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Measuring whole-body anatomic volumes across the adult lifespan: using AI to derive reference charts from CT scans https://doi.org/10.1148/ryai.250506 #AbdRad #heart #lungs
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Body Charts from CT Segmentations across the Adult Lifespan: Large-scale Cross-sectional and Longitudinal Analyses https://doi.org/10.1148/ryai.250506 #SAR2026 #AbdRad #radiology
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Body Charts from CT Segmentations across the Adult Lifespan: Large-scale Cross-sectional and Longitudinal Analyses https://doi.org/10.1148/ryai.250506 #SAR2026 #AbdRad #radiology
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A retrospective study defines extra-axial cerebrospinal fluid growth charts to support diagnosis of benign enlargement of the subarachnoid space https://doi.org/10.1148/ryai.250123 #SAR2026 #AbdRad #radiology
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Measuring whole-body anatomic volumes across the adult lifespan: using AI to derive reference charts from CT scans https://doi.org/10.1148/ryai.250506 #SAR2026 #AbdRad #radiology
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AIRFRAME part 1: developing a deep-learning algorithm for identifying posterior fossa abnormalities on first-trimester US screen scans https://doi.org/10.1148/ryai.250394 @fprefumo.bsky.social #SAR2026 #AbdRad #radiology
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An ontology that standardizes metadata for AI models, datasets, and projects, enabling interoperable description, discovery, and transparent use of AI resources https://doi.org/10.1148/ryai.260069 #SAR2026 #AbdRad #radiology
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Supporting model trustworthiness by understanding failure modes: a new taxonomy of AI hallucination https://doi.org/10.1148/ryai.250203 #SAR2026 #AbdRad #radiology
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Our reviewers are the backbone of the journal. We thank them for sharing their knowledge. https://doi.org/10.1148/ryai.260176 #SAR2026 #AbdRad #radiology
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Facilitating rigorous, transparent, and responsible evaluation of medical AI systems with a taxonomy of metrics https://doi.org/10.1148/ryai.260070 #SAR2026 #AbdRad #radiology
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Managing clinical risk through error analysis of AI models radiologyai.substack.com/p/when-accuracy-is-not-e... #SAR2026 #AbdRad #radiology
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Editor’s Recognition Awards honor our excellent reviewers https://doi.org/10.1148/ryai.260178 #SAR2026 #AbdRad #radiology
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Deep learning for the detection, diagnosis, and segmentation of gastric neoplasms on contrast-enhanced CT https://doi.org/10.1148/ryai.250145 #AbdRad #AI #MachineLearning
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Gastric Neoplasm Detection on Contrast-enhanced CT with Deep Learning https://doi.org/10.1148/ryai.250145 #AbdRad #AI #MachineLearning
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Modeling whole-body, CT-derived anatomic volumes to establish reference charts for the adult lifespan https://doi.org/10.1148/ryai.250506 #AbdRad #kidney #MachineLearning
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Gastric Neoplasm Detection on Contrast-enhanced CT with Deep Learning https://doi.org/10.1148/ryai.250145 #segmentation #AbdRad #radiology
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Gastric Neoplasm Detection with AI (GANDA) can detect and diagnose gastric neoplasm on contrast-enhanced CT https://doi.org/10.1148/ryai.250145 #cancer #AbdRad #radiology
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Gastric Neoplasm Detection with AI (GANDA) can detect and diagnose gastric neoplasm on contrast-enhanced CT https://doi.org/10.1148/ryai.250145 #AbdRad #AI #ML
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Deep learning for the detection, diagnosis, and segmentation of gastric neoplasms on contrast-enhanced CT https://doi.org/10.1148/ryai.250145 #cancer #AbdRad #ML
MASLD is rising, and radiologists are key to early detection. A new RadioGraphics review covers guidelines, imaging, and strategies to improve outcomes.
Read the full article: https://bit.ly/4q7WHGF
#AbdRad
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Gastric Neoplasm Detection with AI (GANDA) can detect and diagnose gastric neoplasm on contrast-enhanced CT https://doi.org/10.1148/ryai.250145 #AbdRad #AI #MachineLearning
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Characterizing the Impact of Training Data on Generalizability: Application in Deep Learning to Estimate Lung Nodule Malignancy Risk https://doi.org/10.1148/ryai.240636 #SABI2025 #AbdRad #radiology
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Registry-linked 5-year breast cancer incidence in a large screening mammography cohort with standalone AI and radiologist readers https://doi.org/10.1148/ryai.240804 #SABI2025 #AbdRad #radiology
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How Much Training Data Does AI Need? https://doi.org/10.1148/ryai.250685 #SABI2025 #AbdRad #radiology
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#DeepLearning model to segment lumbar paraspinal muscles & quantify muscle volumes on MRI https://doi.org/10.1148/ryai.240833 @griffith.edu.au #SABI2025 #AbdRad #radiology
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Dentate nucleus segmentation tool using #DeepLearning applied to brain MRI-based quantitative susceptibility mapping images https://doi.org/10.1148/ryai.240478 #SABI2025 #AbdRad #radiology
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MR-Transformer: A Vision Transformer-based Deep Learning Model for Total Knee Replacement Prediction Using MRI https://doi.org/10.1148/ryai.240373 @cem.bsky.social @cai2r.net #SABI2025 #AbdRad #radiology
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Technical feasibility demonstration shows homomorphic encryption can achieve comparable diagnostic accuracy to nonencrypted models https://doi.org/10.1148/ryai.250682 #SABI2025 #AbdRad #radiology
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A #DeepLearning pharmacokinetic parametric mapping method for early pCR prediction using multiphasic DCE-MRI https://doi.org/10.1148/ryai.240769 #SABI2025 #AbdRad #radiology
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Single Inspiratory Chest CT-based Generative Deep Learning Models to Evaluate Functional Small Airway Disease https://doi.org/10.1148/ryai.240680 #SABI2025 #AbdRad #radiology
