant with a poor prognosis, whereas MFP is an inflammatory condition typically responding well to medical or interventional therapies. Some investigators have explored radiomics-based machine learning (ML) models for distinguishing PDAC from MFP. However, systematic evidence supporting the feasibility of these models is insufficient, presenting a notable challenge for clinical application. This study intended to review the diagnostic performance of radiomics-based ML models in differentiating PDAC from MFP, summarize the methodological quality of the included studies, and provide evidence-based guidance for optimizing radiomics-based ML models and advancing their clinical use. PubMed, Embase, Cochrane, and Web of Science were searched for relevant studies up to June 29, 2024. Eligible studies comprised English cohort, case-control, or cross-sectional designs that applied fully developed radiomics-based ML models-including traditional and deep radiomics-to differentiate PDAC from MFP, while also reporting their diagnostic performance. Studies without full text, limited to image segmentation, or insufficient outcome metrics were excluded. Methodological quality was appraised by means of the radiomics quality score. Since the limited applicability of QUADAS-2 in radiomics-based ML studies, the risk of bias was not formally assessed. Pooled sensitivity, specificity, area under the curve of summary receiver operating characteristics (SROC), likelihood ratios, and diagnostic odds ratio were estimated through a bivariate mixed-effects model. Results were presented with forest plots, SROC curves, and Fagan's nomogram. Subgroup analysis was performed to appraise the diagnostic performance of radiomics-based ML models across various imaging modalities, including computed tomography (CT), magnetic resonance imaging, positron emission tomography-CT, and endoscopic ultrasound. This meta-analysis included 24 studies with 14,406 cases, including 7635 PDAC cases. All studies adopted a case-control design, with 5 conducted across multiple centers. Most studies used CT as the primary imaging modality. The radiomics quality score scores ranged from 5 points (14%) to 17 points (47%), with an average score of 9 (25%). The radiomics-based ML models demonstrated high diagnostic performance. Based on the independent validation sets, the pooled sensitivity, specificity, area under the curve of SROC, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.92 (95% CI 0.91-0.94), 0.90 (95% CI 0.85-0.94), 0.94 (95% CI 0.74-0.99), 9.3 (95% CI 6.0-14.2), 0.08 (95% CI 0.07-0.11), and 110 (95% CI 62-194), respectively. Radiomics-based ML models demonstrate high diagnostic accuracy in differentiating PDAC from MFP, underscoring their potential as noninvasive tools for clinical decision-making. Nonetheless, the overall methodological quality was moderate due to limitations in external validation, standardized protocols, and reproducibility. These findings support the promise of radiomics in clinical diagnostics while highlighting the need for more rigorous, multicenter research to enhance model generalizability and clinical applicability.

The Radiological Society of North America has actively promoted artificial intelligence (AI) challenges since 2017. Algorithms emerging from the recent RSNA 2022 Cervical Spine Fracture Detection Challenge demonstrated state-of-the-art performance in the competition's data set, surpassing results from prior publications. However, their performance in real-world clinical practice is not known. As an initial step toward the goal of assessing feasibility of these models in clinical practice, we conducted a generalizability test by using one of the leading algorithms of the competition. The deep learning algorithm was selected due to its performance, portability, and ease of use, and installed locally. One hundred examinations (50 consecutive cervical spine CT scans with at least 1 fracture present and 50 consecutive negative CT scans) from a level 1 trauma center not represented in the competition data set were processed at 6.4 seconds per examination. Ground truth was established based on the radiology report with retrospective confirmation of positive fracture cases. Sensitivity, specificity, F1 score, and area under the curve were calculated. The external validation data set comprised older patients in comparison to the competition set (53.5 ± 21.8 years versus 58 ± 22.0, respectively; <i>P</i> < .05). Sensitivity and specificity were 86% and 70% in the external validation group and 85% and 94% in the competition group, respectively. Fractures misclassified by the convolutional neural networks frequently had features of advanced degenerative disease, subtle nondisplaced fractures not easily identified on the axial plane, and malalignment. The model performed with a similar sensitivity on the test and external data set, suggesting that such a tool could be potentially generalizable as a triage tool in the emergency setting. Discordant factors such as age-associated comorbidities may affect accuracy and specificity of AI models when used in certain populations. Further research should be encouraged to help elucidate the potential contributions and pitfalls of these algorithms in supporting clinical care.

Individuals with Type 2 Diabetes (T2D) are at high risk of subclinical cardiovascular disease (CVD), potentially detectable through retinal alterations. In this single-centre, prospective cohort study, 255 asymptomatic adults with T2D and no prior history of CVD underwent echocardiography, non-contrast coronary computed tomography and cardiovascular magnetic resonance. Retinal photographs were evaluated for diabetic retinopathy grade and microvascular geometric characteristics using deep learning (DL) tools. Associations with cardiac imaging markers of subclinical CVD were explored. Of the participants (aged 64 ± 7 years, 62% males); 200 (78%) had no diabetic retinopathy and 55 (22%) had mild background retinopathy. Groups were well-matched for age, sex, ethnicity, CV risk factors, urine microalbuminuria, and serum natriuretic peptide and high-sensitivity troponin levels. Presence of retinopathy was associated with a greater burden of coronary atherosclerosis (coronary artery calcium score ≥ 100; OR 2.63; 95% CI 1.29–5.36; <i>P</i> = 0.008), more concentric left ventricular remodelling (OR 3.11; 95% CI 1.50–6.45; <i>P</i> = 0.002), and worse global longitudinal strain (OR 2.32; 95% CI 1.18–4.59; <i>P</i> = 0.015), independent of key co-variables. Early diabetic retinopathy is associated with a high burden of coronary atherosclerosis and markers of early heart failure. Routine diabetic eye screening may serve as an effective alternative to currently advocated screening tests for detecting subclinical CVD in T2D, presenting opportunities for earlier detection and intervention. The online version contains supplementary material available at 10.1038/s41598-025-13468-4.

Substance use disorders (SUD) are chronic, relapsing conditions marked by high variability in treatment response and frequent relapse. This variability arises from complex interactions among behavioral, environmental, and biological factors unique to each individual. Precision medicine, which tailors treatment to patient-specific characteristics, offers a promising avenue to address these challenges. This review explores key factors influencing SUD, including severity, comorbidities, drug use motives, polysubstance use, cognitive impairments, and biological and environmental influences. Advanced neuroimaging, such as MRI and PET, enables patient subtyping by identifying altered brain mechanisms, including reward, relief, and cognitive pathways, and striatal dopamine D_2/3 receptor binding. Pharmacogenetic and epigenetic studies uncover how variations in dopaminergic, serotoninergic, and opioidergic systems shape treatment outcomes. Emerging biomarkers, such as neurofilament light chain, offer non-invasive relapse monitoring. Multifactorial models integrating behavioral and neural markers outperform single-factor approaches in predicting treatment success. Machine learning refines these models, while longitudinal and preclinical studies support individualized care. Despite translational hurdles, precision medicine offers transformative potential for improving SUD treatment outcomes.