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Aberrant brain functional connectivity in patients with dysthyroid optic neuropathy: a resting-state fMRI study.

OBJECTIVES: We aimed to investigate the alterations of static and dynamic brain functional connectivity in dysthyroid optic neuropathy (DON) using resting-state functional MRI (rs-fMRI) with the voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC) methods. MATERIALS AND METHODS: Seventy-five thyroid-associated ophthalmopathy (TAO) patients (29 DON and 46 non-DON) and 32 healthy controls (HCs) were prospectively recruited. Static and dynamic VMHC (sVMHC and dVMHC) and DC (sDC and dDC) values were measured and compared among groups. Support-vector machine (SVM) classification

Prediction of Metastasis-Free Survival in Patients with Localized Prostate Adenocarcinoma Using Delta Radiomics from Pre-Treatment PSMA-PET/CT Scans and Dosiomics.

PURPOSE: To develop prognostic models integrating delta radiomics from prostate-specific membrane antigen positron emission tomography/compu … 0.05) were entered into multivariate Cox models, and five-year MFS was classified using a quadratic support vector machine. Dosiomics analysis included 48 patients. Dosiomics features were extracted from the planning target volume receiving 86 Gy and combined with pre-treatment radiomics and clinical variables using the same framework. RESULTS: For delta radiomics, Mod …

Hybrid CNN-ViT Modeling For Predicting Functional Outcome After Ischemic Stroke: A Retrospective Study.

Acute ischemic stroke (AIS) is a major cause of death and long-term disability, and early prediction of functional recovery is important for … istic curve (AUC), sensitivity, specificity, and accuracy. Among the evaluated clinical models, the support vector machine achieved the highest internal test AUC (0.878). The imaging model achieved an AUC of 0.782. The multimodal fusion model achieved the best overall internal performance, with an AUC of 0.885, sensitivity of 0.920, specificity of 0.760, and accuracy of …

Fourier Transform Infrared Spectroscopic Characterization of Aortic Wall Remodeling by Stable Gastric Pentadecapeptide BPC 157 After Unilateral Adrenalectomy in Rats.

Background: No Fourier transform infrared (FTIR) spectroscopy studies have directly evaluated adrenalectomy vessels, the technique's establi … s: FTIR spectra were acquired, preprocessed, and analyzed using principal component analysis (PCA), support vector machine discriminant analysis (SVMDA), and band-specific statistics. BPC 157 (10 ng/kg intragatrically immediately after unilateral adrenalectomy) produced a clear, reproducible separation of aortic spectra from control samples at all time points. The main …

Multimodal DCE-MRI stacking fusion for luminal versus non-luminal classification in breast cancer: A multicenter study with interpretability and ablation analysis.

PURPOSE: To develop and externally validate a multimodal stacking fusion model based on dynamic contrast-enhanced MRI (DCE-MRI) for classify … meta-classifier selected through a grid search over nine candidate model families that identified a support vector machine. Modality contributions were quantified by SHapley Additive exPlanations (SHAP) attribution and leave-one-modality-out ablation experiments. RESULTS: The stacking model achieved AUCs of 0.906, 0.850, and 0.854 on the training, validation, and exter …

Towards automatic home screening of obstructive sleep apnea using combined features from pulse wave amplitude, pulse-to-pulse interval and oxygen desaturation.

PURPOSE: Automatic home OSA screening with pulse oximeters often relies on oxygen saturation (SpO2), which may miss hypopneas associated wit … pulse amplitude variability. PPI features included statistics and heart rate variability measures. Support vector machine classifiers with different combinations of PWA, PPI, and SpO2 features were trained to detect 60-second segments with apnea or hypopnea. Performance was assessed at both per-segment and per-subject levels for identifying AHI ≥ 15. RESULTS: Adding PW …

An Explainable Machine Learning Model for Predicting Short-Term Haemodynamic Changes Post-TIPS With Prognostic Implications.

BACKGROUND AND AIMS: While remeasuring portacaval pressure gradient (PPG) after transjugular intrahepatic portosystemic shunt (TIPS), it provides superior prognostic information, and its clinical utility is limited by invasiveness. We aimed to develop and validate an explainable machine learning (ML) model for predicting short-term PPG changes and improving prognostic value of PPG in cirrhotic patients undergoing TIPS. METHODS: We enrolled 328 and 128 patients (2018-2023) in retrospective training and prospective validation cohorts, respectively. PPG was measured pre-TIPS, immediately (imePPG

Automated Diagnosis of Breast Cancer Using Deep Learning Techniques Applied to Digital Mammography and Magnetic Resonance Images.

UNLABELLED: Introduction:Breast cancer is a global issue impacting women's well-being, highlighting the importance of early detection to imp … ly connected layers to classify 3 classes (normal, benign, and malignant) resulting in 12 features. Support vector machine was employed to categorize images. Classifier performances were evaluated using the confusion matrix, accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating curve (AUC-ROC). RESULTS: Res-Net model achieved th …

Prediction of H2S adsorption capacity of biochar using rigorous machine learning frameworks.

Biochar is a promising, sustainable adsorbent for hydrogen sulfide (H2S) removal, yet its adsorption capacity is governed by complex interactions among material properties, preparation conditions, and operating parameters. In this study, we develop and systematically compare a suite of machine learning (ML) models including Decision Tree, Random Forest, AdaBoost, K-Nearest Neighbors (KNN), Convolutional Neural Network (CNN), Support Vector Regression (SVR), and an Ensemble Learning scheme to predict the H2S adsorption capacity of biochar. The models are trained on 277 experimental data points

Beyond nigrostriatal dopamine transporter loss: whole-brain DAT depletion patterns discriminate PD phenotypes.

PURPOSE: To map whole-brain dopamine transporter (DAT) depletion patterns in Parkinson’s disease (PD) using 11C-CFT PET and evaluate their u … triatal (NS), extranigral (ONS), and whole-brain (WB) regions informed three classification models (Support vector machine, Logistic Regression, Random Forest), evaluated via Receiver Operating Characteristic analysis. RESULTS: 197 patients (mean age 50 ± 10 years; 112 men) were evaluated. Beyond classical nigrostriatal reductions, PD patients exhibited significant DAT …

Impact of automated and manual segmentation errors on knee osteoarthritis classification using MRI-registered data on CT scans.

Knee osteoarthritis (OA) is a prevalent, disabling disease for which early, accurate diagnosis is essential to guide treatment and reduce long-term burden. Machine learning (ML) approaches using quantitative imaging biomarkers show promise for automated OA classification, but their reliability under imperfect image segmentation remains unclear. This study evaluated the robustness of cartilage-based radiodensity and morphological features derived from MRI-registered CT scans against simulated segmentation errors. Manual segmentations of femoral, patellar, lateral tibial, and medial tibial carti

Exploring Endoplasmic Reticulum Stress-Related Genes in Cartilage Defects: Implications for Diagnosis and Therapy.

INTRODUCTION: Cartilage defects (CDs) are orthopedic conditions with limited regenerative potential. This study aimed to identify endoplasmi … -sample gene set enrichment analysis. Diagnostic models were constructed using logistic regression, support vector machine, and least absolute shrinkage and selection operator regression. RESULTS: Twenty ERSRDEGs were identified, with CYBB, ATP6V1A, and TNFRSF12A significantly upregulated in CD samples. GO and KEGG analyses highlighted oxidative stress response and ext …

A novel SE-ResNet architecture for continuous estimation of wrist and hand movements from HD-sEMG.

With the rapid technological advancements in flexible manipulators, it has emerged as a research hot spot to realize a more flexible control … mprovements. Further, the ResNet-32 model was augmented with SE module, named as SE-ResNet model. A support vector machine (SVM) was introduced for comparative analysis. The RMSE of the SE-ResNet model for simultaneous movements can reach 16.48 degrees, which is better than that of SVM and ResNet-32. In brief, our SE-ResNet model exhibits better prediction performance a …

Altered microstructure in end-stage renal disease patients with maintenance hemodialysis: A diffusion kurtosis imaging analysis.

This study investigates the characteristic changes of brain microstructures in end-stage renal disease patients with and without cognitive impairment (CI) and further predicts individual cognitive function in end-stage renal disease patients. In total, 32 healthy subjects, 22 end-stage renal disease patients with CI, and 22 end-stage renal disease patients without CI were included in our study. All subjects completed diffusion kurtosis imaging (DKI) examinations and the Montreal Cognitive Assessment (MoCA) test. Between-group differences in the DKI metrics were analyzed. In addition, correlati

Assessment of Hepatic Fibrosis Through Radiomics Models Utilizing Diffusion-Based Virtual Magnetic Resonance Elastography.

OBJECTIVE: To investigate the potential of the radiomic features derived from the diffusion magnetic resonance imaging (MRI)-based elastogra … echnique with b values of 200 and 1500 s/mm 2 . A total of 3222 radiomic features were extracted. A support vector machine was applied to build a classification model with radiomic features. We compared the results of models constructed by the mean values of MRE, radiomic features of sADC, vMRE, and sADC + vMRE in identifying significant and advanced liver fibrosis. The …

Automatic analysis of speech representations to assess psychological distress.

BACKGROUND: Current mental health diagnostic methods are limited by subjective clinical interpretation. Automatic speech analysis is a promi … ddressed at the response and participant levels using a Fully Connected Neural Network (FCNN) and a Support Vector Machine (SVM), respectively. RESULTS: Prosody at the participant level achieved the highest mean performance (F1-score 0.67 ± 0.07; accuracy 0.64 ± 0.10; AUC 0.65 ± 0.12), followed by participant-level phonation (F1-score 0.59 ± 0.16; accuracy 0.61 ± 0.14; …

Идентификация и проверка генов, связанных со старением, у пациентов с множественной миеломой.

Это исследование было направлено на выявление и проверку связанных со старением генов (ARGs), связанных с множественной миеломой (MM), что способствовало более глубокому пониманию молекулярных механизмов, лежащих в основе этого заболевания. Для изучения функциональных ролей ARG в MM были проведены генной онтологии и Киото-Энциклопедии по обогащению генов и геномов. Потенциальные гены, выявленные с помощью наименьшего абсолютного сокращения и селекционного оператора (LASSO) логистической регрессии и рекурсивного устранения векторных функций (SVM-RFE), были сопоставлены с помощью диаграммы Венна

Совпадение в аннотации оригинала
This study aimed to identify and validate aging-related genes (ARGs) implicated in multiple myeloma (MM), thereby advancing the understandin … identified through least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) were compared using a Venn diagram, which revealed the overlap between the genes identified by the two algorithms. A receiver operating characteristic curve was generated based on the screening results. The ca …

Marker-Less Video Analysis of Infant Movements for Early Identification of Neurodevelopmental Disorders.

Background/Objectives: The early identification of neurodevelopmental disorders (NDDs) in infants is crucial for effective intervention and … at 10 days old: 'Median Velocity', 'Area differing from moving average', and 'Periodicity'. Using a Support Vector Machine (SVM), we achieved an accuracy rate of approximately 85%, a sensitivity of 64%, and a specificity of 100%. We also observed that the disparities in lower limb movements diminished over time points. Furthermore, the tracking accuracy was assessed thr …

Blast cell segmentation and leukemia classification using hybrid Deep Kronecker WideResNet using blood smear images.

Acute Lymphoblastic Leukemia (ALL) is a dangerous form of leukemia, which disturbs the bone marrow and irregular White Blood Cells (WBC) for … ayesian-based optimized convolutional neural network (CNN) for ALL detection (BO-ALLCNN) by 5.91 %, Support vector machine (SVM) by 4.63 %, ResNet-18 combined with SVM by 4.04 %, Residual Convolutional Neural Network (ResNet-152) by 2.27 %, DKN by 1.51 %, and WRN by 0.55 %, respectively.

Radiomics based on preoperative magnetic resonance imaging predict the cell lineages of nonfunctioning pituitary neuroendocrine tumors.

OBJECTIVE: Accurate preoperative predict the cell lineages of non-functioning pituitary neuroendocrine tumors (NFPitNETs) can help neurosurg … r features extracted. Variance threshold method, t-test, and LASSO were used for feature selection. Support vector machine (SVM) and random forest (RF) were used to predict the three-lineages adenomas based on their radiomics and semantic features. Receiver operating characteristic curve-area under the curve (ROC-AUC) analysis was used to assess the model's performance. …