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SIIM 2026 AI Landscape
Analysis of SIIM 2026 sessions, themes, speaker mix, and abstract activity through the lens of imaging AI.
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A launch-level view of ECR 2026 product announcements: AI share, product mix, and modality focus.
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A data-driven map of ECR 2026 exhibitors: AI share, vendor mix, and regional patterns.
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FDA Approvals Database
Latest News & Trends
Radiology AI Devices at Elevated Risk for FDA Recalls, Study Finds
Radiology AI devices are more likely to face FDA recalls, largely due to deviations from intended use and incomplete clinical data.
AI Model Improves Early Detection of Primary Aldosteronism via EHR Data
An AI-driven model using 30 years of EHR data enhances screening for primary aldosteronism, a frequently underdiagnosed hypertension cause.
Radiologists Share Insights on Boosting Workflow with AI at SIIM
Dr. Nina Kottler discusses strategies for leveraging AI to improve radiology workflow efficiency at SIIM 2026.
From the Research Hub
Prediction of pedicle screw fixation strength under craniocaudal cyclic load: comparison of various models trained on quantitative CT based finite element analysis.
This study aims to predict FEA-derived screw fixation strength (FS-CL) under craniocaudal cyclic load using machine learning and deep learning, and to explore whether FS-CL can serve as a surrogate marker for pedicle screw loosening (PSL) risk. A retrospective analysis was conducted on 618 screw trajectories data from preoperative of 112 patients. Various ML and DL models utilizing CT images and screw trajectory, were developed to predict screw FS-CL including multilayer perceptron (MLP) and dual-channel 3D ResNet-18 models. Model performance was evaluated using mean squared error (MSE), coefficient of determination (R²) on an external validation set of 126 trajectories. Additionally, we validated the clinical efficiency of the model for the risk assessment of PSL based on a case-control cohort of 62 patients. The MLP and 3D ResNet-18 models demonstrated reliable FS-CL predictions, with less time spent compared to the manual FEA. All DL and ML model that focused on region surrounding screw trajectory performed better. The ResNet-18 model achieved the highest predictive performance for screw FS-CL (MSE: 0.009, R²: 0.836) and highest prediction for PSL risk with an AUC value of 0.826. The MLP model also exhibited moderate performance, outperforming other ML models. AI models proposed in this study can accurately predict FEA-derived FS-CL efficiently providing a supplementary tool for PSL risk evaluation.
Normative volumetric growth modeling of the whole fetal body, placenta, and amniotic fluid for three-dimensional T2-weighted magnetic resonance imaging.
Magnetic resonance imaging (MRI)-based volumetry of the fetus, placenta, and amniotic fluid is clinically valuable but rarely used due to labor-intensive manual segmentation of motion-corrupted two-dimensional (2-D) stacks. Existing deep learning approaches are typically limited to single structures and 2-D data, while no robust automated solution exists for whole-uterus volumetry in reconstructed three-dimensional (3-D) MRI, and normative reference ranges are lacking. To develop an automated pipeline for whole-uterus volumetry in 3-D T2-weighted fetal MRI and derive normative growth models for fetal, placental, and amniotic fluid volumes. Motion-corrupted T2-weighted stacks (0.55-3-T field strength) were reconstructed into 3-D isotropic images using deformable slice-to-volume reconstruction, followed by automated segmentation with a 3-D U-Net. The method was applied to 357 normal-control datasets with confirmed term birth (16-41Â weeks gestational age range) to derive quadratic normative growth curves. Performance and clinical utility were further evaluated on 43 independent datasets. Segmentation was highly accurate (Dice: fetus 0.997, placenta 0.995, amniotic fluid 0.998) with low volume errors (<1%) and minimal manual refinement required in <25% of cases. In the control cohort, fetal and placental volumes increased with gestational age (P<0.001), while amniotic fluid followed a quadratic trend. Longitudinal growth rates were 146.6Â cc/week (fetus) and 38.8Â cc/week (placenta). Preterm pregnancies showed significantly lower fetal and placental volumes (P<0.001) and reduced amniotic fluid (P<0.01). This work presents the first automated pipeline for simultaneous whole-uterus volumetry in 3-D fetal MRI and establishes normative growth models across gestation. The approach enables accurate, standardized volumetric assessment and provides a practical tool for detecting abnormal growth patterns in both normal and high-risk pregnancies.
Acceptability and Perceptions of Artificial Intelligence in Organized Breast Cancer Screening: A Study of French Women
This study aims to assess womens perceptions of artificial intelligence (AI) used in breast cancer screening in France by examining their knowledge of AI and the barriers to their participation in organized screening. The results of a survey conducted in June 2025 among a national sample of 2000 women (aged 40-75) reveal limited participation and persistent concerns among women. Nevertheless, despite a low awareness of specific AI applications, a large majority of the women surveyed are very favorable to the use of AI in breast cancer diagnosis, even considering it a lever to increase screening participation.
From the RadAI FDA Approvals Database
Allengers Medical Systems Limited
Digital C-ARM with 3D
The Digital C-ARM with 3D is a mobile fluoroscopic X-ray system that provides real-time 2D and 3D imaging. It assists clinicians in visualizing patient anatomy during procedures, improving accuracy and patient outcomes.
Sonio
Sonio Suspect
Sonio Suspect is AI-powered diagnostic software that helps clinicians identify lesions in medical images that may be suspicious for cancer, enhancing early detection and aiding diagnostic decisions.
Topcon Healthcare Solutions EMEA Oy
Harmony Referral System with Harmony OCT Viewer (Octave)
The Harmony Referral System with Harmony OCT Viewer (Octave) is a system designed to manage and view ophthalmic images, specifically OCT (Optical Coherence Tomography) scans. It helps clinicians by organizing, displaying, and facilitating the analysis of retinal images, aiding in the diagnosis and management of eye conditions.
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