Ovarian tumor, as a common gynecological disease, can rapidly deteriorate into serious health crises when undetected early, thus posing significant threats to the health of women. Deep neural networks have the potential to identify ovarian tumors, thereby reducing mortality rates, but limited public datasets hinder its progress. To address this gap, we introduce a vital ovarian tumor pathological recognition dataset called \textbf{ViTaL} that contains \textbf{V}isual, \textbf{T}abular and \textbf{L}inguistic modality data of 496 patients across six pathological categories. The ViTaL dataset comprises three subsets corresponding to different patient data modalities: visual data from 2216 two-dimensional ultrasound images, tabular data from medical examinations of 496 patients, and linguistic data from ultrasound reports of 496 patients. It is insufficient to merely distinguish between benign and malignant ovarian tumors in clinical practice. To enable multi-pathology classification of ovarian tumor, we propose a ViTaL-Net based on the Triplet Hierarchical Offset Attention Mechanism (THOAM) to minimize the loss incurred during feature fusion of multi-modal data. This mechanism could effectively enhance the relevance and complementarity between information from different modalities. ViTaL-Net serves as a benchmark for the task of multi-pathology, multi-modality classification of ovarian tumors. In our comprehensive experiments, the proposed method exhibited satisfactory performance, achieving accuracies exceeding 90\% on the two most common pathological types of ovarian tumor and an overall performance of 85\%. Our dataset and code are available at https://github.com/GGbond-study/vitalnet.

Early recurrence (ER) following curative-intent surgery remains a major obstacle to improving long-term outcomes in patients with pancreatic cancer (PC). The accurate preoperative prediction of ER could significantly aid clinical decision-making and guide postoperative management. A retrospective cohort of 493 patients with histologically confirmed PC who underwent resection was analyzed. Contrast-enhanced computed tomography (CT) images were used for tumor segmentation, followed by radiomics and deep learning feature extraction. In total, four distinct feature selection algorithms were employed. Predictive models were constructed using random forest (RF) and support vector machine (SVM) classifiers. The model performance was evaluated by the area under the receiver operating characteristic curve (AUC). A comprehensive nomogram integrating feature scores and clinical factors was developed and validated. Among all of the constructed models, the Inte-SVM demonstrated superior classification performance. The nomogram, incorporating the Inte-feature score, CT-assessed lymph node status, and carbohydrate antigen 19-9 (CA19-9), yielded excellent predictive accuracy in the validation cohort (AUC = 0.920). Calibration curves showed strong agreement between predicted and observed outcomes, and decision curve analysis confirmed the clinical utility of the nomogram. A CT-based deep learning radiomics nomogram enabled the accurate preoperative prediction of early recurrence in patients with pancreatic cancer. This model may serve as a valuable tool to assist clinicians in tailoring postoperative strategies and promoting personalized therapeutic approaches.

Background and AimsTransthoracic echocardiography (TTE) is a widely available tool for diagnosing and managing heart failure but has limited predictive value for survival. Cardiopulmonary exercise test (CPET) performance strongly correlates with survival in heart failure patients but is less accessible. We sought to develop an artificial intelligence (AI) algorithm using TTE and electronic medical records to predict CPET peak oxygen consumption (peak VO2) [≤] 14 mL/kg/min. MethodsAn AI model was trained to predict peak VO2 [≤] 14 mL/kg/min from TTE images, structured TTE reports, demographics, medications, labs, and vitals. The training set included patients with a TTE within 6 months of a CPET. Performance was retrospectively tested in a held-out group from the development cohort and an external validation cohort. Results1,127 CPET studies paired with concomitant TTE were identified. The best performance was achieved by using all components (TTE images, all structured clinical data). The model performed well at predicting a peak VO2 [≤] 14 mL/kg/min, with an AUROC of 0.84 (development cohort) and 0.80 (external validation cohort). It performed consistently well using higher ([≤] 18 mL/kg/min) and lower ([≤] 12 mL/kg/min) cut-offs. ConclusionsThis multimodal AI model effectively categorized patients into low and high risk predicted peak VO2, demonstrating the potential to identify previously unrecognized patients in need of advanced heart failure therapies where CPET is not available.

Fractional anisotropy (FA) derived from diffusion MRI is a widely used marker of white matter (WM) integrity. However, conventional FA based genetic studies focus on phenotypes representing tract- or atlas-defined averages, which may oversimplify spatial patterns of WM integrity and thus limiting the genetic discovery. Here, we proposed a deep learning-based framework, termed unsupervised deep representation of white matter (UDR-WM), to extract brain-wide FA features-referred to as UDIP-FA, that capture distributed microstructural variation without prior anatomical assumptions. UDIP-FAs exhibit enhanced sensitivity to aging and substantially higher SNP-based heritability compared to traditional FA phenotypes ( <i>P</i> < 2.20e-16, Mann-Whitney U test, mean h ² = 50.81%). Through multivariate GWAS, we identified 939 significant lead SNPs in 586 loci, mapped to 3480 genes, dubbed UDIP-FA related genes (UFAGs). UFAGs are overexpressed in glial cells, particularly in astrocytes and oligodendrocytes (Bonferroni-corrected <i>P <</i> 2e-6, Wald Test), and show strong overlap with risk gene sets for schizophrenia and Parkinson disease (Bonferroni-corrected P < 7.06e-3, Fisher exact test). UDIP-FAs are genetically correlated with multiple brain disorders and cognitive traits, including fluid intelligence and reaction time, and are associated with polygenic risk for bone mineral density. Network analyses reveal that UFAGs form disease-enriched modules across protein-protein interaction and co-expression networks, implicating core pathways in myelination and axonal structure. Notably, several UFAGs, including <i>ACHE</i> and <i>ALDH2</i> , are targets of existing neuropsychiatric drugs. Together, our findings establish UDIP-FA as a biologically and clinically informative brain phenotype, enabling high-resolution dissection of white matter genetic architecture and its genetic links to complex brain traits.

Multiple Sclerosis (MS) is a chronic neurological disorder affecting millions worldwide. Early detection is vital to prevent long-term disability. Magnetic Resonance Imaging (MRI) plays a crucial role in MS diagnosis, yet differentiating MS lesions from other brain anomalies remains a complex challenge. To develop and evaluate a novel deep learning framework-2DRK-MSCAN-for the early and accurate detection of MS lesions using MRI data. The proposed approach is validated using three publicly available MRI-based brain tumor datasets and comprises three main stages. First, Gradient Domain Guided Filtering (GDGF) is applied during pre-processing to enhance image quality. Next, an EfficientNetV2L backbone embedded within a U-shaped encoder-decoder architecture facilitates precise segmentation and rich feature extraction. Finally, classification of MS lesions is performed using the 2DRK-MSCAN model, which incorporates deep diffusion residual kernels and multiscale snake convolutional attention mechanisms to improve detection accuracy and robustness. The proposed framework achieved 99.9% accuracy in cross-validation experiments, demonstrating its capability to distinguish MS lesions from other anomalies with high precision. The 2DRK-MSCAN framework offers a reliable and effective solution for early MS detection using MRI. While clinical validation is ongoing, the method shows promising potential for aiding timely intervention and improving patient care.

The aim of this study was to evaluate human inter-reader agreement of parameters included in PGMI (perfect-good-moderate-inadequate) classification of screening mammograms and explore the role of artificial intelligence (AI) as an alternative reader. Five radiographers from three European countries independently performed a PGMI assessment of 520 anonymized mammography screening examinations randomly selected from representative subsets from 13 imaging centres within two European countries. As a sixth reader, a dedicated AI software was used. Accuracy, Cohen's Kappa, and confusion matrices were calculated to compare the predictions of the software against the individual assessment of the readers, as well as potential discrepancies between them. A questionnaire and a personality test were used to better understand the decision-making processes of the human readers. Significant inter-reader variability among human readers with poor to moderate agreement (κ = -0.018 to κ = 0.41) was observed, with some showing more homogenous interpretations of single features and overall quality than others. In comparison, the software surpassed human inter-reader agreement in detecting glandular tissue cuts, mammilla deviation, pectoral muscle detection, and pectoral angle measurement, while remaining features and overall image quality exhibited comparable performance to human assessment. Notably, human inter-reader disagreement of PGMI assessment in mammography is considerably high. AI software may already reliably categorize quality. Its potential for standardization and immediate feedback to achieve and monitor high levels of quality in screening programs needs further attention and should be included in future approaches. AI has promising potential for automated assessment of diagnostic image quality. Faster, more representative and more objective feedback may support radiographers in their quality management processes. Direct transformation of common PGMI workflows into an AI algorithm could be challenging.

Osteoporosis is a chronic disease characterized by a progressive decline in bone density and quality, leading to increased bone fragility and a higher susceptibility to fractures, even in response to minimal trauma. Osteoporotic fractures represent a major source of morbidity and mortality among postmenopausal women. This condition poses both clinical and societal challenges, as its consequences include a significant reduction in quality of life, prolonged dependency, and a substantial increase in healthcare costs. Therefore, the development of reliable tools for predicting fracture risk is essential for the effective management of affected patients. In this study, we developed a predictive model based on the Random Forest (RF) algorithm for risk stratification of fragility fractures, integrating clinical, demographic, and imaging variables derived from dual-energy X-ray absorptiometry (DXA) and 3D modeling. Two independent cohorts were analyzed: the HURH cohort and the Camargo cohort, enabling both internal and external validation of the model. The results showed that the RF model consistently outperformed other classification algorithms, including k-nearest neighbors (KNN), support vector machines (SVM), decision trees (DT), and Gaussian naive Bayes (GNB), demonstrating high accuracy, sensitivity, specificity, area under the ROC curve (AUC), and Matthews correlation coefficient (MCC). Additionally, variable importance analysis highlighted that previous fracture history, parathyroid hormone (PTH) levels, and lumbar spine T-score, along with other densitometric parameters, were key predictors of fracture risk. These findings suggest that the integration of advanced machine learning techniques with clinical and imaging data can optimize early identification of high-risk patients, enabling personalized preventive strategies and improving the clinical management of osteoporosis.

This study aims to enhance personalized medical assessments and the early detection of knee-related pathologies by examining the relationship between knee morphology and demographic factors such as age, gender, and body mass index. Additionally, gender-specific reference values for knee morphological features will be determined using explainable artificial intelligence (XAI). A retrospective analysis was conducted on the MRI data of 500 healthy knees aged 20-40 years. The study included various knee morphological features such as Distal Femoral Width (DFW), Lateral Femoral Condyler Width (LFCW), Intercondylar Femoral Width (IFW), Anterior Cruciate Ligament Width (ACLW), and Anterior Cruciate Ligament Length (ACLL). Machine learning models, including Decision Trees, Random Forests, Light Gradient Boosting, Multilayer Perceptron, and Support Vector Machines, were employed to predict gender based on these features. The SHapley Additive exPlanation was used to analyze feature importance. The learning models demonstrated high classification performance, with 83.2% (±5.15) for classification of clusters based on morphological feature and 88.06% (±4.8) for gender classification. These results validated that the strong correlation between knee morphology and gender. The study found that DFW is the most significant feature for gender prediction, with values below 78-79 mm range indicating females and values above this range indicating males. LFCW, IFW, ACLW, and ACLL also showed significant gender-based differences. The findings establish gender-specific reference values for knee morphological features, highlighting the impact of gender on knee morphology. These reference values can improve the accuracy of diagnoses and treatment plans tailored to each gender, enhancing personalized medical care.

In China, there is a lack of standardised clinical imaging databases for multidimensional evaluation of cardiopulmonary diseases. To address this gap, this study protocol launched a project to build a clinical imaging technology integration and a multicentre database for early warning and stratification of cardiopulmonary dysfunction in the elderly. This study employs a cross-sectional design, enrolling over 6000 elderly participants from five regions across China to evaluate cardiopulmonary function and related diseases. Based on clinical criteria, participants are categorized into three groups: a healthy cardiopulmonary function group, a functional decrease group and an established cardiopulmonary diseases group. All subjects will undergo comprehensive assessments including chest CT scans, echocardiography, and laboratory examinations. Additionally, at least 50 subjects will undergo cardiopulmonary exercise testing (CPET). By leveraging artificial intelligence technology, multimodal data will be integrated to establish reference ranges for cardiopulmonary function in the elderly population, as well as to develop early-warning models and severity grading standard models. The study has been approved by the local ethics committee of Shanghai Changzheng Hospital (approval number: 2022SL069A). All the participants will sign the informed consent. The results will be disseminated through peer-reviewed publications and conferences.

Knee osteoarthritis (KOA) is a progressive degenerative joint disease and a leading cause of disability worldwide. Manual diagnosis of KOA from X-ray images is subjective and prone to inter- and intra-observer variability, making early detection challenging. While deep learning (DL)-based models offer automation, they often require large labeled datasets, lack interpretability, and do not provide quantitative feature measurements. Our study presents an automated KOA severity assessment system that integrates a pretrained DL model with image processing techniques to extract and quantify key KOA imaging biomarkers. The pipeline includes contrast limited adaptive histogram equalization (CLAHE) for contrast enhancement, DexiNed-based edge extraction, and thresholding for noise reduction. We design customized algorithms that automatically detect and quantify joint space narrowing (JSN) and osteophytes from the extracted edges. The proposed model quantitatively assesses JSN and finds the number of intercondylar osteophytes, contributing to severity classification. The system achieves accuracies of 88% for JSN detection, 80% for osteophyte identification, and 73% for KOA classification. Its key strength lies in eliminating the need for any expensive training process and, consequently, the dependency on labeled data except for validation. Additionally, it provides quantitative data that can support classification in other OA grading frameworks.