RSNA 2005

Nov 30, 2005

The snow enters Chicago just as RSNA 2005 exits.

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Anatomical illustration of knee pain showing muscle and bone structure with red and blue overlay indicating inflammation and blood flow
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MRI model reveals how 3D knee shape influences ACL loading
By Amerigo Allegretto
Researchers created digital twins from patients' knees, and women had greater ACL force than men.
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Meet the Minnies 2026 finalists
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Who made it to the final round of the 2026 edition of the Minnies, AuntMinnie.com's annual event recognizing excellence in radiology? See who our expert panelists selected as the final candidates.
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Gaps identified in surveillance of incidental lung nodules
By Will Morton
Incidental lung nodules (ILNs) are frequently detected during routine imaging.
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X-ray visualization of human lungs with red highlighted inflammation or abnormality in right lung
SIIM-CAIMI: Tech just one driver of AI's future in radiology
By Amerigo Allegretto
Nabile Safdar, MD, spoke on how regulatory, financial, and cultural change will drive AI's future in medical imaging.
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The findings support a complementary role for F-18 fluciclovine PET in carefully selected patients.
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The findings have implications for policy and workforce development.
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MRI, PET reveal distinct brain changes in loneliness and social isolation
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Prior studies frequently have used the terms interchangeably and applied measures designed for one construct when investigating the other.
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Podcast: Hospital accreditation isn’t a safety net for MRI
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The Invisible Force podcast series co-hosts Tobias "Toby" Gilk and John Posh continue their investigation into a 2023 MRI accident.
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SIIM-CAIMI program chairs talk medical imaging AI's evolution
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SIIM-CAIMI's program chairs share their thoughts on the increasing use of AI tools such as large language models in practice.
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LDCT predicts vertebral fracture risk in heavy smokers
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Participants were heavy smokers aged 55 to 74 years old with at least 30 pack-years of smoking history.
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ChatGPT-5 misses half of distal radius fractures on x-rays
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ChatGPT-5 missed 83 fractures, demonstrating a sensitivity of 51.2% and a specificity of 70.5%, with overall accuracy of 61.6%.
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Four wrist X-ray images showing fracture detection examples: true positive with distal radius fracture, true negative normal wrist, false positive misidentified fracture, and false negative missed fracture.
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