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Precision Psychiatry Symposium Explores Imaging, AI, and Novel Therapies for Schizophrenia
Virginia Tech hosts a symposium focusing on precision psychiatry, spotlighting imaging, biomarkers, and AI to improve schizophrenia treatment.
Case Report Highlights Challenges of AI in Chest X-ray Interpretation
A case report stresses caution in relying solely on AI results for chest radiograph interpretation due to the risk of false positives.
AI Detects Hypertension and Diabetes from 5-Second Facial Videos
A machine-learning algorithm can accurately screen for hypertension and diabetes using brief facial video recordings, offering a contactless diagnostic option.
From the Research Hub
Pooled two-cohort MRI body composition phenotyping with open-source deep learning.
Body mass index fails to capture variation in fat and muscle distribution that determines metabolic health and disease risk. MRI enables radiation-free quantification of regional body composition, yet scalable open-source tools applied in pooled cohorts with differing acquisition protocols have been lacking. MRSegmentator, an open-source nnU-Net-based pipeline, was applied to quantify visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT), gluteofemoral adipose tissue (GFAT), trunk musculature, and the liver mask used for liver fat-fraction estimation in 45,851 adults from the German National Cohort (n = 26,877, 3 T multi-centre Siemens) and UK Biobank (n = 18,974, 1.5 T Siemens). Population-scale compartment volumes were segmented from stitched in-phase gradient-echo (GRE) images in both cohorts; liver fat fraction was calculated from fat-only and water-only images. The annotated development data comprised NAKO T2-HASTE and UKB Dixon reconstructions. A single pooled model was applied without site-specific adaptation. A separate two-reader agreement study used 50 scans from these annotated development-sequence domains. Associations between BMI-adjusted body composition and cardiometabolic conditions were estimated using generalized linear mixed-effects models. Incremental discrimination beyond age, BMI, and waist-to-hip ratio was assessed. Five-fold participant-stratified internal cross-validation against curated human-in-the-loop development references comprising UKB Dixon and NAKO T2-HASTE yielded a mean Dice of 0.91. In a separate 50-scan reader study on these annotated development-sequence images, overall reader-reader Dice was 0.937 and overall algorithm-reader Dice was 0.908. The trained pipeline was then used to segment compartment volumes from stitched in-phase GRE inputs in both cohorts, while liver fat fraction was calculated from fat-only and water-only images; direct sequence-matched validation on NAKO GRE was not performed. VAT showed the strongest positive associations with cardiometabolic conditions, while GFAT showed inverse associations, most prominently for type 2 diabetes (OR 0.69, 95% CI 0.66 to 0.72). Disease-specific body-composition phenotypes were identified, with type 2 diabetes characterized by elevated VAT, reduced GFAT, and increased liver fat. MRI-derived compartments modestly improved discrimination for type 2 diabetes and hyperlipidemia beyond anthropometric measures. A single open-source deep-learning pipeline enabled pooled body-composition phenotyping in two cohorts and captured distributional variation in fat and muscle beyond BMI. High agreement in internal cross-validation (mean Dice 0.91) and the separate two-reader study support the annotated development-sequence analysis, while the population-scale application identified distinct disease-associated phenotypes and modest incremental discrimination beyond conventional anthropometry.
Volumetric reference data of the orbit: a deep learning MRI analysis in the German national cohort.
Manual segmentation of orbital magnetic resonance imaging (MRI) is labor-intensive, hindering large-scale morphometric studies. To overcome this, we developed a fully automated deep learning pipeline to segment orbital MRIs and establish age- and sex-stratified normative reference data. We analyzed T1-weighted brain MRIs from 30,868 participants in the population-based German National Cohort (NAKO). After quality control, 28,779 participants (mean age 48.1 years; 44.1% female) were included. The model, validated against expert manual segmentations, accurately extracted 34 volumetric and geometric parameters across 15 orbital structures (Dice Similarity Coefficients: vitreous 0.97, lens 0.89, optic nerve 0.85). Mean [SD] axial length was 23.5 [1.2] mm. Mean [SD] volumes were 34.4 [3.9] cm³ for total orbital contents, 6.3 [0.8] cm³ for the vitreous, and 0.17 [0.03] cm³ for the lens. Males exhibited significantly larger dimensions across all parameters (p < 0.001). Age-stratified percentile curves revealed continuous age-dependent lens growth (Spearman's ρ = 0.57 in men, 0.51 in women) alongside modest volume increases in the orbit, optic nerve, and extraocular muscles. This deep learning tool effectively resolved the bottleneck of manual segmentation, providing comprehensive orbital reference data for the German population. This foundation enables future high-throughput epidemiological research into the associations between orbital anatomy, systemic health, and disease.
Physics-Assisted Deep Learning Denoising for Stabilized IMPULSED dMRI Microenvironment Parameter Fitting
Diffusion-weighted MRI (dMRI) is a powerful tool for quantifying cellular microenvironment parameters. This study proposes a physics-assisted deep learning (DL)-based denoising framework designed to enhance dMRI signal quality and improve the robustness of subsequent biophysical model fitting. A dataset of paired noise-free and Rician-noise-corrupted dMRI signals was generated using the IMPULSED-dMRI signal model. Three denoising architectures were evaluated: Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM) networks. Denoised signals were then fitted to estimate cell diameter $d$, intracellular volume fraction $V_{\mathrm{in}}$, and extracellular apparent diffusion coefficient $D_\mathrm{ex}$. DL-based processing substantially improved dMRI signal denoising. The MLP and LSTM achieved similar performance, with the LSTM slightly better overall, and both outperformed the CNN. In the subsequent model fitting step, the LSTM produced modest reductions in parameter MAE. The dominant benefit was fitting stabilization, with the overall fitting failure rate reduced from 57.6\% to 17.7\%. The proposed framework improves dMRI signal quality and stabilizes subsequent IMPULSED-based microenvironmental parameter fitting.
From the RadAI FDA Approvals Database
Radformation, Inc.
AutoContour (RADAC V6)
AutoContour (RADAC V6) is a software tool used to assist radiation therapy planning by automatically processing radiological images to define anatomical structures, helping clinicians to more efficiently and accurately plan treatments for cancer patients.
Screenpoint Medical B.V.
Transpara (2.1.0)
Transpara is an AI-based software that helps radiologists detect and diagnose possible cancerous lesions in medical images, improving the accuracy and speed of cancer screening and diagnosis.
ADVAHEALTH SOLUTIONS, LLC
AdvaView
AdvaView is a radiological image processing system designed to assist in the viewing and analysis of medical images, helping clinicians interpret radiology scans more efficiently.
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