Cognitive impairment (CI) in people with MS (pwMS) has complex pathophysiology. Neuropsychological testing (NPT) can be helpful, but interpretation may be challenging for clinicians. Thalamic atrophy (TA) has shown correlation for both neurodegeneration and CI. Leverage machine learning methods to link CI and longitudinal neuroimaging biomarkers. Retrospective review of adult pwMS with NPT and ≥2 brain MRIs. Quantitative MRI regional change rates were calculated using mixed effects models. Participants were divided into training and validation cohorts. K-means clustering was done based on first and second NPT principal components (PC1 and PC2). MRI change rates were compared between clusters. 112 participants were included (mean age 48 years, 71 % female, 80 % relapsing remitting). Processing speed and memory were the major contributors to PC1. We identified two clusters based on PC1, one with significantly more TA in both training and validation cohorts (p = 0.035; p = 0.002) and similar rates of change in all other quantitative MRI measures. The most important contributors to PC1 included measures of processing speed (SDMT/WAIS Coding) and memory (List Learning/BVMT immediate and delayed recall). This clustering method identified a profile of NPT results strongly linked to and possibly driven by TA. These results confirm validity of previously established findings using more advanced analyses in addition to offering novel insights into NPT dimensionality reduction.

Breast cancer is a major cause of mortality among women globally. While mammography remains the gold standard for detection, its interpretation is often limited by radiologist variability and the challenge of differentiating benign and malignant lesions. The study explores the use of Auto- Sklearn, an automated machine learning (AutoML) framework, for breast tumor classification based on mammographic radiomic features. 244 mammographic images were enhanced using Contrast Limited Adaptive Histogram Equalization (CLAHE) and segmented with Active Contour Method (ACM). Thirty-seven radiomic features, including first-order statistics, Gray-Level Co-occurance Matrix (GLCM) texture and shape features were extracted and standardized. Auto-Sklearn was employed to automate model selection, hyperparameter tuning and ensemble construction. The dataset was divided into 80% training and 20% testing set. The initial Auto-Sklearn model achieved an 88.71% accuracy on the training set and 55.10% on the testing sets. After the resampling strategy was applied, the accuracy for the training set and testing set increased to 95.26% and 76.16%, respectively. The Receiver Operating Curve and Area Under Curve (ROC-AUC) for the standard and resampling strategy of Auto-Sklearn were 0.660 and 0.840, outperforming conventional models, demonstrating its efficiency in automating radiomic classification tasks. The findings underscore Auto-Sklearn's ability to automate and enhance tumor classification performance using handcrafted radiomic features. Limitations include dataset size and absence of clinical metadata. This study highlights the application of Auto-Sklearn as a scalable, automated and clinically relevant tool for breast cancer classification using mammographic radiomics.

The rapid development of artificial intelligence has driven smart health with next-generation wireless communication technologies, stimulating exciting applications in remote diagnosis and intervention. To enable a timely and effective response for remote healthcare, efficient transmission of medical data through noisy channels with limited bandwidth emerges as a critical challenge. In this work, we propose a novel diffusion-based semantic communication framework, namely DiSC-Med, for the medical image transmission, where medical-enhanced compression and denoising blocks are developed for bandwidth efficiency and robustness, respectively. Unlike conventional pixel-wise communication framework, our proposed DiSC-Med is able to capture the key semantic information and achieve superior reconstruction performance with ultra-high bandwidth efficiency against noisy channels. Extensive experiments on real-world medical datasets validate the effectiveness of our framework, demonstrating its potential for robust and efficient telehealth applications.

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.

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.

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.

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.

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.

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 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.