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.

With the improvement of imaging, the screening rate of Pulmonary nodules (PNs) has further increased, but their identification of High-Risk Prognostic Pathological Components (HRPPC) is still a major challenge. In this study, we aimed to build a multi-parameter machine learning predictive model to improve the discrimination accuracy of HRPPC. This study included 816 patients with ≤ 3 cm pulmonary nodules with clear pathology and underwent pulmonary resection. High-resolution chest CT images, clinicopathological characteristics were collected from patients. Lasso regression was utilized in order to identify key features, and a machine learning prediction model was constructed based on the screened key features. The recognition ability of the prediction model was evaluated using (ROC) curves and confusion matrices. Model calibration ability was evaluated using calibration curves. Decision curve analysis (DCA) was used to evaluate the value of the model for clinical applications. Use SHAP values for interpreting predictive models. A total of 816 patients were included in this study, of which 112 (13.79%) had HRPPC of pulmonary nodules. By selecting key variables through Lasso recursive feature elimination, we finally identified 13 key relevant features. The XGB model performed the best, with an area under the ROC curve (AUC) of 0.930 (95% CI: 0.906-0.954) in the training cohort and 0.835 (95% CI: 0.774-0.895) in the validation cohort, indicating that the XGB model had excellent predictive performance. In addition, the calibration curves of the XGB model showed good calibration in both cohorts. DCA demonstrated that the predictive model had a positive benefit in general clinical decision-making. The SHAP values identified the top 3 predictors affecting the HRPPC of PNs as CT Value, Nodule Long Diameter, and PRO-GRP. Our prediction model for identifying HRPPC in PNs has excellent discrimination, calibration and clinical utility. Thoracic surgeons could make relatively reliable predictions of HRPPC in PNs without the possibility of invasive testing.

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 detection of cortical infarct is critical for timely treatment and improved patient outcomes. Current brain imaging methods often require invasive procedures that primarily assess blood vessel and structural white matter damage. There is a need for non-invasive approaches, such as functional MRI (fMRI), that better reflect neuronal viability. This study utilized automated machine learning (auto-ML) techniques to identify novel infarct-specific fMRI biomarkers specifically related to chronic cortical infarcts. We analyzed resting-state fMRI data from the multi-center ADNI dataset, which included 20 chronic infarct patients and 30 cognitively normal (CN) controls. This study utilized automated machine learning (auto-ML) techniques to identify novel fMRI biomarkers specifically related to chronic cortical infarcts. Surface-based registration methods were applied to minimize partial-volume effects typically associated with lower resolution fMRI data. We evaluated the performance of 7 previously known fMRI biomarkers alongside 107 new auto-generated fMRI biomarkers across 33 different classification models. Our analysis identified 6 new fMRI biomarkers that substantially improved infarct detection performance compared to previously established metrics. The best-performing combination of biomarkers and classifiers achieved a cross-validation ROC score of 0.791, closely matching the accuracy of diffusion-weighted imaging methods used in acute stroke detection. Our proposed auto-ML fMRI infarct-detection technique demonstrated robustness across diverse imaging sites and scanner types, highlighting the potential of automated feature extraction to significantly enhance non-invasive infarct detection.

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.

To investigate the value of deep learning-based quantitative CT (QCT) in predicting progressive fibrosing interstitial lung disease (PF-ILD) and assessing prognosis. This single-center retrospective study included ILD patients with CT examinations between January 2015 and June 2021. Each ILD finding (ground-glass opacity (GGO), reticular opacity (RO), honeycombing) and fibrosis (sum of RO and honeycombing) was quantified from baseline and follow-up CTs. Logistic regression was performed to identify predictors of PF-ILD, defined as radiologic progression along with forced vital capacity (FVC) decline ≥ 5% predicted. Cox proportional hazard regression was used to assess mortality. The added value of incorporating QCT into FVC was evaluated using C-index. Among 465 ILD patients (median age [IQR], 65 [58-71] years; 238 men), 148 had PF-ILD. After adjusting for clinico-radiological variables, baseline RO (OR: 1.096, 95% CI: 1.042, 1.152, p < 0.001) and fibrosis extent (OR: 1.035, 95% CI: 1.004, 1.067, p = 0.025) were PF-ILD predictors. Baseline RO (HR: 1.063, 95% CI: 1.013, 1.115, p = 0.013), honeycombing (HR: 1.074, 95% CI: 1.034, 1.116, p < 0.001), and fibrosis extent (HR: 1.067, 95% CI: 1.043, 1.093, p < 0.001) predicted poor prognosis. The Cox models combining baseline percent predicted FVC with QCT (each ILD finding, C-index: 0.714, 95% CI: 0.660, 0.764; fibrosis, C-index: 0.703, 95% CI: 0.649, 0.752; both p-values < 0.001) outperformed the model without QCT (C-index: 0.545, 95% CI: 0.500, 0.599). Deep learning-based QCT for ILD findings is useful for predicting PF-ILD and its prognosis. Question Does deep learning-based CT quantification of interstitial lung disease (ILD) findings have value in predicting progressive fibrosing ILD (PF-ILD) and improving prognostication? Findings Deep learning-based CT quantification of baseline reticular opacity and fibrosis predicted the development of PF-ILD. In addition, CT quantification demonstrated value in predicting all-cause mortality. Clinical relevance Deep learning-based CT quantification of ILD findings is useful for predicting PF-ILD and its prognosis. Identifying patients at high risk of PF-ILD through CT quantification enables closer monitoring and earlier treatment initiation, which may lead to improved clinical outcomes.

Widely used in oncology PET, 2-deoxy-2-18F-FDG PET is more accessible and affordable than amyloid PET, which is a crucial tool to determine amyloid positivity in diagnosis of Alzheimer disease (AD). This study aimed to leverage deep learning with residual 3D convolutional neural networks (3DCNN) to develop a robust model that predicts amyloid-β positivity by using FDG-PET. In this study, a cohort of 187 patients was used for model development. It consisted of patients ranging from cognitively normal to those with dementia and other cognitive impairments who underwent T1-weighted MRI, 18F-FDG, and 11C-Pittsburgh compound B (PiB) PET scans. A residual 3DCNN model was configured using nonexhaustive grid search and trained on repeated random splits of our development data set. We evaluated the performance of our model, and particularly its robustness, using a multisite data set of 99 patients of different ethnicities with images at different site harmonization levels. Our model achieved mean AUC scores of 0.815 and 0.840 on images without and with site harmonization correspondingly. Respectively, it achieved higher AUC scores of 0.801 and 0.834 in the cognitively normal (CN) group compared with 0.777 and 0.745 in the dementia group. As for F1 score, the corresponding mean scores were 0.770 and 0.810 on images without and with site harmonization. In the CN group, it achieved lower F1 scores of 0.580 and 0.658 compared with 0.907 and 0.931 in the dementia group. We demonstrated that residual 3DCNN can learn complex 3D spatial patterns in FDG-PET images and robustly predict amyloid-β positivity with significantly less reliance on site harmonization preprocessing.