BackgroundCognitive dysfunction often co-occurs with psychopathology. Advances in neuroimaging and machine learning have led to neural indicators that predict individual differences in cognition with reasonable performance. We examined whether these neural indicators explain the relationship between cognition and mental health in the UK Biobank cohort (n > 14000). MethodsUsing machine learning, we quantified the covariation between general cognition and 133 mental health indices and derived neural indicators of cognition from 72 neuroimaging phenotypes across diffusion-weighted MRI (dwMRI), resting-state functional MRI (rsMRI), and structural MRI (sMRI). With commonality analyses, we investigated how much of the cognition-mental health covariation is captured by each neural indicator and neural indicators combined within and across MRI modalities. ResultsThe predictive association between mental health and cognition was at out-of-sample r = 0.3. Neuroimaging phenotypes captured 2.1% to 25.8% of the cognition-mental health covariation. The highest proportion of variance explained by dwMRI was attributed to the number of streamlines connecting cortical regions (19.3%), by rsMRI through functional connectivity between 55 large-scale networks (25.8%), and by sMRI via the volumetric characteristics of subcortical structures (21.8%). Combining neuroimaging phenotypes within modalities improved the explanation to 25.5% for dwMRI, 29.8% for rsMRI, and 31.6% for sMRI, and combining them across all MRI modalities enhanced the explanation to 48%. ConclusionsWe present an integrated approach to derive multimodal MRI markers of cognition that can be transdiagnostically linked to psychopathology. This demonstrates that the predictive ability of neural indicators extends beyond the prediction of cognition itself, enabling us to capture the cognition-mental health covariation.

This study compares the classification accuracy of novel transformer-based deep learning models (ViT and BEiT) on brain MRIs of gliomas and meningiomas through a feature-driven approach. Meta's Segment Anything Model was used for semi-automatic segmentation, therefore proposing a total neural network-based workflow for this classification task. ViT and BEiT models were finetuned to a publicly available brain MRI dataset. Gliomas/meningiomas cases (625/507) were used for training and 520 cases (260/260; gliomas/meningiomas) for testing. The extracted deep radiomic features from ViT and BEiT underwent normalization, dimensionality reduction based on the Pearson correlation coefficient (PCC), and feature selection using analysis of variance (ANOVA). A multi-layer perceptron (MLP) with 1 hidden layer, 100 units, rectified linear unit activation, and Adam optimizer was utilized. Hyperparameter tuning was performed via 5-fold cross-validation. The ViT model achieved the highest AUC on the validation dataset using 7 features, yielding an AUC of 0.985 and accuracy of 0.952. On the independent testing dataset, the model exhibited an AUC of 0.962 and an accuracy of 0.904. The BEiT model yielded an AUC of 0.939 and an accuracy of 0.871 on the testing dataset. This study demonstrates the effectiveness of transformer-based models, especially ViT, for glioma and meningioma classification, achieving high AUC scores and accuracy. However, the study is limited by the use of a single dataset, which may affect generalizability. Future work should focus on expanding datasets and further optimizing models to improve performance and applicability across different institutions. This study introduces a feature-driven methodology for glioma and meningioma classification, showcasing advancements in the accuracy and model robustness of transformer-based models.

New imaging techniques appearing over the last few decades have replaced procedures that were uncomfortable, of low specificity, and prone to adverse events. While computed tomography remains useful for imaging patients with seizures in acute settings, structural magnetic resonance imaging (MRI) has become the most important imaging modality for epilepsy evaluation, with adjunctive functional imaging also increasingly well established in presurgical evaluation, including positron emission tomography (PET), single photon ictal-interictal subtraction computed tomography co-registered to MRI and functional MRI for preoperative cognitive mapping. Neuroimaging in inherited metabolic epilepsies is integral to diagnosis, monitoring, and assessment of treatment response. Neurotransmitter receptor PET and magnetic resonance spectroscopy can help delineate the pathophysiology of these disorders. Machine learning and artificial intelligence analyses based on large MRI datasets composed of healthy volunteers and people with epilepsy have been initiated to detect lesions that are not found visually, particularly focal cortical dysplasia. These methods, not yet approved for patient care, depend on careful clinical correlation and training sets that fully sample broad populations.

BackgroundCT-based radio-biomarkers could provide non-invasive insights into tumour biology to risk-stratify patients. One of the limitations is laborious manual segmentation of regions-of-interest (ROI). We present a deep learning auto-segmentation pipeline for radiomic analysis. Patients and Methods153 patients with resected stage 2A-3B non-small cell lung cancer (NSCLCs) had tumours segmented using nnU-Net with review by two clinicians. The nnU-Net was pretrained with anatomical priors in non-cancerous lungs and finetuned on NSCLCs. Three ROIs were segmented: intra-tumoural, peri-tumoural, and whole lung. 1967 features were extracted using PyRadiomics. Feature reproducibility was tested using segmentation perturbations. Features were selected using minimum-redundancy-maximum-relevance with Random Forest-recursive feature elimination nested in 500 bootstraps. ResultsAuto-segmentation time was [~]36 seconds/series. Mean volumetric and surface Dice-Sorensen coefficient (DSC) scores were 0.84 ({+/-}0.28), and 0.79 ({+/-}0.34) respectively. DSC were significantly correlated with tumour shape (sphericity, diameter) and location (worse with chest wall adherence), but not batch effects (e.g. contrast, reconstruction kernel). 6.5% cases had missed segmentations; 6.5% required major changes. Pre-training on anatomical priors resulted in better segmentations compared to training on tumour-labels alone (p<0.001) and tumour with anatomical labels (p<0.001). Most radiomic features were not reproducible following perturbations and resampling. Adding radiomic features, however, did not significantly improve the clinical model in predicting 2-year disease-free survival: AUCs 0.67 (95%CI 0.59-0.75) vs 0.63 (95%CI 0.54-0.71) respectively (p=0.28). ConclusionOur study demonstrates that integrating auto-segmentation into radio-biomarker discovery is feasible with high efficiency and accuracy. Whilst radiomic analysis show limited reproducibility, our auto-segmentation may allow more robust radio-biomarker analysis using deep learning features.

A recently developed method enables automated measurement of the hallux valgus angle (HVA) and the first intermetatarsal angle (IMA) from weight-bearing foot radiographs. This approach employs bone segmentation to identify anatomical landmarks and provides standardized angle measurements based on established guidelines. While effective for HVA and IMA, preoperative radiograph analysis remains complex and requires additional measurements, such as the hallux interphalangeal angle (IPA), which has received limited research attention. To expand the previous method, which measured HVA and IMA, by incorporating the automatic measurement of IPA, evaluating its accuracy and clinical relevance. A preexisting database of manually labeled foot radiographs was used to train a U-Net neural network for segmenting bones and identifying landmarks necessary for IPA measurement. Of the 265 radiographs in the dataset, 161 were selected for training and 20 for validation. The U-Net neural network achieves a high mean Sørensen-Dice index (> 0.97). The remaining 84 radiographs were used to assess the reliability of automated IPA measurements against those taken manually by two orthopedic surgeons (O_A and O_B) using computer-based tools. Each measurement was repeated to assess intraobserver (O_A1 and O_A2) and interobserver (O_A2 and O_B) reliability. Agreement between automated and manual methods was evaluated using the Intraclass Correlation Coefficient (ICC), and Bland-Altman analysis identified systematic differences. Standard error of measurement (SEM) and Pearson correlation coefficients quantified precision and linearity, and measurement times were recorded to evaluate efficiency. The artificial intelligence (AI)-based system demonstrated excellent reliability, with ICC3.1 values of 0.92 (AI <i>vs</i> O_A2) and 0.88 (AI <i>vs</i> O_B), both statistically significant (<i>P</i> < 0.001). For manual measurements, ICC values were 0.95 (O_A2 <i>vs</i> O_A1) and 0.95 (O_A2 <i>vs</i> O_B), supporting both intraobserver and interobserver reliability. Bland-Altman analysis revealed minimal biases of: (1) 1.61° (AI <i>vs</i> O_A2); and (2) 2.54° (AI <i>vs</i> O_B), with clinically acceptable limits of agreement. The AI system also showed high precision, as evidenced by low SEM values: (1) 1.22° (O_A2 <i>vs</i> O_B); (2) 1.77° (AI <i>vs</i> O_A2); and (3) 2.09° (AI <i>vs</i> O_B). Furthermore, Pearson correlation coefficients confirmed strong linear relationships between automated and manual measurements, with <i>r</i> = 0.85 (AI <i>vs</i> O_A2) and <i>r</i> = 0.90 (AI <i>vs</i> O_B). The AI method significantly improved efficiency, completing all 84 measurements 8 times faster than manual methods, reducing the time required from an average 36 minutes to just 4.5 minutes. The proposed AI-assisted IPA measurement method shows strong clinical potential, effectively corresponding with manual measurements. Integrating IPA with HVA and IMA assessments provides a comprehensive tool for automated forefoot deformity analysis, supporting hallux valgus severity classification and preoperative planning, while offering substantial time savings in high-volume clinical settings.

Introduction and ObjectivesMany women with multiple sclerosis (MS) experience neurogenic overactive bladder (NOAB) characterized by urinary frequency, urinary urgency and urgency incontinence. The objective of the study was to create machine learning (ML) models utilizing clinical and imaging data to predict NOAB treatment success stratified by treatment type. MethodsThis was a retrospective cohort study of female patients with diagnosis of NOAB and MS seen at a tertiary academic center from 2017-2022. Clinical and imaging data were extracted. Three types of NOAB treatment options evaluated included behavioral therapy, medication therapy and minimally invasive therapies. The primary outcome - treatment success was defined as > 50% reduction in urinary frequency, urinary urgency or a subjective perception of treatment success. For the construction of the logistic regression ML models, bivariate analyses were performed with backward selection of variables with p-values of < 0.10 and clinically relevant variables applied. For ML, the cohort was split into a training dataset (70%) and a test dataset (30%). Area under the curve (AUC) scores are calculated to evaluate model performance. ResultsThe 110 patients included had a mean age of patients were 59 years old (SD 14 years), with a predominantly White cohort (91.8%), post-menopausal (68.2%). Patients were stratified by NOAB treatment therapy type received with 70 patients (63.6%) at behavioral therapy, 58 (52.7%) with medication therapy and 44 (40%) with minimally invasive therapies. On MRI brain imaging, 63.6% of patients had > 20 lesions though majority were not active lesions. The lesions were mostly located within the supratentorial (94.5%), infratentorial (68.2%) and 58.2 infratentorial brain (63.8%) as well as in the deep white matter (53.4%). For MRI spine imaging, most of the lesions were in the cervical spine (71.8%) followed by thoracic spine (43.7%) and lumbar spine (6.4%).10.3%). After feature selection, the top 10 highest ranking features were used to train complimentary LASSO-regularized logistic regression (LR) and extreme gradient-boosted tree (XGB) models. The top-performing LR models for predicting response to behavioral, medication, and minimally invasive therapies yielded AUC values of 0.74, 0.76, and 0.83, respectively. ConclusionsUsing these top-ranked features, LR models achieved AUC values of 0.74-0.83 for prediction of treatment success based on individual factors. Further prospective evaluation is needed to better characterize and validate these identified associations.

Foundation Models (FMs) have shown great promise for multimodal medical image analysis such as Magnetic Resonance Imaging (MRI). However, certain MRI sequences may be unavailable due to various constraints, such as limited scanning time, patient discomfort, or scanner limitations. The absence of certain modalities can hinder the performance of FMs in clinical applications, making effective missing modality imputation crucial for ensuring their applicability. Previous approaches, including generative adversarial networks (GANs), have been employed to synthesize missing modalities in either a one-to-one or many-to-one manner. However, these methods have limitations, as they require training a new model for different missing scenarios and are prone to mode collapse, generating limited diversity in the synthesized images. To address these challenges, we propose DiffM⁴RI, a diffusion model for many-to-many missing modality imputation in MRI. DiffM⁴RI innovatively formulates the missing modality imputation as a modality-level inpainting task, enabling it to handle arbitrary missing modality situations without the need for training multiple networks. Experiments on the BraTs datasets demonstrate DiffM⁴RI can achieve an average SSIM improvement of 0.15 over MustGAN, 0.1 over SynDiff, and 0.02 over VQ-VAE-2. These results highlight the potential of DiffM⁴RI in enhancing the reliability of FMs in clinical applications. The code is available at https://github.com/27yw/DiffM4RI.

This paper presents a novel methodology that combines a pre-trained Visual Transformer-Based Deep Model (ViT) with a custom denoising image filter for the diagnosis of Unilateral Diaphragmatic Paralysis (UDP) using Ultrasound (US) images. The ViT is employed to extract complex features from US images of 17 volunteers, capturing intricate patterns and details that are critical for accurate diagnosis. The extracted features are then fed into an ensemble learning model to determine the presence of UDP. The proposed framework achieves an average accuracy of 93.8% on a stratified 5-fold cross-validation, surpassing relevant state-of-the-art (SOTA) image classifiers. This high level of performance underscores the robustness and effectiveness of the framework, highlighting its potential as a prominent diagnostic tool in medical imaging.

Small bowel motility can be quantified using cine MRI, but the influence of patient and imaging factors on motility scores remains unclear. This study evaluated whether patient and imaging factors affect motility scores derived from deep learning-based segmentation of cine MRI. Fifty-four patients (mean age 53.6 ± 16.4 years; 34 women) with chronic constipation or suspected colonic pseudo-obstruction who underwent cine MRI covering the entire small bowel between 2022 and 2023 were included. A deep learning algorithm was developed to segment small bowel regions, and motility was quantified with an optical flow-based algorithm, producing a motility score for each slice. Associations of motility scores with patient factors (age, sex, body mass index, symptoms, and bowel distension) and MRI slice-related factors (anatomical location, bowel area, and anteroposterior position) were analyzed using linear mixed models. Deep learning-based small bowel segmentation achieved a mean volumetric Dice similarity coefficient of 75.4 ± 18.9%, with a manual correction time of 26.5 ± 13.5 s. Median motility scores per patient ranged from 26.4 to 64.4, with an interquartile range of 3.1-26.6. Multivariable analysis revealed that MRI slice-related factors, including anatomical location with mixed ileum and jejunum (β = -4.9; p = 0.01, compared with ileum dominant), bowel area (first order β = -0.2, p < 0.001; second order β = 5.7 × 10^-4, p < 0.001), and anteroposterior position (first order β = -51.5, p < 0.001; second order β = 28.8, p = 0.004) were significantly associated with motility scores. Patient factors showed no association with motility scores. Small bowel motility scores were significantly associated with MRI slice-related factors. Determining global motility without adjusting for these factors may be limited. Question Global small bowel motility can be quantified from cine MRI; however, the confounding factors affecting motility scores remain unclear. Findings Motility scores were significantly influenced by MRI slice-related factors, including anatomical location, bowel area, and anteroposterior position. Clinical relevance Adjusting for slice-related factors is essential for accurate interpretation of small bowel motility scores on cine MRI.

Accurate segmentation of brain tumor images, particularly gliomas in MRI scans, is crucial for early diagnosis, monitoring progression, and evaluating tumor structure and therapeutic response. A novel lightweight, transformer-based U-Net model for brain tumor segmentation, integrating attention mechanisms and multi-layer feature extraction via atrous convolution to capture long-range relationships and contextual information across image regions is proposed in this work. The model performance is evaluated on the publicly accessible BraTS 2020 dataset using evaluation metrics such as the Dice coefficient, accuracy, mean Intersection over Union (IoU), sensitivity, and specificity. The proposed model outperforms many of the existing methods, such as MimicNet, Swin Transformer-based UNet and hybrid multiresolution-based UNet, and is capable of handling a variety of segmentation issues. The experimental results demonstrate that the proposed model acheives an accuracy of 98.23%, a Dice score of 0.9716, and a mean IoU of 0.8242 during training when compared to the current state-of-the-art methods.