Endometriosis affects approximately 190 million females of reproductive age worldwide. Magnetic Resonance Imaging (MRI) has been recommended as the primary non-invasive diagnostic method for endometriosis. This study presents new female pelvic MRI multicenter datasets for endometriosis and shows the baseline segmentation performance of two auto-segmentation pipelines: the self-configuring nnU-Net and RAovSeg, a custom network. The multi-sequence endometriosis MRI scans from two clinical institutions were collected. A multicenter dataset of 51 subjects with manual labels for multiple pelvic structures from three raters was used to assess interrater agreement. A second single-center dataset of 81 subjects with labels for multiple pelvic structures from one rater was used to develop the ovary auto-segmentation pipelines. Uterus and ovary segmentations are available for all subjects, endometrioma segmentation is available for all subjects where it is detectable in the image. This study highlights the challenges of manual ovary segmentation in endometriosis MRI and emphasizes the need for an auto-segmentation method. The dataset is publicly available for further research in pelvic MRI auto-segmentation to support endometriosis research.

This study aims to develop an Exploratory deep learning method to quantitatively predict hematoma progression (EXPEDITION in short) after intracerebral hemorrhage (ICH). Patients with primary ICH in the basal ganglia or thalamus were retrospectively enrolled, and their baseline non-contrast CT (NCCT) image, CT perfusion (CTP) images, and subsequent re-examining NCCT images from the 2nd to the 8th day after baseline CTP were collected. The subjects who had received three or more re-examining scans were categorized into the test data set, and others were assigned to the training data set. Hematoma volume was estimated by manually outlining the lesion shown on each NCCT scan. Cerebral venous hemodynamic feature was extracted from CTP images. Then, EXPEDITION was trained. The Bland-Altman analysis was used to assess the prediction performance. A total of 126 patients were enrolled initially, and 73 patients were included in the final analysis. They were then categorized into the training data set (58 patients with 93 scans) and the test data set (15 patients with 50 scans). For the test set, the mean difference [mean ±1.96SD] of hematoma volume between the EXPEDITION prediction and the reference is -0.96 [-9.64, +7.71] mL. Specifically, in the test set, the consistency between the true and the predicted volume values was compared, indicating that the EXPEDITION achieved the needed accuracy for quantitative prediction of hematoma progression. An Exploratory deep learning method, EXPEDITION, was proposed to quantitatively predict hematoma progression after primary ICH in basal ganglia or thalamus.

In an extended time window, contrast-based neuroimaging is valuable for treatment selection or prognosis in patients with stroke undergoing reperfusion treatment. However, its immediate availability remains limited, especially in resource-constrained regions. We sought to evaluate the association of initial core volume (ICV) measured on non-contrast computed tomography (NCCT) by a deep learning-based algorithm with outcomes in patients undergoing reperfusion treatment. Consecutive patients who received reperfusion treatments were collected from a prospectively maintained registry in three comprehensive stroke centers from January 2021 to May 2024. ICV on admission was estimated on NCCT by a previously validated deep learning algorithm (Methinks). Outcomes of interest included favorable outcome (modified Rankin Scale score 0-2 at 90 days) and symptomatic intracranial hemorrhage (sICH). The study comprised 658 patients of mean (SD) age 72.7 (14.4) years and median (IQR) baseline National Institutes of Health Stroke Scale (NIHSS) score of 12 (6-19). Primary endovascular treatment was performed in 53.7% of patients and 24.9% received IV thrombolysis only. Patients with favorable outcomes had a lower mean (SD) automated ICV (aICV; 12.9 (26.9) mL vs 34.9 (40) mL, P<0.001). Lower aICV was associated with a favorable outcome (adjusted OR 0.983 (95% CI 0.975 to 0.992), P<0.001) after adjusted logistic regression. For every 1 mL increase in aICV, the odds of a favorable outcome decreased by 1.7%. Patients who experienced sICH had a higher mean (SD) aICV (47.8 (61.1) mL vs 20.5 (32) mL, P=0.001). Higher aICV was independently associated with sICH (adjusted OR 1.014 (95% CI 1.004 to 1.025), P=0.009) after adjusted logistic regression. For every 1 mL increase in aICV, the odds of sICH increased by 1.4%. In patients with stroke undergoing reperfusion therapy, aICV assessment on NCCT predicts long-term outcomes and sICH. Further studies determining the potential role of aICV assessment to safely expand and simplify reperfusion therapies based on AI interpretation of NCCT may be justified.

&#xD;Clinical drivers for real-time head and neck (H&N) tumor tracking during radiation therapy (RT) are accounting for motion caused by changes to the immobilization mask fit, and to reduce mask-related patient distress by replacing the masks with patient motion management methods. The purpose of this paper is to investigate a deep learning-based method to segment H&N tumors in patient kilovoltage (kV) x-ray images to enable real-time H&N tumor tracking during RT.&#xD;Approach: An ethics-approved clinical study collected data from 17 H&N cancer patients undergoing conventional H&N RT. For each patient, personalized conditional Generative Adversarial Networks (cGANs) were trained to segment H&N tumors in kV x-ray images. Network training data were derived from each patient's planning CT and contoured gross tumor volumes (GTV). For each training epoch, the planning CT and GTV were deformed and forward projected to create the training dataset. The testing data consisted of kV x-ray images used to reconstruct the pre-treatment CBCT volume for the first, middle and end fractions. The ground truth tumor locations were derived by deformably registering the planning CT to the pre-treatment CBCT and then deforming the GTV and forward projecting the deformed GTV. The generated cGAN segmentations were compared to ground truth tumor segmentations using the absolute magnitude of the centroid error and the mean surface distance (MSD) metrics.&#xD;Main Results:&#xD;The centroid error for the nasopharynx (n=4), oropharynx (n=9) and larynx (n=4) patients was 1.5±0.9mm, 2.4±1.6mm, 3.5±2.2mm respectively and the MSD was 1.5±0.3mm, 1.9±0.9mm and 2.3±1.0mm respectively. There was a weak correlation between the centroid error and the LR tumor location (r=0.41), which was higher for oropharynx patients (r=0.77).&#xD;Significance: The paper reports on markerless H&N tumor detection accuracy using kV images. Accurate tracking of H&N tumors can enable more precise RT leading to mask-free RT enabling better patient outcomes.&#xD.

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.&#xD;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.&#xD;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.&#xD;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.