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AI Models Use EMR and Radiology Data To Predict Intimate Partner Violence
Mass General Brigham researchers developed AI models that use EMR, including radiology data, to predict risk for intimate partner violence (IPV) years before patients seek care.
AI Fusion Model Uses Radiology Data to Predict Intimate Partner Violence Risk
Researchers created an AI tool leveraging clinical and radiology data to accurately predict patients at risk of intimate partner violence (IPV).
AMA Survey: Physicians Double AI Use, Wary About Patient-Directed Radiology AI
The share of U.S. doctors using AI in practice has doubled, yet many are hesitant about patients using AI for interpreting radiology results.
From the Research Hub
Predicting brain volumes from anthropometric and demographic features: insights from UK biobank neuroimaging data.
Brain size measures are well-studied and often treated as a confound in volumetric neuroimaging analyses. Yet their relationship with body anthropometric measures and demographics remains underexplored. In this study, we examined those relationships alongside age- and sex-related differences in global brain volumes. Using brain magnetic resonance imaging (MRI) of healthy participants in the UK Biobank, we derived global measures of brain morphometry, including total intracranial volume (TIV), total brain volume (TBV), gray matter volume (GMV), white matter volume (WMV), and cerebrospinal fluid (CSF). We extracted these measures using the Computational Anatomy Toolbox (CAT) and FreeSurfer. Our analyses were structured in three approaches: across-sex analysis, sex-specific analysis, and impact of age analysis. Employing machine learning (ML), we found that TIV was strongly predicted by sex (across-sex [Formula: see text] 0.68), reflecting sex difference. On the other hand, TBV, GMV, WMV, and CSF were more sensitive to age, with higher prediction accuracy when age was included as a feature, highlighting age-related changes in the brain structure, such as fluid expansion. Sex-specific models showed reduced TIV prediction ([Formula: see text] 0.25) but improved TBV accuracy ([Formula: see text] 0.44), underscoring sex-specific body-brain relationships. Anthropometric measures, particularly seated height and weight, improved prediction of TIV and TBV, while waist and hip circumference showed negative associations, though their effects generally remained secondary to age and sex. These findings advance our understanding of brain-body scaling relationships and underscore the necessity of accounting for age and sex in neuroimaging studies of brain morphology. The online version contains supplementary material available at 10.1007/s00429-025-03070-9.
CT-based hybrid deep learning-radiomics framework for predicting postoperative rebleeding in hypertensive intracerebral hemorrhage.
Hypertensive intracerebral hemorrhage (HICH) is a frequently encountered and highly lethal cerebrovascular disorder, and postoperative rebleeding remains one of its most feared complications. We developed a nomogram featuring a hybrid architecture that integrates radiomics-derived signatures and deep learning representations from CT imaging together with routine clinical parameters, with the goal of enhancing the individualized prediction of rebleeding risk following surgical intervention in HICH patients. A total of 151 individuals diagnosed with HICH were prospectively enrolled and randomly assigned to a training set (n=105) and a validation set (n=46) following a 7:3 ratio. The resulting model outperformed single-domain approaches relying solely on traditional clinical indicators or deep learning features. In the training cohort, the nomogram yielded an AUC of 0.993 (95 % CI: 0.982-1.000), while in the internal testing cohort, the AUC reached 0.860 (95 % CI: 0.745-0.974). The model highlighted several key predictors associated with postoperative rebleeding. Overall, the integrated nomogram, embedding clinical data, radiomic phenotypes, and deep learning markers, exhibited robust predictive capability in assessing rebleeding risk among patients with HICH. Ongoing research is needed to further refine and validate the model in broader clinical settings.
Geometric Brain Signatures for Diagnosing Rare Hereditary Ataxias and Predicting Function
Hereditary cerebellar ataxias (HCAs) are rare neurodegenerative disorders characterised by progressive motor impairment and overlapping clinical phenotypes. Although genetic testing provides etiological diagnosis, diagnostic delays frequently arise before targeted testing, owing to non-specific presentation and limited clinician familiarity. Imaging-derived biomarkers that capture phenotypic expression and network-level consequences of disease could support earlier recognition of hereditary ataxia, guide appropriate genetic testing, and provide sensitive measures of disease evolution. Building on evidence that cortical geometry shapes functional organisation, we hypothesised that geometric signatures derived from structural magnetic resonance imaging (sMRI) could discriminate HCA subtypes and yield progression-sensitive biomarkers, while enabling scalable prediction of function. We decomposed sMRI and task-evoked functional MRI data from three independent cohorts using cortical geometric eigenmodes, intrinsic spatial patterns defined by cortical surface geometry, to obtain structural and functional geometric signatures. Structural signatures were used to train neural networks for disease classification and to derive biomarkers sensitive to annual progression. We further modelled structure-to-function mappings to predict functional geometric signatures from sMRI and evaluated their diagnostic and longitudinal utility. Our framework achieved high diagnostic performance, distinguishing healthy controls from Friedreich ataxia (FRDA) with a maximum AUC of 0.93 and separating FRDA from spinocerebellar ataxia type 1 (SCA1) and SCA3, with AUCs up to 0.81, showing cross-cohort generalisability. Structure-to-function-signature prediction achieved a coefficient of determination up to 0.62 and a correlation reaching 0.86 across health and disease, while predicted functional signatures improved classification beyond structural signatures alone and enabled partial reconstruction of the individual task-activation map. Geometric brain signatures showed greater progression sensitivity than conventional volumetric MRI measures. This geometry-driven framework offers novel, objective, multiscale biomarkers for diagnostic-decision-support and monitoring HCAs and provides proof of concept for the feasibility of predicting fMRI equivalent biomarkers in disease from routine sMRI, which is far more practical in movement disorder populations.
From the RadAI FDA Approvals Database
Ligence Uab
Ligence Heart
Ligence Heart is an automated software designed to assist clinicians by processing radiological images of the heart. It helps by analyzing cardiac imaging data to support medical diagnosis or evaluation.
Aidoc Medical , Ltd.
BriefCase-Triage: CARE Multi-Triage CT for Pneumothorax; Pericardial effusion; Large aortic aneurysm; Shoulder fracture or dislocation device
BriefCase-Triage: CARE Multi-Triage CT is an AI software that helps clinicians by automatically analyzing CT images to detect critical conditions like pneumothorax, pericardial effusion, large aortic aneurysm, and shoulder fractures or dislocations, enabling faster diagnosis and treatment.
Karl Storz SE & CO. KG
KARL STORZ ICG Imaging System with RUBINA® Lens
The KARL STORZ ICG Imaging System with RUBINA® Lens is an advanced medical imaging device that uses near-infrared fluorescence (indocyanine green, ICG) combined with confocal optical imaging to help surgeons visualize blood flow and tissue perfusion during surgery. This enhances surgical precision and patient outcomes by providing real-time images of vascular structures and tissue viability.
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