Intracranial dural arteriovenous fistula (DAVF) is an acquired vascular condition involving abnormal connections between dural arteries and veins without intervening capillary beds. Cognitive impairment is a common symptom in DAVFs, often linked to disrupted brain network connectivity. Resting-state functional MRI (rsfMRI) allows for examining functional connectivity through blood oxygenation level dependent (BOLD) signal analysis. However, rsfMRI signals exhibit fractal behavior that complicates connectivity analysis. This study explores nonfractal connectivity as a potential biomarker for cognitive impairment in DAVF patients by isolating short-memory components in BOLD signals.
Method: 50 DAVF patients and 50 healthy controls underwent neuropsychological assessments and rsfMRI. Preprocessed BOLD signals were decomposed using wavelet transforms to isolate fractal and nonfractal components. Connectivity matrices based on fractal, nonfractal, and Pearson correlation components were generated and used as features for classification. Machine learning classifiers, including SVM and decision trees, were optimized via cross-validation in MATLAB, with performance assessed by accuracy, sensitivity, specificity, and AUC.
Results: Nonfractal connectivity outperformed fractal and Pearson correlation measures, achieving a classification accuracy of 89.82% using SVM, with high sensitivity (86.54%), specificity (92.4%), and an AUC of 0.96. Nonfractal connectivity effectively differentiated cognitive impairment in DAVFs, offering a clearer depiction of neural activity by reducing the influence of fractal patterns.
Conclusion: This study suggests that nonfractal connectivity is a promising biomarker for assessing cognitive impairment in DAVF patients, potentially supporting early diagnosis and intervention. While nonfractal analysis showed promising classification accuracy, further research with larger datasets is needed to validate these findings and explore applicability in other neurological conditions.&#xD.

Pulmonary diseases have become one of the main reasons for people's health decline, impacting millions of people worldwide. Rapid advancement of deep learning has significantly impacted medical image analysis by improving diagnostic accuracy and efficiency. Timely and precise diagnosis of these diseases proves to be invaluable for effective treatment procedures. Chest X-rays (CXR) perform a pivotal role in diagnosing various respiratory diseases by offering valuable insights into the chest and lung regions. This study puts forth a hybrid approach for classifying CXR images into four classes namely COVID-19, tuberculosis, pneumonia, and normal (healthy) cases. The presented method integrates a machine learning method, Support Vector Machine (SVM), with a pre-trained deep learning model for improved classification accuracy and reduced training time. Data from a number of public sources was used in this study, which represents a wide range of demographics. Class weights were implemented during training to balance the contribution of each class in order to address the class imbalance. Several pre-trained architectures, namely DenseNet, MobileNet, EfficientNetB0, and EfficientNetB3, have been investigated, and their performance was evaluated. Since MobileNet achieved the best classification accuracy of 94%, it was opted for the hybrid model, which combines MobileNet with SVM classifier, increasing the accuracy to 97%. The results suggest that this approach is reliable and holds great promise for clinical applications.&#xD.

Kidney stones, a prevalent urological condition, associated with acute pain requires prompt and precise diagnosis for optimal therapeutic intervention. While computed tomography (CT) imaging remains the definitive diagnostic modality, manual interpretation of these images is a labor-intensive and error-prone process. This research endeavors to introduce Artificial Intelligence based methodology for automated detection and classification of renal calculi within the CT images. To identify the CT images with kidney stones, a comprehensive exploration of various ML and DL architectures, along with rigorous experimentation with diverse hyperparameters, was undertaken to refine the model's performance. The proposed workflow involves two key stages: (1) precise segmentation of pathological regions of interest (ROIs) using DL algorithms, and (2) binary classification of the segmented ROIs using both ML and DL models. The SwinTResNet model, optimized using the RMSProp algorithm with a learning rate of 0.0001, demonstrated optimal performance, achieving a training accuracy of 97.27% and a validation accuracy of 96.16% in the segmentation task. The Vision Transformer (ViT) architecture, when coupled with the ADAM optimizer and a learning rate of 0.0001, exhibited robust convergence and consistently achieved the highest performance metrics. Specifically, the model attained a peak training accuracy of 96.63% and a validation accuracy of 95.67%. The results demonstrate the potential of this integrated framework to enhance diagnostic accuracy and efficiency, thereby supporting improved clinical decision-making in the management of kidney stones.

Recent advances have applied diffusion model (DM) to magnetic resonance imaging (MRI) reconstruction, demonstrating impressive performance. However, current DM-based MRI reconstruction methods suffer from two critical limitations. First, they model image features at the pixel-level and require numerous iterations for the final image reconstruction, leading to high computational costs. Second, most of these methods operate in the image domain, which cannot avoid the introduction of secondary artifacts. To address these challenges, we propose a novel latent-k-space refinement diffusion model (LRDM) for MRI reconstruction. Specifically, we encode the original k-space data into a highly compact latent space to capture the primary features for accelerated acquisition and apply DM in the low-dimensional latent-k-space to generate prior knowledge. The compact latent space allows the DM to require only 4 iterations to generate accurate priors. To compensate for the inevitable loss of detail during latent-k-space diffusion, we incorporate an additional diffusion model focused exclusively on refining high-frequency structures and features. The results from both models are then decoded and combined to obtain the final reconstructed image. Experimental results demonstrate that the proposed method significantly reduces reconstruction time while delivering comparable image reconstruction quality to conventional DM-based approaches.&#xD.

Psychological resilience is influenced by both psychological and biological factors. However, the potential of using DNA methylation (DNAm) probes and brain imaging variables to predict psychological resilience remains unclear. This study aimed to investigate DNAm, structural magnetic resonance imaging (sMRI), and diffusion tensor imaging (DTI) as biomarkers for psychological resilience. Additionally, we evaluated the ability of epigenetic and imaging markers to distinguish between individuals with low and high resilience using machine learning algorithms. A total of 130 young adults assessed with the Connor-Davidson Resilience Scale (CD-RISC) were divided into high and low psychological resilience groups. We utilized two feature selection algorithms, the Boruta and variable selection using random forest (varSelRF), to identify important variables based on nine for DNAm, sixty-eight for gray matter volume (GMV) measured with sMRI, and fifty-four diffusion indices of DTI. We constructed machine learning models to identify low resilience individuals using the selected variables. The study identified thirteen variables (five DNAm, five GMV, and three DTI diffusion indices) from feature selection methods. We utilized the selected variables based on 10-fold cross validation using four machine learning models for low resilience (AUC = 0.77-0.82). In interaction analysis, we identified cg03013609 had a stronger interaction with cg17682313 and the rostral middle frontal gyrus in the right hemisphere for psychological resilience. Our findings supported the concept that DNAm, sMRI, and DTI signatures can identify individuals with low psychological resilience. These combined epigenetic imaging markers demonstrated high discriminative abilities for low psychological resilience using machine learning models.

To develop a standardized, real-time feedback system for monitoring urinary stone fragmentation during shockwave lithotripsy (SWL), thereby optimizing treatment efficacy and minimizing patient risk. A two-pronged approach was implemented to quantify stone fragmentation in C-arm X-ray images. First, the initial pre-treatment stone image was compared to subsequent images to measure stone area loss. Second, a Convolutional Neural Network (CNN) was trained to estimate the probability that an image contains a urinary stone. These two criteria were integrated to create a real-time signaling system capable of evaluating shockwave efficacy during SWL. The system was developed using data from 522 shockwave treatments encompassing 4,057 C-arm X-ray images. The combined area-loss metric and CNN output enabled consistent real-time assessment of stone fragmentation, providing actionable feedback to guide SWL in diverse clinical contexts. The proposed system offers a novel and reliable method for monitoring of urinary stone fragmentation during SWL. By helping to balance treatment efficacy with patient safety, it holds significant promise for semi-automated SWL platforms, particularly in resource-limited or remote environments such as arid regions and extended space missions.

To develop two distinct models for predicting microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC) based on habitat imaging, and to integrate these models for prognosis assessment. In this multicenter retrospective study, patients from two different institutions were enrolled and categorized for MVI (n=295) and VETC (n=276) prediction. Tumor and peritumoral regions on hepatobiliary phase images were segmented into subregions, from which all relevant features were extracted. The MVI and VETC predictive models were constructed by analyzing these features using various machine learning algorithms, and classifying patients into high-risk and low-risk groups. Cox regression analysis was utilized to identify risk factors for early recurrence. The MVI and VETC prediction models demonstrated excellent performance in both the training and external validation cohorts (AUC: 0.961 and 0.838 for MVI; 0.931 and 0.820 for VETC). Based on model predictions, patients were classified into high-risk group (High-risk MVI/ High-risk VETC), medium-risk group (High-risk MVI/Low-risk VETC or Low-risk MVI/High-risk VETC), and low-risk group (Low-risk MVI/Low-risk VETC). Multivariable Cox regression analysis revealed that risk group, number of tumors, and gender were independent predictors of early recurrence. Models based on habitat imaging can be used for the preoperative, noninvasive prediction of MVI and VETC, offering valuable stratification and diagnostic insights for HCC patients.

This study aims to explore a novel approach for predicting cervical lymph node metastasis (CLNM) and pathological subtypes in oral squamous cell carcinoma (OSCC) by comparing deep learning (DL) and habitat analysis models based on contrast-enhanced CT (CECT). A retrospective analysis was conducted using CECT images from patients diagnosed with OSCC via paraffin pathology at the Second Affiliated Hospital of Dalian Medical University. All patients underwent primary tumor resection and cervical lymph node dissection, with a total of 132 cases included. A DL model was developed by analysing regions of interest (ROIs) in the CECT images using a convolutional neural network (CNN). For habitat analysis, the ROI images were segmented into 3 regions using K-means clustering, and features were selected through a fully connected neural network (FCNN) to build the model. A separate clinical model was constructed based on nine clinical features, including age, gender, and tumor location. Using LNM and pathological subtypes as endpoints, the predictive performance of the clinical model, DL model, habitat analysis model, and a combined clinical + habitat model was evaluated using confusion matrices and receiver operating characteristic (ROC) curves. For LNM prediction, the combined clinical + habitat model achieved an area under the ROC curve (AUC) of 0.97. For pathological subtype prediction, the AUC was 0.96. The DL model yielded an AUC of 0.83 for LNM prediction and 0.91 for pathological subtype classification. The clinical model alone achieved an AUC of 0.94 for predicting LNM. The integrated habitat-clinical model demonstrates improved predictive performance. Combining habitat analysis with clinical features offers a promising approach for the prediction of oral cancer. The habitat-clinical integrated model may assist clinicians in performing accurate preoperative prognostic assessments in patients with oral cancer.

A significant obstacle in brain tumor treatment planning is determining the tumor's actual size. Magnetic resonance imaging (MRI) is one of the first-line brain tumor diagnosis. It takes a lot of effort and mostly depends on the operator's experience to manually separate the size of a brain tumor from 3D MRI volumes. Machine learning has been vastly enhanced by deep learning and computer-aided tumor detection methods. This study proposes to investigate the architecture of object detectors, specifically focusing on search efficiency. In order to provide more specificity, our goal is to effectively explore the Feature Pyramid Network (FPN) and prediction head of a straightforward anchor-free object detector called DEEP Q-NAS. The study utilized the BraTS 2021 dataset which includes multi-parametric magnetic resonance imaging (mpMRI) scans. The architecture we found outperforms the latest object detection models (like Fast R-CNN, YOLOv7, and YOLOv8) by 2.2 to 7 points with average precision (AP) on the MS COCO 2017 dataset. It has a similar level of complexity and less memory usage, which shows how effective our proposed NAS is for object detection. The DEEP Q-NAS with ResNeXt-152 model demonstrates the highest level of detection accuracy, achieving a rate of 99%.

This investigation delves into the diagnosis of COVID-19, using X-ray images generated by way of an effective deep learning model. In terms of assessing the COVID-19 diagnosis learning model, the methods currently employed tend to focus on the accuracy rate level, while neglecting several significant assessment parameters. These parameters, which include precision, sensitivity and specificity, significantly, F1-score, and ROC-AUC influence the performance level of the model. In this paper, we have improved the InceptionResNet and called Enhanced InceptionResNet with restructured parameters termed, "Enhanced InceptionResNet," which incorporates depth-wise separable convolutions to enhance the efficiency of feature extraction and minimize the consumption of computational resources. For this investigation, three residual network (ResNet) models, namely Res- Net, InceptionResNet model, and the Enhanced InceptionResNet with restructured parameters, were employed for a medical image classification assignment. The performance of each model was evaluated on a balanced dataset of 2600 X-ray images. The models were subsequently assessed for accuracy and loss, as well subjected to a confusion matrix analysis. The Enhanced InceptionResNet consistently outperformed ResNet and InceptionResNet in terms of validation and testing accuracy, recall, precision, F1-score, and ROC-AUC demonstrating its superior capacity for identifying pertinent information in the data. In the context of validation and testing accuracy, our Enhanced InceptionRes- Net repeatedly proved to be more reliable than ResNet, an indication of the former's capacity for the efficient identification of pertinent information in the data (99.0% and 98.35%, respectively), suggesting enhanced feature extraction capabilities. The Enhanced InceptionResNet excelled in COVID-19 diagnosis from chest X-rays, surpassing ResNet and Default InceptionResNet in accuracy, precision, and sensitivity. Despite computational demands, it shows promise for medical image classification. Future work should leverage larger datasets, cloud platforms, and hyperparameter optimisation to improve performance, especially for distinguishing normal and pneumonia cases.