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Body / AbdominalAI / InformaticsResearch

Deep learning model stages rectal cancer without manual segmentation on MRI

MST-Net, a segmentation-free deep learning model, achieved AUC 0.95 (accuracy 90%) for T2 vs T3 rectal cancer staging on preoperative T2-weighted MRI, outperforming junior radiologists (69%) and matching senior (85%). External validation AUC 0.85 (accuracy 79%).

Insights into imaging
GeneralDevices & AI clearances

FDA Clears Fujifilm FCT iStream Phase 2 CT System

FDA cleared Fujifilm's FCT iStream Phase 2 computed tomography system via the 510(k) pathway. The device is classified under product code JAK for computed tomography x-ray systems.

U.S. FDA
Neuro / Head & NeckDevices & AI clearances

MRI2CT's NextMR Spine Gains FDA 510(k) Clearance

The FDA cleared NextMR Spine (MRI2CT, Inc.) via the 510(k) pathway. The radiological image processing system (product code QIH) was reviewed under the Radiology committee.

U.S. FDA
Neuro / Head & NeckAI / InformaticsResearch

CT Radiomics Model Predicts 3-Month Poor Outcome After Basal Ganglia Hemorrhage

A CT radiomics-based random forest model predicted poor 3-month functional outcome (mRS ≥4) in conservatively managed basal ganglia hemorrhage with an AUC of 0.867 (95% CI 0.797–0.929) after bootstrap correction. Key predictors: admission GCS and wavelet radiomic features. Exter…

Frontiers in neurology
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