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Interventional (IR)Guideline

SIR Foundation Research Consensus Panel Sets Priorities for Acute DVT Intervention Studies

SIR Foundation research consensus panel calls for standardized acute DVT intervention endpoints: biologically informed patient selection, anatomic/physiologic metrics, validated severity tools, longitudinal biomarkers, and economic/QoL measures to advance precision and reduce po…

Journal of vascular and interventional radiology : JVIR

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Neuro / Head & NeckPediatricAI / InformaticsResearch

Deep Learning Reconstruction Improves Neonatal Brain MRI Quality at 3T: A Pilot Study

Higher denoising levels with a deep learning reconstruction algorithm progressively improved qualitative and quantitative image quality in 3D T1-weighted neonatal brain MRI, though the pilot study's small sample size (n=15) limits generalizability.

Journal of clinical imaging science
Body / AbdominalAI / InformaticsResearch

Open vertebrae CT dataset and deep learning models benchmarked against existing tools

Open neural network models for vertebral body segmentation in computed tomography (CT) achieve Dice scores up to 0.962 (95% CI 0.941–0.978) for L3 and outperform existing pipelines in center localization error, externally validated in 300 oncologic cases. Labels and weights are…

European journal of radiology
GeneralDevices & AI clearances

UC-CARE SmartGuide Disposable Grids Cleared by FDA

The FDA has cleared UC-CARE, Ltd.'s SmartGuide Disposable Guides and Grids, a radiology device, via the 510(k) pathway. Product code ITX (Radiology panel).

U.S. FDA
Chest / ThoracicEmergencyAI / InformaticsResearch

Reduced Feature Sets Match Full Models for ICU Mortality Prediction with Chest X-Ray

A small set of four clinical and radiographic features predicted ICU mortality with an AUC of 0.96 (accuracy 0.82), matching a 74-feature radiomic model. Cobb angle, bilateral infiltrates, and pleural effusions were identified as consistent, robust predictors.

Diagnostics (Basel, Switzerland)
GeneralAI / InformaticsResearch

Systematic Review: AI for Third Molar Imaging Analysis

A systematic review of 32 studies finds AI, mostly convolutional neural networks on panoramic X-rays, shows promising but variable performance for third molar detection, segmentation, classification, and surgical difficulty prediction; external validation is lacking.

Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons
Body / AbdominalAI / InformaticsNews

Radiomics and deep learning sharpen adrenal mass assessment

A narrative review highlights radiomics and deep learning as promising tools for adrenal mass assessment, including differentiating pheochromocytoma/paraganglioma, cortical adenoma, and cortical carcinoma and predicting genotype/prognosis.

Frontiers in endocrinology
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